diff --git a/03_motif.ipynb b/03_motif.ipynb index da7c2cdd..0636ad28 100644 --- a/03_motif.ipynb +++ b/03_motif.ipynb @@ -154,171 +154,171 @@ "\n", "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", "\n", "\n", - "\n", - "\n", + "\n", + "\n", "β 4\n", "\n", - "\n", - "\n", + "\n", + "\n", "β 4\n", "\n", - "\n", - "\n", + "\n", + "\n", "α 3\n", "\n", - "\n", - "\n", + "\n", + "\n", "β 2\n", "\n", - "\n", - "\n", + "\n", + "\n", "β 4\n", "\n", - "\n", - "\n", + "\n", + "\n", "α 3\n", "\n", - "\n", - "\n", + "\n", + "\n", "β 2\n", "\n", - "\n", - "\n", + "\n", + "\n", "β 4\n", "\n", - "\n", - "\n", + "\n", + "\n", "α 6\n", "\n", - "\n", - "\n", + "\n", + "\n", "α 3\n", "\n", - "\n", - "\n", + "\n", + "\n", "β 4\n", "\n", - "\n", - "\n", + "\n", + "\n", "α 6\n", "\n", - "\n", - "\n", + "\n", + "\n", "α 6\n", "\n", "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", "\n", "" ], @@ -588,93 +588,93 @@ "data": { "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
 motifpvalcorr_pvaleffect_sizemotifpvalcorr_pvaleffect_size
4GlcNAc0.0381200.2058491.5309054GlcNAc0.0381200.2058491.530905
8Man0.0543560.2349901.3902538Man0.0543560.2349901.390253
25Man(a1-?)Man0.0609230.2349901.30833325Man(a1-?)Man0.0609230.2349901.308333
10Man(a1-3)Man0.0342120.2058491.19658610Man(a1-3)Man0.0342120.2058491.196586
11Man(a1-6)Man0.0195430.1758851.16881511Man(a1-6)Man0.0195430.1758851.168815
13Man(b1-4)GlcNAc0.0195430.1758851.16881513Man(b1-4)GlcNAc0.0195430.1758851.168815
14GlcNAc(b1-4)GlcNAc0.0195430.1758851.16881514GlcNAc(b1-4)GlcNAc0.0195430.1758851.168815
7Kdo0.3287900.479672-0.8116797Kdo0.3287900.479672-0.811679
2Glc0.6441800.668956-0.8116792Glc0.6441800.668956-0.811679
16Man(a1-2)Man0.1774610.4796720.77232016Man(a1-2)Man0.1774610.4796720.772320
\n" @@ -706,14 +706,13 @@ "\n", "### get_representative_substructures\n", "\n", - "> get_representative_substructures (enrichment_df, libr=None)\n", + "> get_representative_substructures (enrichment_df)\n", "\n", "builds minimal glycans that contain enriched motifs from get_pvals_motifs\n", "\n", "| Arguments:\n", "| :-\n", "| enrichment_df (dataframe): output from get_pvals_motifs\n", - "| libr (dict): dictionary of form glycoletter:index\n", "\n", "| Returns:\n", "| :-\n", @@ -724,14 +723,13 @@ "\n", "### get_representative_substructures\n", "\n", - "> get_representative_substructures (enrichment_df, libr=None)\n", + "> get_representative_substructures (enrichment_df)\n", "\n", "builds minimal glycans that contain enriched motifs from get_pvals_motifs\n", "\n", "| Arguments:\n", "| :-\n", "| enrichment_df (dataframe): output from get_pvals_motifs\n", - "| libr (dict): dictionary of form glycoletter:index\n", "\n", "| Returns:\n", "| :-\n", @@ -1173,145 +1171,145 @@ " \n", " 5\n", " GlcNAc\n", - " 9.587714\n", - " 1.825232\n", - " 2.466213e-07\n", - " 0.000003\n", + " 9.609197\n", + " 1.829371\n", + " 1.858470e-07\n", + " 0.000002\n", " True\n", - " 0.970701\n", - " 78.579681\n", + " 0.978984\n", + " 77.186979\n", " \n", " \n", " 1\n", " GlcNAc(b1-4)GlcNAc\n", - " 4.793857\n", - " 1.825232\n", - " 1.397194e-05\n", - " 0.000091\n", + " 4.804599\n", + " 1.829371\n", + " 3.461150e-05\n", + " 0.000225\n", " True\n", - " 0.970701\n", - " 27.358139\n", + " 0.978984\n", + " 29.217568\n", " \n", " \n", - " 3\n", - " Man(a1-3)Man\n", - " 6.144803\n", - " 1.574753\n", - " 2.898804e-04\n", - " 0.001256\n", + " 0\n", + " core_fucose(a1-3)\n", + " 1.743212\n", + " 2.041870\n", + " 3.998650e-04\n", + " 0.001595\n", " True\n", - " 0.970701\n", - " 20.488011\n", + " 0.978984\n", + " 9.357325\n", " \n", " \n", - " 0\n", - " core_fucose(a1-3)\n", - " 1.739676\n", - " 2.038228\n", - " 4.170061e-04\n", - " 0.001355\n", + " 3\n", + " Man(a1-3)Man\n", + " 6.157953\n", + " 1.578871\n", + " 4.907424e-04\n", + " 0.001595\n", " True\n", - " 0.970701\n", - " 8.921681\n", + " 0.978984\n", + " 21.168752\n", " \n", " \n", " 9\n", " Man\n", - " 20.137643\n", - " 1.653075\n", - " 6.775881e-04\n", - " 0.001762\n", + " 20.181892\n", + " 1.657247\n", + " 7.534599e-04\n", + " 0.001959\n", + " True\n", + " 0.978984\n", + " 12.201594\n", + " \n", + " \n", + " 11\n", + " Glc(b1-3)Glc\n", + " 7.719058\n", + " -4.545660\n", + " 1.219698e-03\n", + " 0.002643\n", " True\n", - " 0.970701\n", - " 12.114112\n", + " 0.978984\n", + " -8.313695\n", " \n", " \n", " 8\n", " betaGlucan\n", - " 3.882789\n", - " -4.348075\n", - " 1.388199e-03\n", - " 0.003008\n", + " 3.859529\n", + " -4.545660\n", + " 1.679563e-03\n", + " 0.003119\n", " True\n", - " 0.970701\n", - " -7.293732\n", + " 0.978984\n", + " -7.697592\n", " \n", " \n", " 7\n", " Man(a1-?)Man\n", - " 15.343787\n", - " 1.601436\n", - " 2.066612e-03\n", - " 0.003807\n", - " True\n", - " 0.970701\n", - " 11.849161\n", - " \n", - " \n", - " 11\n", - " Glc(b1-3)Glc\n", - " 7.765577\n", - " -4.348075\n", - " 2.342845e-03\n", - " 0.003807\n", + " 15.377293\n", + " 1.605619\n", + " 2.055855e-03\n", + " 0.003341\n", " True\n", - " 0.970701\n", - " -7.492273\n", + " 0.978984\n", + " 11.845911\n", " \n", " \n", " 10\n", " Kdo\n", - " 7.275331\n", - " -2.944934\n", - " 2.996892e-03\n", - " 0.004329\n", + " 7.276969\n", + " -2.942110\n", + " 2.724400e-03\n", + " 0.003935\n", " True\n", - " 0.970701\n", - " -5.256891\n", + " 0.978984\n", + " -5.409346\n", " \n", " \n", " 6\n", " Kdo(a2-?)Kdo\n", - " 4.850221\n", - " -2.944934\n", - " 4.896407e-03\n", - " 0.006365\n", + " 4.851313\n", + " -2.942110\n", + " 4.181116e-03\n", + " 0.004941\n", " True\n", - " 0.970701\n", - " -4.641914\n", + " 0.978984\n", + " -4.803346\n", " \n", " \n", " 12\n", " Glc\n", - " 11.648366\n", - " -4.348075\n", - " 6.485473e-03\n", - " 0.007665\n", + " 11.578587\n", + " -4.545660\n", + " 4.117824e-03\n", + " 0.004941\n", " True\n", - " 0.970701\n", - " -6.989013\n", + " 0.978984\n", + " -7.816323\n", " \n", " \n", " 2\n", " Man(a1-2)Man\n", - " 4.405127\n", - " 1.411156\n", - " 7.122072e-03\n", - " 0.007716\n", + " 4.414742\n", + " 1.415482\n", + " 7.313003e-03\n", + " 0.007922\n", " True\n", - " 0.970701\n", - " 8.140751\n", + " 0.978984\n", + " 8.205204\n", " \n", " \n", " 4\n", " GalNAc(a1-4)GlcNAcA\n", - " 2.425110\n", - " -2.944934\n", - " 2.122188e-02\n", - " 0.021222\n", + " 2.425656\n", + " -2.942110\n", + " 1.686497e-02\n", + " 0.016865\n", " True\n", - " 0.970701\n", - " -3.177455\n", + " 0.978984\n", + " -3.320831\n", " \n", " \n", "\n", @@ -1319,34 +1317,34 @@ ], "text/plain": [ " Glycan Mean abundance Log2FC p-val corr p-val \\\n", - "5 GlcNAc 9.587714 1.825232 2.466213e-07 0.000003 \n", - "1 GlcNAc(b1-4)GlcNAc 4.793857 1.825232 1.397194e-05 0.000091 \n", - "3 Man(a1-3)Man 6.144803 1.574753 2.898804e-04 0.001256 \n", - "0 core_fucose(a1-3) 1.739676 2.038228 4.170061e-04 0.001355 \n", - "9 Man 20.137643 1.653075 6.775881e-04 0.001762 \n", - "8 betaGlucan 3.882789 -4.348075 1.388199e-03 0.003008 \n", - "7 Man(a1-?)Man 15.343787 1.601436 2.066612e-03 0.003807 \n", - "11 Glc(b1-3)Glc 7.765577 -4.348075 2.342845e-03 0.003807 \n", - "10 Kdo 7.275331 -2.944934 2.996892e-03 0.004329 \n", - "6 Kdo(a2-?)Kdo 4.850221 -2.944934 4.896407e-03 0.006365 \n", - "12 Glc 11.648366 -4.348075 6.485473e-03 0.007665 \n", - "2 Man(a1-2)Man 4.405127 1.411156 7.122072e-03 0.007716 \n", - "4 GalNAc(a1-4)GlcNAcA 2.425110 -2.944934 2.122188e-02 0.021222 \n", + "5 GlcNAc 9.609197 1.829371 1.858470e-07 0.000002 \n", + "1 GlcNAc(b1-4)GlcNAc 4.804599 1.829371 3.461150e-05 0.000225 \n", + "0 core_fucose(a1-3) 1.743212 2.041870 3.998650e-04 0.001595 \n", + "3 Man(a1-3)Man 6.157953 1.578871 4.907424e-04 0.001595 \n", + "9 Man 20.181892 1.657247 7.534599e-04 0.001959 \n", + "11 Glc(b1-3)Glc 7.719058 -4.545660 1.219698e-03 0.002643 \n", + "8 betaGlucan 3.859529 -4.545660 1.679563e-03 0.003119 \n", + "7 Man(a1-?)Man 15.377293 1.605619 2.055855e-03 0.003341 \n", + "10 Kdo 7.276969 -2.942110 2.724400e-03 0.003935 \n", + "6 Kdo(a2-?)Kdo 4.851313 -2.942110 4.181116e-03 0.004941 \n", + "12 Glc 11.578587 -4.545660 4.117824e-03 0.004941 \n", + "2 Man(a1-2)Man 4.414742 1.415482 7.313003e-03 0.007922 \n", + "4 GalNAc(a1-4)GlcNAcA 2.425656 -2.942110 1.686497e-02 0.016865 \n", "\n", " significant corr Levene p-val Effect size \n", - "5 True 0.970701 78.579681 \n", - "1 True 0.970701 27.358139 \n", - "3 True 0.970701 20.488011 \n", - "0 True 0.970701 8.921681 \n", - "9 True 0.970701 12.114112 \n", - "8 True 0.970701 -7.293732 \n", - "7 True 0.970701 11.849161 \n", - "11 True 0.970701 -7.492273 \n", - "10 True 0.970701 -5.256891 \n", - "6 True 0.970701 -4.641914 \n", - "12 True 0.970701 -6.989013 \n", - "2 True 0.970701 8.140751 \n", - "4 True 0.970701 -3.177455 " + "5 True 0.978984 77.186979 \n", + "1 True 0.978984 29.217568 \n", + "0 True 0.978984 9.357325 \n", + "3 True 0.978984 21.168752 \n", + "9 True 0.978984 12.201594 \n", + "11 True 0.978984 -8.313695 \n", + "8 True 0.978984 -7.697592 \n", + "7 True 0.978984 11.845911 \n", + "10 True 0.978984 -5.409346 \n", + "6 True 0.978984 -4.803346 \n", + "12 True 0.978984 -7.816323 \n", + "2 True 0.978984 8.205204 \n", + "4 True 0.978984 -3.320831 " ] }, "execution_count": null, @@ -1444,7 +1442,7 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -1691,7 +1689,7 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -1770,7 +1768,7 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -1897,92 +1895,92 @@ " \n", " 10\n", " GlcNAc\n", - " 732.620588\n", - " 8.817500e-07\n", + " 743.390670\n", + " 8.441284e-07\n", " True\n", " \n", " \n", " 8\n", " GlcNAc(b1-4)GlcNAc\n", - " 456.043612\n", - " 1.814326e-06\n", + " 496.837365\n", + " 1.405371e-06\n", " True\n", " \n", " \n", " 9\n", " Man(a1-3)Man\n", - " 282.312291\n", - " 5.037609e-06\n", + " 298.819794\n", + " 4.255423e-06\n", " True\n", " \n", " \n", " 11\n", " Man(a1-?)Man\n", - " 124.165211\n", - " 4.061198e-05\n", + " 123.539236\n", + " 4.102418e-05\n", " True\n", " \n", " \n", " 12\n", " Man\n", - " 117.012818\n", - " 4.061198e-05\n", + " 116.609521\n", + " 4.102418e-05\n", " True\n", " \n", " \n", " 0\n", " betaGlucan\n", - " 76.852532\n", - " 8.783919e-05\n", + " 84.461057\n", + " 8.044371e-05\n", " True\n", " \n", " \n", " 6\n", " core_fucose(a1-3)\n", - " 76.736929\n", - " 8.783919e-05\n", + " 82.422509\n", + " 8.044371e-05\n", " True\n", " \n", " \n", - " 7\n", - " Man(a1-2)Man\n", - " 76.343181\n", - " 8.783919e-05\n", + " 1\n", + " Glc(b1-3)Glc\n", + " 74.886889\n", + " 8.254134e-05\n", " True\n", " \n", " \n", - " 1\n", - " Glc(b1-3)Glc\n", - " 65.460306\n", - " 1.215481e-04\n", + " 7\n", + " Man(a1-2)Man\n", + " 77.713969\n", + " 8.254134e-05\n", " True\n", " \n", " \n", " 2\n", " Glc\n", - " 54.581410\n", - " 1.838486e-04\n", + " 62.362338\n", + " 1.256968e-04\n", " True\n", " \n", " \n", " 5\n", " Kdo\n", - " 32.240122\n", - " 7.291254e-04\n", + " 33.362614\n", + " 6.636653e-04\n", " True\n", " \n", " \n", " 4\n", " Kdo(a2-?)Kdo\n", - " 27.068946\n", - " 1.075898e-03\n", + " 28.146337\n", + " 9.680660e-04\n", " True\n", " \n", " \n", " 3\n", " GalNAc(a1-4)GlcNAcA\n", - " 18.195129\n", - " 2.835669e-03\n", + " 19.101383\n", + " 2.500952e-03\n", " True\n", " \n", " \n", @@ -1991,19 +1989,19 @@ ], "text/plain": [ " Glycan F statistic corr p-val significant\n", - "10 GlcNAc 732.620588 8.817500e-07 True\n", - "8 GlcNAc(b1-4)GlcNAc 456.043612 1.814326e-06 True\n", - "9 Man(a1-3)Man 282.312291 5.037609e-06 True\n", - "11 Man(a1-?)Man 124.165211 4.061198e-05 True\n", - "12 Man 117.012818 4.061198e-05 True\n", - "0 betaGlucan 76.852532 8.783919e-05 True\n", - "6 core_fucose(a1-3) 76.736929 8.783919e-05 True\n", - "7 Man(a1-2)Man 76.343181 8.783919e-05 True\n", - "1 Glc(b1-3)Glc 65.460306 1.215481e-04 True\n", - "2 Glc 54.581410 1.838486e-04 True\n", - "5 Kdo 32.240122 7.291254e-04 True\n", - "4 Kdo(a2-?)Kdo 27.068946 1.075898e-03 True\n", - "3 GalNAc(a1-4)GlcNAcA 18.195129 2.835669e-03 True" + "10 GlcNAc 743.390670 8.441284e-07 True\n", + "8 GlcNAc(b1-4)GlcNAc 496.837365 1.405371e-06 True\n", + "9 Man(a1-3)Man 298.819794 4.255423e-06 True\n", + "11 Man(a1-?)Man 123.539236 4.102418e-05 True\n", + "12 Man 116.609521 4.102418e-05 True\n", + "0 betaGlucan 84.461057 8.044371e-05 True\n", + "6 core_fucose(a1-3) 82.422509 8.044371e-05 True\n", + "1 Glc(b1-3)Glc 74.886889 8.254134e-05 True\n", + "7 Man(a1-2)Man 77.713969 8.254134e-05 True\n", + "2 Glc 62.362338 1.256968e-04 True\n", + "5 Kdo 33.362614 6.636653e-04 True\n", + "4 Kdo(a2-?)Kdo 28.146337 9.680660e-04 True\n", + "3 GalNAc(a1-4)GlcNAcA 19.101383 2.500952e-03 True" ] }, "execution_count": null, @@ -2499,7 +2497,7 @@ " \n", " \n", " \n", - " 3\n", + " 1\n", " Neu5Ac(a2-3)\n", " 0.034722\n", " 0.013889\n", @@ -2519,7 +2517,7 @@ " True\n", " \n", " \n", - " 1\n", + " 0\n", " Neu5Ac(a2-6)\n", " 0.073413\n", " 0.044048\n", @@ -2529,7 +2527,7 @@ " True\n", " \n", " \n", - " 0\n", + " 2\n", " Gal(b1-3)\n", " 0.894345\n", " 0.715476\n", @@ -2539,7 +2537,7 @@ " False\n", " \n", " \n", - " 2\n", + " 3\n", " Fuc(a1-2)\n", " 1.000000\n", " 1.000000\n", @@ -2554,18 +2552,18 @@ ], "text/plain": [ " Molecule_Name BH_Q_Value Adjusted_P_value Period_Length Lag_Phase \\\n", - "3 Neu5Ac(a2-3) 0.034722 0.013889 24.0 16.5 \n", + "1 Neu5Ac(a2-3) 0.034722 0.013889 24.0 16.5 \n", "4 Neu5Ac(a2-?) 0.034722 0.013889 0.0 0.0 \n", - "1 Neu5Ac(a2-6) 0.073413 0.044048 24.0 13.5 \n", - "0 Gal(b1-3) 0.894345 0.715476 24.0 16.5 \n", - "2 Fuc(a1-2) 1.000000 1.000000 24.0 0.0 \n", + "0 Neu5Ac(a2-6) 0.073413 0.044048 24.0 13.5 \n", + "2 Gal(b1-3) 0.894345 0.715476 24.0 16.5 \n", + "3 Fuc(a1-2) 1.000000 1.000000 24.0 0.0 \n", "\n", " Amplitude significant \n", - "3 0.357084 True \n", + "1 0.357084 True \n", "4 0.000000 True \n", - "1 0.101473 True \n", - "0 0.216896 False \n", - "2 0.546986 False " + "0 0.101473 True \n", + "2 0.216896 False \n", + "3 0.546986 False " ] }, "execution_count": null, @@ -2599,8 +2597,7 @@ "\n", "### annotate_glycan\n", "\n", - "> annotate_glycan (glycan, motifs=None, libr=None, termini_list=[],\n", - "> gmotifs=None)\n", + "> annotate_glycan (glycan, motifs=None, termini_list=[], gmotifs=None)\n", "\n", "searches for known motifs in glycan sequence\n", "\n", @@ -2608,7 +2605,6 @@ "| :-\n", "| glycan (string or networkx): glycan in IUPAC-condensed format (or as networkx graph) that has to contain a floating substituent\n", "| motifs (dataframe): dataframe of glycan motifs (name + sequence), can be used with a list of glycans too; default:motif_list\n", - "| libr (dict): dictionary of form glycoletter:index\n", "| termini_list (list): list of monosaccharide positions (from 'terminal', 'internal', and 'flexible')\n", "| gmotifs (networkx): precalculated motif graphs for speed-up; default:None\n", "\n", @@ -2621,8 +2617,7 @@ "\n", "### annotate_glycan\n", "\n", - "> annotate_glycan (glycan, motifs=None, libr=None, termini_list=[],\n", - "> gmotifs=None)\n", + "> annotate_glycan (glycan, motifs=None, termini_list=[], gmotifs=None)\n", "\n", "searches for known motifs in glycan sequence\n", "\n", @@ -2630,7 +2625,6 @@ "| :-\n", "| glycan (string or networkx): glycan in IUPAC-condensed format (or as networkx graph) that has to contain a floating substituent\n", "| motifs (dataframe): dataframe of glycan motifs (name + sequence), can be used with a list of glycans too; default:motif_list\n", - "| libr (dict): dictionary of form glycoletter:index\n", "| termini_list (list): list of monosaccharide positions (from 'terminal', 'internal', and 'flexible')\n", "| gmotifs (networkx): precalculated motif graphs for speed-up; default:None\n", "\n", @@ -2882,652 +2876,652 @@ "data": { "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
motif_nameTerminal_LewisXInternal_LewisXLewisYSialylLewisXSulfoSialylLewisXTerminal_LewisAInternal_LewisALewisBSialylLewisASulfoLewisAH_type2H_type1A_antigenB_antigenGalili_antigenGloboHGb5Gb4Gb33SGb38DSGb33SGb48DSGb46DSGb43SGb58DSGb56DSGb56DSGb5_26SGb38DSGb3_26SGb48DSGb4_26SGb58DSGb5_266DSGb5Forssman_antigeniGb3I_antigeni_antigenPI_antigenChitobioseTrimannosylcoreInternal_LacNAc_type1Terminal_LacNAc_type1Internal_LacNAc_type2Terminal_LacNAc_type2Internal_LacdiNAc_type1Terminal_LacdiNAc_type1Internal_LacdiNAc_type2Terminal_LacdiNAc_type2bisectingGlcNAcVIMPolyLacNAcGanglio_SeriesLacto_Series(LewisC)NeoLacto_SeriesbetaGlucanKeratanSulfateHyluronanMollu_seriesArthro_seriesCellulose_likeChondroitin_4SGPI_anchorIsoglobo_seriesLewisDGlobo_seriesSdaSDAMuco_seriesHeparinPeptidoglycanDermatansulfateCADLactosylceramideLactotriaosylceramideLexLexGM3H_type3GM2GM1cisGM1VIM2GD3GD1aGD2GD1bSDLexNglycolyl_GM2Fuc_LN3GT1bGD1GD1a_2LcGg4GT3Disialyl_T_antigenGT1aGT2GT1c2Fuc_GM1GQ1cO_linked_mannoseGT1aaGQ1bHNK1GQ1baO_mannose_Lex2Fuc_GD1bSialopentaosylceramideSulfogangliotetraosylceramideB-GM1GQ1aabisSulfo-Lewis xpara-Forssmancore_fucosecore_fucose(a1-3)GP1cB-GD1bGP1caIsoglobotetraosylceramidepolySiahigh_mannoseGala_seriesLPS_coreNglycan_complexNglycan_complex2Oglycan_core1Oglycan_core2Oglycan_core3Oglycan_core4Oglycan_core5Oglycan_core6Oglycan_core7XylogalacturonanSialosylparaglobosideLDNFOFucArabinogalactan_type2EGF_repeat Nglycan_hybridArabinanXyloglucanAcharan_SulfateM3FXM3X1-6betaGalactanArabinogalactan_type1GalactomannanTetraantennary_NglycanMucin_elongated_core2FucoidanAlginateFGXXDifucosylated_coreGalFuc_coreTerminal_LewisXInternal_LewisXLewisYSialylLewisXSulfoSialylLewisXTerminal_LewisAInternal_LewisALewisBSialylLewisASulfoLewisAH_type2H_type1A_antigenB_antigenGalili_antigenGloboHGb5Gb4Gb33SGb38DSGb33SGb48DSGb46DSGb43SGb58DSGb56DSGb56DSGb5_26SGb38DSGb3_26SGb48DSGb4_26SGb58DSGb5_266DSGb5Forssman_antigeniGb3I_antigeni_antigenPI_antigenChitobioseTrimannosylcoreInternal_LacNAc_type1Terminal_LacNAc_type1Internal_LacNAc_type2Terminal_LacNAc_type2Internal_LacdiNAc_type1Terminal_LacdiNAc_type1Internal_LacdiNAc_type2Terminal_LacdiNAc_type2bisectingGlcNAcVIMPolyLacNAcGanglio_SeriesLacto_Series(LewisC)NeoLacto_SeriesbetaGlucanKeratanSulfateHyluronanMollu_seriesArthro_seriesCellulose_likeChondroitin_4SGPI_anchorIsoglobo_seriesLewisDGlobo_seriesSdaSDAMuco_seriesHeparinPeptidoglycanDermatansulfateCADLactosylceramideLactotriaosylceramideLexLexGM3H_type3GM2GM1cisGM1VIM2GD3GD1aGD2GD1bSDLexNglycolyl_GM2Fuc_LN3GT1bGD1GD1a_2LcGg4GT3Disialyl_T_antigenGT1aGT2GT1c2Fuc_GM1GQ1cO_linked_mannoseGT1aaGQ1bHNK1GQ1baO_mannose_Lex2Fuc_GD1bSialopentaosylceramideSulfogangliotetraosylceramideB-GM1GQ1aabisSulfo-Lewis xpara-Forssmancore_fucosecore_fucose(a1-3)GP1cB-GD1bGP1caIsoglobotetraosylceramidepolySiahigh_mannoseGala_seriesLPS_coreNglycan_complexNglycan_complex2Oglycan_core1Oglycan_core2Oglycan_core3Oglycan_core4Oglycan_core5Oglycan_core6Oglycan_core7XylogalacturonanSialosylparaglobosideLDNFOFucArabinogalactan_type2EGF_repeat Nglycan_hybridArabinanXyloglucanAcharan_SulfateM3FXM3X1-6betaGalactanArabinogalactan_type1GalactomannanTetraantennary_NglycanMucin_elongated_core2FucoidanAlginateFGXXDifucosylated_coreGalFuc_core
Man(a1-3)[Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAc000000000000000000000000000000000000000011000000000000000000000000000000000000000000000000000000000000000000000000010000000000000000000000000001000000000000Man(a1-3)[Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAc000000000000000000000000000000000000000011000000000000000000000000000000000000000000000000000000000000000000000000010000000000000000000000000001000000000000
Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-3)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc000000000000000000000000000000000000000011000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-3)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc000000000000000000000000000000000000000011000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
GalNAc(a1-4)GlcNAcA(a1-4)[GlcN(b1-7)]Kdo(a2-5)[Kdo(a2-4)]Kdo(a2-6)GlcN4P(b1-6)GlcN4P000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000GalNAc(a1-4)GlcNAcA(a1-4)[GlcN(b1-7)]Kdo(a2-5)[Kdo(a2-4)]Kdo(a2-6)GlcN4P(b1-6)GlcN4P000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
\n" @@ -3953,8 +3947,7 @@ "\n", "### get_k_saccharides\n", "\n", - "> get_k_saccharides (glycans, size=2, libr=None, up_to=False,\n", - "> just_motifs=False)\n", + "> get_k_saccharides (glycans, size=2, up_to=False, just_motifs=False)\n", "\n", "function to retrieve k-saccharides (default:disaccharides) occurring in a list of glycans\n", "\n", @@ -3962,7 +3955,6 @@ "| :-\n", "| glycans (list): list of glycans in IUPAC-condensed nomenclature\n", "| size (int): number of monosaccharides per -saccharide, default:2 (for disaccharides)\n", - "| libr (dict): dictionary of form glycoletter:index\n", "| up_to (bool): in theory: include -saccharides up to size k; in practice: include monosaccharides; default:False\n", "| just_motifs (bool): if you only want the motifs as a nested list, no dataframe with counts; default:False\n", "\n", @@ -3975,8 +3967,7 @@ "\n", "### get_k_saccharides\n", "\n", - "> get_k_saccharides (glycans, size=2, libr=None, up_to=False,\n", - "> just_motifs=False)\n", + "> get_k_saccharides (glycans, size=2, up_to=False, just_motifs=False)\n", "\n", "function to retrieve k-saccharides (default:disaccharides) occurring in a list of glycans\n", "\n", @@ -3984,7 +3975,6 @@ "| :-\n", "| glycans (list): list of glycans in IUPAC-condensed nomenclature\n", "| size (int): number of monosaccharides per -saccharide, default:2 (for disaccharides)\n", - "| libr (dict): dictionary of form glycoletter:index\n", "| up_to (bool): in theory: include -saccharides up to size k; in practice: include monosaccharides; default:False\n", "| just_motifs (bool): if you only want the motifs as a nested list, no dataframe with counts; default:False\n", "\n", @@ -4025,120 +4015,120 @@ "data": { "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
 Man(a1-3)[Man(a1-6)]ManMan(a1-3)[Xyl(b1-2)]ManMan(a1-3)Man(b1-4)GlcNAcMan(a1-6)[Xyl(b1-2)]ManMan(a1-6)Man(b1-4)GlcNAcXyl(b1-2)Man(b1-4)GlcNAcMan(b1-4)GlcNAc(b1-4)GlcNAcGlcNAc(b1-4)[Fuc(a1-3)]GlcNAcMan(a1-2)Man(a1-2)ManMan(a1-2)Man(a1-3)ManMan(a1-3)Man(a1-6)ManGalNAc(a1-4)GlcNAcA(a1-4)KdoGlcNAcA(a1-4)[GlcN(b1-7)]KdoGlcNAcA(a1-4)Kdo(a2-5)KdoGlcN(b1-7)Kdo(a2-5)Kdo]Kdo(a2-5)[Kdo(a2-4)]KdoKdo(a2-5)Kdo(a2-6)GlcN4PKdo(a2-4)Kdo(a2-6)GlcN4PKdo(a2-6)GlcN4P(b1-6)GlcN4PMan(a1-?)[Xyl(b1-?)]ManMan(a1-?)Man(b1-?)GlcNAcMan(a1-?)Man(a1-?)ManKdo(a2-?)Kdo(a2-?)GlcN4PMan(a1-3)[Man(a1-6)]ManMan(a1-3)[Xyl(b1-2)]ManMan(a1-3)Man(b1-4)GlcNAcMan(a1-6)[Xyl(b1-2)]ManMan(a1-6)Man(b1-4)GlcNAcXyl(b1-2)Man(b1-4)GlcNAcMan(b1-4)GlcNAc(b1-4)GlcNAcGlcNAc(b1-4)[Fuc(a1-3)]GlcNAcMan(a1-2)Man(a1-2)ManMan(a1-2)Man(a1-3)ManMan(a1-3)Man(a1-6)ManGalNAc(a1-4)GlcNAcA(a1-4)KdoGlcNAcA(a1-4)[GlcN(b1-7)]KdoGlcNAcA(a1-4)Kdo(a2-5)KdoGlcN(b1-7)Kdo(a2-5)Kdo]Kdo(a2-5)[Kdo(a2-4)]KdoKdo(a2-5)Kdo(a2-6)GlcN4PKdo(a2-4)Kdo(a2-6)GlcN4PKdo(a2-6)GlcN4P(b1-6)GlcN4PMan(a1-?)[Xyl(b1-?)]ManMan(a1-?)Man(b1-?)GlcNAcMan(a1-?)Man(a1-?)ManKdo(a2-?)Kdo(a2-?)GlcN4P
011111111000000000002200011111111000000000002200
100101010112000000000240100101010112000000000240
200000000000111111110002200000000000111111110002
\n" @@ -4170,14 +4160,13 @@ "\n", "### get_terminal_structures\n", "\n", - "> get_terminal_structures (glycan, libr=None)\n", + "> get_terminal_structures (glycan)\n", "\n", "returns terminal structures from all non-reducing ends (monosaccharide+linkage)\n", "\n", "| Arguments:\n", "| :-\n", "| glycan (string or networkx): glycan in IUPAC-condensed nomenclature or as networkx graph\n", - "| libr (dict): dictionary of form glycoletter:index\n", "\n", "| Returns:\n", "| :-\n", @@ -4188,14 +4177,13 @@ "\n", "### get_terminal_structures\n", "\n", - "> get_terminal_structures (glycan, libr=None)\n", + "> get_terminal_structures (glycan)\n", "\n", "returns terminal structures from all non-reducing ends (monosaccharide+linkage)\n", "\n", "| Arguments:\n", "| :-\n", "| glycan (string or networkx): glycan in IUPAC-condensed nomenclature or as networkx graph\n", - "| libr (dict): dictionary of form glycoletter:index\n", "\n", "| Returns:\n", "| :-\n", @@ -4308,60 +4296,60 @@ "data": { "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
 defined_atom_stereo_countcomplexityh_bond_acceptor_countxlogpdefined_bond_stereo_countexact_massbond_stereo_countundefined_bond_stereo_countmonoisotopic_massh_bond_donor_countundefined_atom_stereo_countheavy_atom_countrotatable_bond_countchargecovalent_unit_countisotope_atom_countmolecular_weightatom_stereo_counttpsamolecular_weighth_bond_donor_countdefined_atom_stereo_countrotatable_bond_counttpsacovalent_unit_countbond_stereo_countatom_stereo_counth_bond_acceptor_countisotope_atom_countexact_massdefined_bond_stereo_countmonoisotopic_masscomplexityundefined_bond_stereo_countundefined_atom_stereo_countxlogpheavy_atom_countcharge
Neu5Ac(a2-3)Gal(b1-4)GlcNAc(b1-2)Man(a1-3)[Neu5Ac(a2-6)Gal(b1-4)GlcNAc(b1-2)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc56441062-23.60000002222.7830048002222.7830048391152430102224.0571070Neu5Ac(a2-3)Gal(b1-4)GlcNAc(b1-2)Man(a1-3)[Neu5Ac(a2-6)Gal(b1-4)GlcNAc(b1-2)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc2224.0395643107010576202222.783004802222.7830048441001-23.6000001520
\n" @@ -4555,7 +4543,7 @@ "\n", "### compare_glycans\n", "\n", - "> compare_glycans (glycan_a, glycan_b, libr=None, wildcards_ptm=False)\n", + "> compare_glycans (glycan_a, glycan_b, wildcards_ptm=False)\n", "\n", "returns True if glycans are the same and False if not\n", "\n", @@ -4563,7 +4551,6 @@ "| :-\n", "| glycan_a (string or networkx object): glycan in IUPAC-condensed format or as a precomputed networkx object\n", "| glycan_b (stringor networkx object): glycan in IUPAC-condensed format or as a precomputed networkx object\n", - "| libr (dict): dictionary of form glycoletter:index\n", "| wildcards_ptm (bool): set to True to allow modification wildcards (e.g., 'OS' matching with '6S'), only works when strings are provided; default:False\n", "\n", "| Returns:\n", @@ -4575,7 +4562,7 @@ "\n", "### compare_glycans\n", "\n", - "> compare_glycans (glycan_a, glycan_b, libr=None, wildcards_ptm=False)\n", + "> compare_glycans (glycan_a, glycan_b, wildcards_ptm=False)\n", "\n", "returns True if glycans are the same and False if not\n", "\n", @@ -4583,7 +4570,6 @@ "| :-\n", "| glycan_a (string or networkx object): glycan in IUPAC-condensed format or as a precomputed networkx object\n", "| glycan_b (stringor networkx object): glycan in IUPAC-condensed format or as a precomputed networkx object\n", - "| libr (dict): dictionary of form glycoletter:index\n", "| wildcards_ptm (bool): set to True to allow modification wildcards (e.g., 'OS' matching with '6S'), only works when strings are provided; default:False\n", "\n", "| Returns:\n", @@ -4634,9 +4620,8 @@ "\n", "### subgraph_isomorphism\n", "\n", - "> subgraph_isomorphism (glycan, motif, libr=None, termini_list=[],\n", - "> count=False, wildcards_ptm=False,\n", - "> return_matches=False)\n", + "> subgraph_isomorphism (glycan, motif, termini_list=[], count=False,\n", + "> wildcards_ptm=False, return_matches=False)\n", "\n", "returns True if motif is in glycan and False if not\n", "\n", @@ -4644,7 +4629,6 @@ "| :-\n", "| glycan (string or networkx): glycan in IUPAC-condensed format or as graph in NetworkX format\n", "| motif (string or networkx): glycan motif in IUPAC-condensed format or as graph in NetworkX format\n", - "| libr (dict): dictionary of form glycoletter:index\n", "| termini_list (list): list of monosaccharide positions (from 'terminal', 'internal', and 'flexible')\n", "| count (bool): whether to return the number or absence/presence of motifs; default:False\n", "| wildcards_ptm (bool): set to True to allow modification wildcards (e.g., 'OS' matching with '6S'), only works when strings are provided; default:False\n", @@ -4659,9 +4643,8 @@ "\n", "### subgraph_isomorphism\n", "\n", - "> subgraph_isomorphism (glycan, motif, libr=None, termini_list=[],\n", - "> count=False, wildcards_ptm=False,\n", - "> return_matches=False)\n", + "> subgraph_isomorphism (glycan, motif, termini_list=[], count=False,\n", + "> wildcards_ptm=False, return_matches=False)\n", "\n", "returns True if motif is in glycan and False if not\n", "\n", @@ -4669,7 +4652,6 @@ "| :-\n", "| glycan (string or networkx): glycan in IUPAC-condensed format or as graph in NetworkX format\n", "| motif (string or networkx): glycan motif in IUPAC-condensed format or as graph in NetworkX format\n", - "| libr (dict): dictionary of form glycoletter:index\n", "| termini_list (list): list of monosaccharide positions (from 'terminal', 'internal', and 'flexible')\n", "| count (bool): whether to return the number or absence/presence of motifs; default:False\n", "| wildcards_ptm (bool): set to True to allow modification wildcards (e.g., 'OS' matching with '6S'), only works when strings are provided; default:False\n", @@ -4723,8 +4705,7 @@ "\n", "### generate_graph_features\n", "\n", - "> generate_graph_features (glycan, glycan_graph=True, libr=None,\n", - "> label='network')\n", + "> generate_graph_features (glycan, glycan_graph=True, label='network')\n", "\n", "compute graph features of glycan\n", "\n", @@ -4732,7 +4713,6 @@ "| :-\n", "| glycan (string or networkx object): glycan in IUPAC-condensed format (or glycan network if glycan_graph=False)\n", "| glycan_graph (bool): True expects a glycan, False expects a network (from construct_network); default:True\n", - "| libr (dict): dictionary of form glycoletter:index\n", "| label (string): Label to place in output dataframe if glycan_graph=False; default:'network'\n", "\n", "| Returns:\n", @@ -4744,8 +4724,7 @@ "\n", "### generate_graph_features\n", "\n", - "> generate_graph_features (glycan, glycan_graph=True, libr=None,\n", - "> label='network')\n", + "> generate_graph_features (glycan, glycan_graph=True, label='network')\n", "\n", "compute graph features of glycan\n", "\n", @@ -4753,7 +4732,6 @@ "| :-\n", "| glycan (string or networkx object): glycan in IUPAC-condensed format (or glycan network if glycan_graph=False)\n", "| glycan_graph (bool): True expects a glycan, False expects a network (from construct_network); default:True\n", - "| libr (dict): dictionary of form glycoletter:index\n", "| label (string): Label to place in output dataframe if glycan_graph=False; default:'network'\n", "\n", "| Returns:\n", @@ -4915,7 +4893,7 @@ "\n", "### largest_subgraph\n", "\n", - "> largest_subgraph (glycan_a, glycan_b, libr=None)\n", + "> largest_subgraph (glycan_a, glycan_b)\n", "\n", "find the largest common subgraph of two glycans\n", "\n", @@ -4923,7 +4901,6 @@ "| :-\n", "| glycan_a (string or networkx): glycan in IUPAC-condensed format or as networkx graph\n", "| glycan_b (string or networkx): glycan in IUPAC-condensed format or as networkx graph\n", - "| libr (dict): dictionary of form glycoletter:index\n", "\n", "| Returns:\n", "| :-\n", @@ -4934,7 +4911,7 @@ "\n", "### largest_subgraph\n", "\n", - "> largest_subgraph (glycan_a, glycan_b, libr=None)\n", + "> largest_subgraph (glycan_a, glycan_b)\n", "\n", "find the largest common subgraph of two glycans\n", "\n", @@ -4942,7 +4919,6 @@ "| :-\n", "| glycan_a (string or networkx): glycan in IUPAC-condensed format or as networkx graph\n", "| glycan_b (string or networkx): glycan in IUPAC-condensed format or as networkx graph\n", - "| libr (dict): dictionary of form glycoletter:index\n", "\n", "| Returns:\n", "| :-\n", @@ -4994,14 +4970,13 @@ "\n", "### ensure_graph\n", "\n", - "> ensure_graph (glycan, libr=None, **kwargs)\n", + "> ensure_graph (glycan, **kwargs)\n", "\n", "ensures function compatibility with string glycans and graph glycans\n", "\n", "| Arguments:\n", "| :-\n", "| glycan (string or networkx graph): glycan in IUPAC-condensed format or as a networkx graph\n", - "| libr (dict): dictionary of form glycoletter:index\n", "| **kwargs: keyword arguments that are directly passed on to glycan_to_nxGraph\n", "\n", "| Returns:\n", @@ -5013,14 +4988,13 @@ "\n", "### ensure_graph\n", "\n", - "> ensure_graph (glycan, libr=None, **kwargs)\n", + "> ensure_graph (glycan, **kwargs)\n", "\n", "ensures function compatibility with string glycans and graph glycans\n", "\n", "| Arguments:\n", "| :-\n", "| glycan (string or networkx graph): glycan in IUPAC-condensed format or as a networkx graph\n", - "| libr (dict): dictionary of form glycoletter:index\n", "| **kwargs: keyword arguments that are directly passed on to glycan_to_nxGraph\n", "\n", "| Returns:\n", @@ -5071,14 +5045,13 @@ "\n", "### get_possible_topologies\n", "\n", - "> get_possible_topologies (glycan, libr=None, exhaustive=False)\n", + "> get_possible_topologies (glycan, exhaustive=False)\n", "\n", "creates possible glycans given a floating substituent; only works with max one floating substituent\n", "\n", "| Arguments:\n", "| :-\n", "| glycan (string or networkx): glycan in IUPAC-condensed format or as networkx graph\n", - "| libr (dict): dictionary of form glycoletter:index\n", "| exhaustive (bool): whether to also allow additions at internal positions; default:False\n", "\n", "| Returns:\n", @@ -5090,14 +5063,13 @@ "\n", "### get_possible_topologies\n", "\n", - "> get_possible_topologies (glycan, libr=None, exhaustive=False)\n", + "> get_possible_topologies (glycan, exhaustive=False)\n", "\n", "creates possible glycans given a floating substituent; only works with max one floating substituent\n", "\n", "| Arguments:\n", "| :-\n", "| glycan (string or networkx): glycan in IUPAC-condensed format or as networkx graph\n", - "| libr (dict): dictionary of form glycoletter:index\n", "| exhaustive (bool): whether to also allow additions at internal positions; default:False\n", "\n", "| Returns:\n", @@ -5127,8 +5099,7 @@ "\n", "### possible_topology_check\n", "\n", - "> possible_topology_check (glycan, glycans, libr=None, exhaustive=False,\n", - "> **kwargs)\n", + "> possible_topology_check (glycan, glycans, exhaustive=False, **kwargs)\n", "\n", "checks whether glycan with floating substituent could match glycans from a list; only works with max one floating substituent\n", "\n", @@ -5136,7 +5107,6 @@ "| :-\n", "| glycan (string or networkx): glycan in IUPAC-condensed format (or as networkx graph) that has to contain a floating substituent\n", "| glycans (list): list of glycans in IUPAC-condensed format (or networkx graphs; should not contain floating substituents)\n", - "| libr (dict): dictionary of form glycoletter:index\n", "| exhaustive (bool): whether to also allow additions at internal positions; default:False\n", "| **kwargs: keyword arguments that are directly passed on to compare_glycans\n", "\n", @@ -5149,8 +5119,7 @@ "\n", "### possible_topology_check\n", "\n", - "> possible_topology_check (glycan, glycans, libr=None, exhaustive=False,\n", - "> **kwargs)\n", + "> possible_topology_check (glycan, glycans, exhaustive=False, **kwargs)\n", "\n", "checks whether glycan with floating substituent could match glycans from a list; only works with max one floating substituent\n", "\n", @@ -5158,7 +5127,6 @@ "| :-\n", "| glycan (string or networkx): glycan in IUPAC-condensed format (or as networkx graph) that has to contain a floating substituent\n", "| glycans (list): list of glycans in IUPAC-condensed format (or networkx graphs; should not contain floating substituents)\n", - "| libr (dict): dictionary of form glycoletter:index\n", "| exhaustive (bool): whether to also allow additions at internal positions; default:False\n", "| **kwargs: keyword arguments that are directly passed on to compare_glycans\n", "\n", @@ -5524,460 +5492,460 @@ "data": { "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6428,1788 +6396,1788 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
glycanApif(a1-2)Xyl(b1-2)[Glc6Ac(b1-4)]GlcAra(a1-2)Ara(a1-6)GlcNAcAra(a1-2)Glc(b1-2)AraAra(a1-2)GlcAAra(a1-2)[Glc(b1-6)]GlcAra(a1-6)GlcAraf(a1-3)Araf(a1-5)[Araf(a1-6)Gal(b1-6)Glc(b1-6)Man(a1-3)]Araf(a1-5)Araf(a1-3)Araf(a1-3)ArafAraf(a1-3)Gal(b1-6)GalD-Apif(b1-2)GlcD-Apif(b1-2)GlcAD-Apif(b1-3)Xyl(b1-2)[Glc6Ac(b1-4)]GlcD-Apif(b1-3)Xyl(b1-4)Rha(a1-2)AraD-Apif(b1-3)Xyl(b1-4)Rha(a1-2)D-FucD-Apif(b1-3)Xyl(b1-4)[Glc(b1-3)]Rha(a1-2)D-FucD-Apif(b1-3)[Gal(b1-4)Xyl(b1-4)]Rha(a1-2)D-FucD-Apif(b1-3)[Gal(b1-4)Xyl(b1-4)]Rha(a1-2)[Rha(a1-3)]D-FucD-Apif(b1-3)[Gal(b1-4)Xyl(b1-4)]Rha(a1-3)D-FucD-Apif(b1-6)GlcD-ApifOMe(b1-3)XylOMe(b1-4)RhaOMe(a1-2)D-FucOMeD-ApifOMe(b1-3)XylOMe(b1-4)[GlcOMe(b1-3)]RhaOMe(a1-2)D-FucOMeFruf(a2-1)[Glc(b1-2)][Glc(b1-3)Glc4Ac6Ac(b1-3)]GlcFruf(a2-1)[Glc(b1-2)][Glc(b1-3)Glc4Ac6Ac(b1-3)]Glc6AcFruf(a2-1)[Glc(b1-2)][Glc(b1-3)Glc6Ac(b1-3)]GlcFruf(a2-1)[Glc(b1-2)][Glc(b1-3)Glc6Ac(b1-3)]Glc6AcFruf(b2-1)Glc3Ac6AcFruf(b2-1)Glc4Ac6AcFruf(b2-1)Glc6AcFruf(b2-1)[Glc(b1-2)]GlcFruf(b2-1)[Glc(b1-2)][Glc(b1-3)Glc(b1-3)]GlcFruf(b2-1)[Glc(b1-2)][Glc(b1-3)]Glc6AcFruf(b2-1)[Glc(b1-2)][Glc(b1-4)Glc(b1-3)]GlcFruf(b2-1)[Glc(b1-2)][Glc(b1-4)Glc(b1-3)]Glc6AcFruf(b2-1)[Glc(b1-2)][Glc(b1-4)Glc6Ac(b1-3)]GlcFruf(b2-1)[Glc(b1-2)][Glc(b1-4)Glc6Ac(b1-3)]Glc6AcFruf(b2-1)[Glc(b1-2)][Glc6Ac(b1-3)]GlcFruf(b2-1)[Glc(b1-2)][Glc6Ac(b1-3)]Glc6AcFruf(b2-1)[Glc(b1-4)Glc6Ac(b1-3)]Glc6AcFruf(b2-1)[Glc3Ac(b1-2)]GlcFruf(b2-1)[Glc6Ac(b1-2)]GlcFruf1Ac(b2-1)Glc2Ac4Ac6AcFuc(a1-2)Gal(b1-2)Xyl(a1-6)GlcFuc(a1-2)Gal(b1-2)Xyl(a1-6)Glc(b1-4)GlcFuc(a1-2)Gal(b1-2)Xyl(a1-6)[Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)]Glc(b1-4)GlcFuc(a1-2)Gal(b1-2)Xyl(a1-6)[Glc(b1-4)]Glc(b1-4)GlcFuc(a1-2)Gal(b1-4)XylFuc(a1-4)GlcNAc(b1-2)Man(a1-3)[Gal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcFuc(a1-6)GlcNAc(b1-2)[Man(a1-6)]Man(a1-6)[Xyl(b1-2)][Man(a1-3)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(?1-?)Gal(b1-4)[Fuc(a1-3)]GlcNAc(b1-2)Man(a1-3)[Man(a1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(?1-?)[Gal(?1-?)]GlcNAc(?1-?)[Fuc(a1-3)]GlcNAc(b1-2)Man(a1-3)[Gal(?1-?)Man(a1-3)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(a1-4)GalGal(a1-6)GalGal(a1-6)Gal(a1-6)GalGal(a1-6)Gal(a1-6)Gal(a1-6)Gal(a1-6)Glc(a1-2)FrufGal(a1-6)Gal(a1-6)Gal(a1-6)Gal(a1-6)[Fruf(b2-1)]GlcGal(a1-6)Gal(a1-6)Gal(a1-6)GlcGal(a1-6)Gal(a1-6)Gal(a1-6)Glc(a1-2)FrufGal(a1-6)Gal(a1-6)GlcGal(a1-6)Gal(a1-6)Glc(a1-2)FrufGal(a1-6)Glc(a1-2)FrufGal(a1-6)ManGal(a1-6)Man(b1-4)ManGal(a1-6)Man(b1-4)Man(b1-4)Man(b1-4)ManGal(a1-6)Man(b1-4)Man(b1-4)Man(b1-4)[Gal(a1-6)]Man(b1-4)Man(b1-4)Man(b1-4)[Gal(a1-6)]ManGal(a1-6)Man(b1-4)Man(b1-4)[Gal(a1-6)]ManGal(a1-6)Man(b1-4)[Gal(a1-6)]ManGal(b1-2)GlcAGal(b1-2)GlcA6MeGal(b1-2)Xyl(a1-6)Glc(b1-4)[Fuc(a1-2)Gal(b1-2)Xyl(a1-6)]Glc(b1-4)GlcGal(b1-2)Xyl(a1-6)[Glc(b1-4)]Glc(b1-4)[Fuc(a1-2)Gal(b1-2)Xyl(a1-6)]Glc(b1-4)GlcGal(b1-2)Xyl(a1-6)[Glc(b1-4)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)GlcGal(b1-2)[Xyl(b1-3)]GlcAGal(b1-3)GlcNAc(b1-2)Man(a1-3)[Gal(b1-3)GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)GlcNAc(b1-2)Man(a1-3)[Gal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)GlcNAc(b1-2)Man(a1-3)[GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)GlcNAc(b1-2)Man(a1-3)[Xyl(b1-2)][Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)GlcNAc(b1-2)Man(a1-6)[GlcNAc(b1-2)Man(a1-3)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)GlcNAc(b1-4)Man(a1-3)[Gal(b1-3)GlcNAc(b1-4)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)GlcNAc(b1-4)Man(a1-3)[GlcNAc(b1-4)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)GlcNAc(b1-4)Man(a1-3)[Xyl(b1-2)][Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)GlcNAc(b1-4)Man(a1-6)[GlcNAc(b1-4)Man(a1-3)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)GlcNAc(b1-4)Man(a1-6)[Xyl(b1-2)][Man(a1-3)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-3)[Gal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(a1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-3)[Gal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-3)[GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-3)[Xyl(b1-2)][Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-6)[GlcNAc(b1-2)Man(a1-3)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-6)[Xyl(b1-2)][Man(a1-3)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-?)[Gal(b1-3)GlcNAc(b1-2)Man(a1-?)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-?)[GlcNAc(b1-2)Man(a1-?)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-?)[Man(a1-?)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)[Man(a1-6)]Man(a1-6)[Xyl(b1-2)][Man(a1-3)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-6)]GlcNAc(b1-2)[Man(a1-6)]Man(a1-6)[Xyl(b1-2)][Man(a1-3)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-4)Gal(b1-4)ManGal(b1-4)Gal(b1-4)ManOMeGal(b1-4)GlcAGal(b1-4)GlcNAc(b1-2)[Gal(b1-4)GlcNAc(b1-4)]Man(a1-3)[Gal(b1-4)GlcNAc(b1-2)[Gal(b1-4)GlcNAc(b1-6)]Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcGal(b1-4)Man(b1-4)ManGal(b1-4)Man(b1-4)Man(b1-4)GalGal(b1-4)Xyl(b1-4)Rha(a1-2)D-FucGal(b1-4)Xyl(b1-4)Rha(a1-2)D-Fuc1CoumOMeGal(b1-4)Xyl(b1-4)Rha(a1-2)D-Fuc1FerOMeGal(b1-4)Xyl(b1-4)Rha(a1-2)[Rha(a1-3)]D-FucGal(b1-4)Xyl(b1-4)Rha(a1-2)[Rha(a1-3)]D-Fuc1CoumOMeGal(b1-4)Xyl(b1-4)Rha(a1-2)[Rha(a1-3)]D-FucOMeOSinGal(b1-4)Xyl(b1-4)[D-Apif(b1-3)]Rha(a1-2)D-FucGal(b1-4)Xyl(b1-4)[D-Apif(b1-3)]Rha(a1-2)D-Fuc1CoumOMeGal(b1-4)Xyl(b1-4)[D-Apif(b1-3)]Rha(a1-2)[Rha(a1-3)]D-FucGal(b1-4)Xyl(b1-4)[D-Apif(b1-3)]Rha(a1-2)[Rha(a1-3)]D-Fuc1CoumOMeGal(b1-4)[Fuc(a1-3)]GlcNAc(b1-2)Man(a1-6)[GlcNAc(b1-2)Man(a1-3)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)GlcNAcGalA(a1-2)[Araf(a1-5)Araf(a1-4)]Rha(b1-4)GalAGalA(a1-4)GalA(a1-4)GalA(a1-4)GalA(a1-2)Rha(a1-4)GalA(a1-2)Rha(a1-4)GalA(a1-2)GalAGalA(a1-4)GalA(a1-4)GalA(a1-4)GalA(a1-4)GalA(a1-4)GalA(a1-4)GalA(a1-4)GalA(a1-4)GalA(a1-4)GalA(a1-4)GalA(a1-4)GalAGalOMe(b1-2)[XylOMe(b1-3)]GlcAOMeGalOMe(b1-4)XylOMe(b1-4)RhaOMe(a1-2)D-FucOMeGalOMe(b1-4)XylOMe(b1-4)RhaOMe(a1-2)[RhaOMe(a1-3)]D-FucOMeGalOMe(b1-4)XylOMe(b1-4)[D-ApifOMe(b1-3)]RhaOMe(a1-2)[RhaOMe(a1-3)]D-FucOMeGalf(b1-2)[Galf(b1-4)]ManGlc(a1-2)Glc(a1-3)Glc(a1-3)Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-2)Man(a1-3)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcGlc(a1-2)Glc(a1-3)Glc(a1-3)Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-3)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcGlc(a1-2)Glc(a1-3)Glc(a1-3)Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcGlc(a1-2)Glc(a1-3)Glc(a1-3)Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcNGlc(a1-2)Rha(a1-6)GlcGlc(a1-3)Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-2)Man(a1-3)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcGlc(a1-3)Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-2)Man(a1-3)[Man(a1-2)Man(a1-6)]Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcGlc(a1-3)Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-2)Man(a1-3)[Man(a1-6)]Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcGlc(a1-4)Glc(a1-2)Rha(a1-6)GlcGlc(a1-4)Glc(a1-4)Glc(a1-6)GlcGlc(a1-4)Glc(a1-4)GlcAGlc(a1-4)GlcA(b1-2)GlcAGlc(b1-2)AraGlc(b1-2)Ara(a1-2)GlcAGlc(b1-2)Gal(b1-2)Gal(b1-2)GlcAGlc(b1-2)Gal(b1-2)GlcAGlc(b1-2)Gal(b1-2)GlcA(b1-3)[Glc(b1-3)]AraGlc(b1-2)GlcGlc(b1-2)Glc(a1-2)FrufOBzOCinGlc(b1-2)Glc(b1-2)GlcGlc(b1-2)GlcAGlc(b1-2)[Ara(a1-3)]GlcA6MeGlc(b1-2)[Ara(a1-3)]GlcAOMeGlc(b1-2)[Ara(a1-6)]GlcGlc(b1-2)[Glc(b1-3)]Glc(a1-2)FrufGlc(b1-2)[Glc(b1-3)]Glc1Fer6Ac(a1-2)Fruf1FerOBzGlc(b1-2)[Glc6Ac(b1-3)]Glc1Fer(a1-2)Fruf1FerOBzGlc(b1-2)[Rha(a1-3)]GlcAGlc(b1-2)[Xyl(b1-2)Ara(a1-6)]GlcGlc(b1-2)[Xyl(b1-2)D-Fuc(b1-6)]GlcGlc(b1-3)AraGlc(b1-3)GlcGlc(b1-3)Glc(b1-3)[Glc(b1-2)]Glc(a1-2)FrufGlc(b1-3)Glc6Ac(b1-3)[Glc(b1-2)]Glc(a1-2)FrufGlc(b1-3)Glc6Ac(b1-3)[Glc(b1-2)]Glc1Coum6Ac(a1-2)Fruf1CoumOBzGlc(b1-3)Glc6Ac(b1-3)[Glc(b1-2)]Glc1Fer(a1-2)Fruf1CoumOBzGlc(b1-3)Glc6Ac(b1-3)[Glc(b1-2)]Glc1Fer(a1-2)Fruf1FerOBzGlc(b1-3)Glc6Ac(b1-3)[Glc(b1-2)]Glc1Fer6Ac(a1-2)Fruf1CoumOBzGlc(b1-3)Glc6Ac(b1-3)[Glc(b1-2)]Glc1Fer6Ac(a1-2)Fruf1FerOBzGlc(b1-3)Glc6Ac(b1-3)[Glc(b1-2)][Rha(a1-4)]Glc1Coum6Ac(a1-2)Fruf1CoumOBzGlc(b1-3)Glc6Ac(b1-3)[Glc(b1-2)][Rha(a1-4)]Glc1Fer6Ac(a1-2)Fruf1CoumOBzGlc(b1-3)Rha1Fer(a1-4)Fruf(b2-1)GlcOBzGlc(b1-3)[Araf(a1-4)]Rha(a1-2)GlcGlc(b1-3)[Xyl(b1-4)]Rha(a1-2)D-FucOMeGlc(b1-4)Glc(b1-3)[Glc(b1-2)]Glc(a1-2)FrufGlc(b1-4)Glc(b1-3)[Glc(b1-2)]Glc1Coum6Ac(a1-2)Fruf1FerOBzGlc(b1-4)Glc(b1-3)[Glc(b1-2)]Glc1Fer(a1-2)Fruf1FerOBzGlc(b1-4)Glc(b1-3)[Glc(b1-2)]Glc1Fer6Ac(a1-2)Fruf1CoumOBzGlc(b1-4)Glc(b1-3)[Glc(b1-2)]Glc1Fer6Ac(a1-2)Fruf1FerOBzGlc(b1-4)Glc(b1-4)GlcGlc(b1-4)Glc(b1-4)Glc(b1-4)ManGlc(b1-4)Glc6Ac(b1-3)Glc1Fer6Ac(a1-2)Fruf1FerOBzGlc(b1-4)Glc6Ac(b1-3)[Glc(b1-2)]Glc1Coum6Ac(a1-2)Fruf1FerOBzGlc(b1-4)Glc6Ac(b1-3)[Glc(b1-2)]Glc1Fer(a1-2)Fruf1FerOBzGlc(b1-4)Glc6Ac(b1-3)[Glc(b1-2)]Glc1Fer6Ac(a1-2)Fruf1CoumOBzGlc(b1-4)Glc6Ac(b1-3)[Glc(b1-2)]Glc1Fer6Ac(a1-2)Fruf1FerOBzGlc(b1-4)Man(b1-4)GlcGlc(b1-4)RhaGlc(b1-4)Rha1Fer(a1-4)Fruf(b2-1)GlcOBzGlc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)GlcGlc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)GlcGlc(b1-6)Glc(b1-3)GlcGlc1CerGlc2Ac(b1-4)[D-Apif(b1-3)Xyl(b1-2)]GlcGlc2Ac3Ac4Ac6Ac(b1-3)AraGlc6Ac(b1-2)Glc(a1-2)FrufOBzOCinGlc6Ac(b1-3)Glc6Ac(b1-3)[Glc6Ac(b1-2)]Glc1Fer6Ac(a1-2)Fruf1CoumOAcOBzGlc6Ac(b1-3)Glc6Ac(b1-3)[Glc6Ac(b1-2)][RhaOAc(a1-4)]Glc1Fer6Ac(a1-2)Fruf1CoumOAcOBzGlc6Ac(b1-3)[Glc(b1-2)]Glc1Coum(a1-2)Fruf1CoumOBzGlc6Ac(b1-3)[Glc(b1-2)]Glc1Fer(a1-2)Fruf1CoumOBzGlc6Ac(b1-3)[Glc(b1-2)]Glc1Fer(a1-2)Fruf1FerOBzGlc6Ac(b1-3)[Glc(b1-2)]Glc1Fer6Ac(a1-2)Fruf1FerOBzGlcA(b1-2)GlcGlcA(b1-2)GlcAGlcA(b1-2)GlcA(b1-2)RhaGlcA4Me(a1-2)[Xyl(b1-4)Xyl(b1-4)Xyl(b1-4)Xyl(b1-4)]XylGlcA4Me(a1-2)[Xyl(b1-4)Xyl(b1-4)Xyl(b1-4)]XylGlcA4Me(a1-2)[Xyl(b1-4)]XylGlcNAc(b1-2)Man(a1-3)[Gal(b1-3)GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGlcNAc(b1-2)Man(a1-3)[GlcNAc(b1-2)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGlcNAc(b1-2)Man(a1-3)[GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)GlcNAcGlcNAc(b1-2)Man(a1-3)[GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGlcNAc(b1-2)Man(a1-3)[GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Gal(a1-3)]GlcNAcGlcNAc(b1-2)Man(a1-3)[Man(a1-3)[Man(a1-6)]Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)GlcNAcGlcNAc(b1-2)Man(a1-3)[Man(a1-3)[Man(a1-6)]Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGlcNAc(b1-2)Man(a1-3)[Xyl(b1-2)][Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcGlcNAc(b1-2)Man(a1-3)[Xyl(b1-2)][Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGlcNAc(b1-2)Man(a1-6)[Man(a1-3)]Man(b1-4)GlcNAc(b1-4)GlcNAcGlcNAc(b1-2)Man(a1-6)[Man(a1-3)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGlcNAc(b1-2)Man(a1-6)[Xyl(b1-2)][Man(a1-3)]Man(b1-4)GlcNAc(b1-4)GlcNAcGlcNAc(b1-2)Man(a1-6)[Xyl(b1-2)][Man(a1-3)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGlcNAc(b1-2)Man(a1-?)[Man(a1-?)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)GlcNAcGlcNAc(b1-2)Man(a1-?)[Man(a1-?)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGlcNAc(b1-2)Man(a1-?)[Xyl(b1-2)][Man(a1-?)]Man(b1-4)GlcNAc(b1-4)GlcNAcGlcNAc(b1-2)Man(a1-?)[Xyl(b1-2)][Man(a1-?)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGlcNAc(b1-4)Man(a1-3)[GlcNAc(b1-4)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGlcNAc(b1-4)Man(a1-3)[Xyl(b1-2)][Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcGlcNAc(b1-4)Man(a1-3)[Xyl(b1-2)][Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGlcNAc(b1-4)Man(a1-6)[Xyl(b1-2)][Man(a1-3)]Man(b1-4)GlcNAc(b1-4)GlcNAcGlcNAc(b1-4)Man(a1-6)[Xyl(b1-2)][Man(a1-3)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGlcNAc(b1-?)Man(a1-3)[GlcNAc(b1-?)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGlcOMe(b1-3)[XylOMe(b1-4)]RhaOMe(a1-2)D-FucOMeGlcf(b1-2)Xyl(b1-4)Rha(b1-4)[Xyl(b1-3)]XylHexf(?1-?)Xyl(b1-4)Rha(b1-4)[Xyl(a1-3)]XylL-Lyx(a1-2)Ara(a1-2)GlcALyx(a1-2)Ara(a1-2)GlcAMan(a1-2)Man(a1-2)Man(a1-2)Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-2)Man(a1-2)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcMan(a1-2)Man(a1-2)Man(a1-2)Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-2)Man(a1-2)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcNMan(a1-2)Man(a1-2)Man(a1-2)Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-2)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcMan(a1-2)Man(a1-2)Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-2)Man(a1-2)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcMan(a1-2)Man(a1-2)Man(a1-3)[Man(a1-2)Man(a1-2)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcMan(a1-2)Man(a1-2)Man(a1-3)[Man(a1-2)Man(a1-3)[Man(a1-6)]Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcMan(a1-2)Man(a1-2)Man(a1-3)[Man(a1-3)[Man(a1-6)]Man(a1-6)]Man(b1-4)GlcNAcMan(a1-2)Man(a1-2)Man(a1-3)[Man(a1-3)[Man(a1-6)]Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcNMan(a1-2)Man(a1-2)Man(a1-3)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcMan(a1-2)Man(a1-2)Man(a1-6)[Man(a1-2)Man(a1-3)]Man(a1-3)[Man(a1-2)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcMan(a1-2)Man(a1-2)[Man(a1-6)]Man(a1-3)[Man(a1-3)[Man(a1-2)Man(a1-6)]Man(a1-6)]Man(b1-4)GlcNAcMan(a1-2)Man(a1-3)Man(b1-4)GlcNAc(b1-4)GlcNAcMan(a1-2)Man(a1-3)[Man(a1-2)Man(a1-2)Man(a1-6)]Man(a1-3)[Man(a1-2)Man(a1-2)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcMan(a1-2)Man(a1-3)[Man(a1-2)Man(a1-6)]Man(a1-3)[Man(a1-2)Man(a1-2)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcMan(a1-2)Man(a1-3)[Man(a1-2)Man(a1-6)]Man(a1-6)[Man(a1-2)Man(a1-2)Man(a1-3)]Man(b1-4)GlcNAcMan(a1-2)Man(a1-3)[Man(a1-2)Man(a1-6)]Man(a1-6)[Man(a1-2)Man(a1-2)Man(a1-3)]Man(b1-4)GlcNAc(b1-4)GlcNAcNMan(a1-2)Man(a1-3)[Man(a1-2)Man(a1-6)]Man(a1-6)[Man(a1-2)Man(a1-3)]Man(b1-4)GlcNAc(b1-4)GlcNAcMan(a1-2)Man(a1-3)[Man(a1-3)[Man(a1-6)]Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcMan(a1-2)Man(a1-3)[Man(a1-6)]Man(a1-6)[Man(a1-2)Man(a1-2)Man(a1-3)]Man(b1-4)GlcNAc(b1-4)GlcNAcNMan(a1-2)Man(a1-3)[Man(a1-6)]Man(a1-6)[Man(a1-2)Man(a1-3)]Man(b1-4)GlcNAc(b1-4)GlcNAcMan(a1-2)Man(a1-3)[Man(a1-6)]Man(a1-6)[Man(a1-2)Man(a1-3)]Man(b1-4)GlcNAc(b1-4)GlcNAcNMan(a1-2)Man(a1-6)[Man(a1-2)Man(a1-3)]Man(a1-6)[Man(a1-2)Man(a1-2)Man(a1-3)]Man(b1-4)GlcNAc(b1-4)GlcNAcNMan(a1-2)Man(a1-6)[Man(a1-3)]Man(a1-3)[Man(a1-2)Man(a1-6)[Man(a1-3)]Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcNMan(a1-2)Man(a1-6)[Man(a1-3)]Man(a1-6)[Man(a1-2)Man(a1-2)Man(a1-3)]Man(b1-4)GlcNAc(b1-4)GlcNAcNMan(a1-2)Man(a1-6)[Man(a1-3)]Man(a1-6)[Man(a1-2)Man(a1-3)]Man(b1-4)GlcNAc(b1-4)GlcNAcMan(a1-2)Man(a1-6)[Man(a1-3)]Man(a1-6)[Man(a1-3)]Man(b1-4)GlcNAc(b1-4)GlcNAcMan(a1-2)[Man(a1-3)]Man(a1-6)[Xyl(b1-2)][Man(a1-3)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcMan(a1-3)Man(a1-6)[Man(a1-3)]Man(b1-4)GlcNAc(b1-4)GlcNAcMan(a1-3)Man(a1-6)[Xyl(b1-2)][Man(a1-3)]Man(a1-6)Man(b1-4)GlcNAc(b1-4)GlcNAcMan(a1-3)Man(a1-6)[Xyl(b1-2)][Man(a1-3)]Man(b1-4)GlcNAc(b1-4)GlcNAcMan(a1-3)Man(b1-4)GlcNAc(b1-4)GlcNAcMan(a1-3)Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcMan(a1-3)[Gal(b1-3)GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcMan(a1-3)[GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcMan(a1-3)[Man(a1-2)Man(a1-6)]Man(a1-3)[Man(a1-3)[Man(a1-2)Man(a1-6)]Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcMan(a1-3)[Man(a1-2)Man(a1-6)]Man(a1-6)[Man(a1-2)Man(a1-2)Man(a1-3)]Man(b1-4)GlcNAc(b1-4)GlcNAcNMan(a1-3)[Man(a1-3)[Man(a1-6)]Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcMan(a1-3)[Man(a1-6)]Man(a1-6)[Man(a1-2)Man(a1-3)]Man(b1-4)GlcNAcMan(a1-3)[Man(a1-6)]Man(a1-6)[Man(a1-3)]Man(b1-4)GlcNAcMan(a1-3)[Man(a1-6)]Man(a1-6)[Man(a1-3)]Man(b1-4)GlcNAc(b1-4)GlcNAcMan(a1-3)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcMan(a1-3)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcMan(a1-3)[Man(a1-6)][Xylf(a1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcMan(a1-3)[Xyl(b1-2)][Man(a1-6)]Man(b1-4)GlcNAcMan(a1-3)[Xyl(b1-2)][Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc-olMan(a1-3)[Xyl(b1-2)][Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcNMan(a1-3)[Xyl(b1-2)][Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]HexMan(a1-3)[Xyl(b1-2)][Man(a1-6)]Man(b1-4)ManNAcMan(a1-3)[Xylf(b1-2)][Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcMan(a1-6)[Man(a1-3)]Man(a1-6)[Man(a1-3)]Man(b1-4)GlcNAc(b1-4)GlcNAcMan(a1-6)[Xyl(b1-2)][Man(a1-3)]Man(a1-6)Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcMan(a1-?)Man(a1-6)[Man(a1-3)]Man(b1-4)GlcNAcMan(a1-?)Man(a1-6)[Man(a1-3)]Man(b1-4)GlcNAc(b1-4)GlcNAcMan(a1-?)Man(a1-6)[Xyl(b1-2)][Man(a1-3)]Man(b1-4)GlcNAc(b1-4)GlcNAcMan(a1-?)Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcMan(b1-2)ManMan(b1-4)Gal(b1-4)Gal(b1-4)ManMan(b1-4)Gal(b1-4)Gal(b1-4)ManOMeMan(b1-4)ManMan(b1-4)Man(b1-4)ManMan(b1-4)Man(b1-4)Man(b1-4)ManMan(b1-4)Man(b1-4)Man(b1-4)Man(b1-4)ManMan(b1-4)Man(b1-4)Man(b1-4)[Gal(a1-6)]ManMan(b1-4)Man(b1-4)[Gal(a1-6)]ManMan(b1-4)Man(b1-4)[Gal(a1-6)]Man(b1-4)[Gal(a1-6)]Man(b1-4)Man(b1-4)Man(b1-4)ManMan(b1-4)Man(b1-4)[Gal(a1-6)]Man(b1-4)[Gal(a1-6)]Man(b1-4)Man(b1-4)Man(b1-4)[Man(b1-6)]Man(b1-4)[Man(b1-6)]Man(b1-4)Man(b1-4)Man(b1-4)[Gal(a1-6)]Man(b1-4)[Gal(a1-3)Gal(a1-3)Gal(a1-6)]Man(b1-4)Man(b1-4)Man(b1-4)[Man(b1-6)]Man(b1-4)[Man(b1-6)]Man(b1-4)Man(b1-4)Man(b1-4)[Man(b1-6)]Man(b1-4)[Man(b1-6)]Man(b1-4)Man(b1-4)ManMan(b1-4)[Gal(a1-6)]ManMan(b1-4)[Gal(a1-6)]Man(b1-4)ManMan(b1-4)[Gal(a1-6)]Man(b1-4)Man(b1-4)ManMan(b1-4)[Gal(a1-6)]Man(b1-4)[Gal(a1-6)]ManMan(b1-4)[Gal(a1-6)]Man(b1-4)[Gal(a1-6)]Man(b1-4)[Gal(a1-6)]Man(b1-4)ManMan(b1-4)[Gal(a1-6)]Man(b1-4)[Gal(a1-6)]Man(b1-4)[Gal(a1-6)]Man(b1-4)[Gal(a1-6)]ManMan(b1-6)GlcNeu5Ac(a2-6)Gal(b1-4)GlcNAc(b1-2)Man(a1-3)[Neu5Ac(a2-6)Gal(b1-4)GlcNAc(b1-2)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-6)]GlcNAcNeu5Ac(a2-6)Gal(b1-4)GlcNAc(b1-2)[Neu5Ac(a2-3)Gal(b1-4)GlcNAc(b1-4)]Man(a1-3)[Neu5Ac(a2-6)Gal(b1-4)GlcNAc(b1-2)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcRha(a1-2)AraRha(a1-2)Ara(a1-2)GlcARha(a1-2)Ara(a1-2)GlcA6MeRha(a1-2)Ara(a1-2)GlcAOMeRha(a1-2)D-Ara(b1-2)GlcARha(a1-2)Gal(b1-2)GlcRha(a1-2)Gal(b1-2)GlcARha(a1-2)Gal(b1-2)GlcA6MeRha(a1-2)Gal(b1-2)GlcAOMeRha(a1-2)Glc(b1-2)GlcRha(a1-2)Glc(b1-2)GlcARha(a1-2)Glc(b1-2)GlcA6MeRha(a1-2)Glc(b1-2)GlcAOMeRha(a1-2)Glc(b1-6)GlcRha(a1-2)GlcA(b1-2)GlcARha(a1-2)GlcAOMe(b1-2)GlcAOMeRha(a1-2)Rha(a1-2)Gal(b1-4)[Glc(b1-2)]GlcARha(a1-2)XylRha(a1-2)Xyl(b1-2)GlcARha(a1-2)Xyl(b1-2)GlcA6MeRha(a1-2)Xyl(b1-2)GlcAOMeRha(a1-2)Xyl3AcRha(a1-2)Xyl4AcRha(a1-2)[Glc(b1-3)]GlcRha(a1-2)[Glc(b1-6)]Gal(b1-2)GlcA6MeRha(a1-2)[Rha(a1-4)]GlcRha(a1-2)[Rha(a1-6)]GalRha(a1-2)[Rha(a1-6)]GlcRha(a1-2)[Xyl(b1-4)]GlcRha(a1-2)[Xyl(b1-4)]Glc(b1-6)GlcRha(a1-3)GlcARha(a1-4)Gal(b1-2)GlcARha(a1-4)Gal(b1-2)GlcAOMeRha(a1-4)Gal(b1-2)GlcOMeRha(a1-4)Gal(b1-4)Gal(b1-4)GalGroRha(a1-4)Xyl(b1-2)GlcRha(a1-4)Xyl(b1-2)GlcARha(a1-4)Xyl(b1-2)GlcAOMeRha(a1-6)[Xyl(b1-3)Xyl(b1-2)]Glc(b1-2)GlcRha(b1-2)Glc(b1-2)GlcARha1Fer(a1-4)Fruf(b2-1)GlcOBzRhaOMe(a1-2)[RhaOMe(a1-6)]GlcOMe-olRhaOMe(a1-6)GlcOMe(b1-2)GlcOMe-olXyl(a1-6)Glc(b1-4)GlcXyl(a1-6)Glc(b1-4)Glc-olXyl(a1-6)Glc(b1-4)[Fuc(a1-2)Gal(b1-2)Xyl(a1-6)]Glc(b1-4)GlcXyl(a1-6)Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)GlcXyl(a1-6)Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc-olXyl(a1-6)Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Fuc(a1-2)Gal(b1-2)Xyl(a1-6)]Glc(b1-4)GlcXyl(a1-6)Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)GlcXyl(a1-6)Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)GlcXyl(a1-6)Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)GlcXyl(a1-6)Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)GlcXyl(a1-6)Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc-olXyl(a1-6)Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)GlcXyl(a1-6)Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)GlcXyl(a1-6)Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc-olXyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)GlcXyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc-olXyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Fuc(a1-2)Gal(b1-2)Xyl(a1-6)]Glc(b1-4)GlcXyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)GlcXyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)GlcXyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)GlcXyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)GlcXyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc-olXyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)GlcXyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)GlcXyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)GlcXyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)GlcXyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)GlcXyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc-olXyl(b1-2)Ara(a1-6)GlcXyl(b1-2)Ara(a1-6)GlcNAcXyl(b1-2)Ara(a1-6)[Glc(b1-2)]GlcXyl(b1-2)Ara(a1-6)[Glc(b1-4)]GlcNAcXyl(b1-2)D-Fuc(b1-6)GlcXyl(b1-2)D-Fuc(b1-6)GlcNAcXyl(b1-2)D-Fuc(b1-6)[Glc(b1-2)]GlcXyl(b1-2)Fuc(a1-6)GlcXyl(b1-2)Fuc(a1-6)GlcNAcXyl(b1-2)Gal(b1-2)GlcA6MeXyl(b1-2)Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcXyl(b1-2)Rha(a1-2)AraXyl(b1-2)[Glc(b1-3)]AraXyl(b1-2)[Man(a1-3)][Man(a1-3)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcXyl(b1-2)[Man(a1-3)][Man(a1-6)]Man(a1-3)Man(b1-4)GlcNAc(b1-4)GlcNAcXyl(b1-2)[Man(a1-3)][Man(a1-6)]Man(a1-3)Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcXyl(b1-2)[Man(a1-3)][Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcXyl(b1-2)[Man(a1-3)][Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcXyl(b1-2)[Man(a1-3)][Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcNXyl(b1-2)[Man(a1-3)][Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-6)]GlcNAcXyl(b1-2)[Man(a1-6)]Man(a1-3)Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcXyl(b1-2)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcXyl(b1-2)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcXyl(b1-2)[Rha(a1-3)]GlcAXyl(b1-3)AraXyl(b1-3)Xyl(b1-2)[Rha(a1-6)]Glc(b1-2)GlcXyl(b1-3)Xyl(b1-4)Rha(a1-2)[Rha(a1-6)]GlcXyl(b1-3)Xyl(b1-4)Rha(a1-2)[Rha(a1-6)]Glc(b1-2)GlcXyl(b1-4)Rha(a1-2)AraXyl(b1-4)Rha(a1-2)D-FucXyl(b1-4)Rha(a1-2)D-FucOMeXyl(b1-4)Rha(a1-2)[Rha(a1-6)]GlcXyl(b1-4)Xyl(b1-4)Xyl(b1-4)Xyl3Ac(b1-4)Xyl(b1-4)Xyl(b1-4)[GlcA(a1-2)]Xyl(b1-4)XylXyl(b1-4)Xyl(b1-4)Xyl(b1-4)Xyl3Ac(b1-4)Xyl(b1-4)Xyl(b1-4)[GlcA(a1-2)]Xyl3Ac(b1-4)XylXyl(b1-4)Xyl(b1-4)Xyl(b1-4)Xyl3Ac(b1-4)Xyl(b1-4)Xyl(b1-4)[GlcA4Me(a1-2)]Xyl(b1-4)XylXyl(b1-4)Xyl(b1-4)Xyl(b1-4)Xyl3Ac(b1-4)Xyl(b1-4)Xyl(b1-4)[GlcA4Me(a1-2)]Xyl3Ac(b1-4)XylXyl(b1-4)Xyl(b1-4)[GlcA(a1-2)]Xyl(b1-4)Xyl(b1-4)Xyl(b1-4)Xyl(b1-4)XylXyl(b1-4)[GlcAOMe(a1-2)]Xyl(b1-4)Xyl(b1-4)Xyl(b1-4)XylXyl2Ac3Ac4Ac(b1-3)AraXylOMe(b1-2)[RhaOMe(a1-6)]GlcOMe(b1-2)GlcOMe-olXylOMe(b1-3)XylOMe(b1-2)[RhaOMe(a1-6)]GlcOMe(b1-2)GlcOMe-olXylOMe(b1-4)RhaOMe(a1-2)D-FucOMeXylOMe(b1-4)RhaOMe(a1-2)[RhaOMe(a1-6)]GlcOMeXylOMe(b1-4)RhaOMe(a1-2)[RhaOMe(a1-6)]GlcOMe-olXylf(b1-2)Xyl(b1-3)[Rha(b1-2)Rha(b1-4)]Xyl[Araf(a1-3)Gal(b1-3)Gal(b1-6)]Gal(b1-3)Gal[Araf(a1-3)Gal(b1-6)]Gal(b1-3)Gal[Gal(a1-4)Gal(a1-6)]Man(b1-4)[Gal(a1-6)]Man(b1-4)[Man(b1-4)Man(b1-4)Man(b1-4)Gal(a1-6)]Man(b1-2)[Gal(a1-6)]Man(b1-2)[Gal(a1-4)Gal(a1-6)]Man(b1-4)Man[Gal(a1-6)]Man(b1-4)Man[Gal(a1-6)]Man(b1-4)Man(b1-4)Man[Gal(a1-6)]Man(b1-4)Man(b1-4)Man(b1-4)Man(b1-4)Man[Gal(a1-6)]Man(b1-4)[Gal(a1-6)]Man(b1-4)Man(b1-4)Man[Gal(a1-6)]Man(b1-4)[Gal(a1-6)]Man(b1-4)[Gal(a1-6)]Man(b1-4)Man[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Gal(b1-3)Gal(b1-6)[Araf(a1-3)]Gal(b1-6)]Gal(b1-3)Gal[Gal(b1-3)Gal(b1-6)]Gal(b1-3)Gal[Gal(b1-6)Gal(b1-6)Gal(b1-6)]Gal(b1-3)Gal[Gal(b1-6)Gal(b1-6)]Gal(b1-3)Gal[Gal(b1-6)]Gal(b1-3)Gal(b1-3)Gal(b1-3)Gal(b1-3)Gal(b1-3)Gal(b1-3)Gal[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Araf(a1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Araf(a1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Araf(a1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Araf(a1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-5)Araf(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-5)Araf(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Fuc(a1-2)Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-5)Araf(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-5)Araf(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)GlcApif(a1-2)Xyl(b1-2)[Glc6Ac(b1-4)]GlcAra(a1-2)Ara(a1-6)GlcNAcAra(a1-2)Glc(b1-2)AraAra(a1-2)GlcAAra(a1-2)[Glc(b1-6)]GlcAra(a1-6)GlcAraf(a1-3)Araf(a1-5)[Araf(a1-6)Gal(b1-6)Glc(b1-6)Man(a1-3)]Araf(a1-5)Araf(a1-3)Araf(a1-3)ArafAraf(a1-3)Gal(b1-6)GalD-Apif(b1-2)GlcD-Apif(b1-2)GlcAD-Apif(b1-3)Xyl(b1-2)[Glc6Ac(b1-4)]GlcD-Apif(b1-3)Xyl(b1-4)Rha(a1-2)AraD-Apif(b1-3)Xyl(b1-4)Rha(a1-2)D-FucD-Apif(b1-3)Xyl(b1-4)[Glc(b1-3)]Rha(a1-2)D-FucD-Apif(b1-3)[Gal(b1-4)Xyl(b1-4)]Rha(a1-2)D-FucD-Apif(b1-3)[Gal(b1-4)Xyl(b1-4)]Rha(a1-2)[Rha(a1-3)]D-FucD-Apif(b1-3)[Gal(b1-4)Xyl(b1-4)]Rha(a1-3)D-FucD-Apif(b1-6)GlcD-ApifOMe(b1-3)XylOMe(b1-4)RhaOMe(a1-2)D-FucOMeD-ApifOMe(b1-3)XylOMe(b1-4)[GlcOMe(b1-3)]RhaOMe(a1-2)D-FucOMeFruf(a2-1)[Glc(b1-2)][Glc(b1-3)Glc4Ac6Ac(b1-3)]GlcFruf(a2-1)[Glc(b1-2)][Glc(b1-3)Glc4Ac6Ac(b1-3)]Glc6AcFruf(a2-1)[Glc(b1-2)][Glc(b1-3)Glc6Ac(b1-3)]GlcFruf(a2-1)[Glc(b1-2)][Glc(b1-3)Glc6Ac(b1-3)]Glc6AcFruf(b2-1)Glc3Ac6AcFruf(b2-1)Glc4Ac6AcFruf(b2-1)Glc6AcFruf(b2-1)[Glc(b1-2)]GlcFruf(b2-1)[Glc(b1-2)][Glc(b1-3)Glc(b1-3)]GlcFruf(b2-1)[Glc(b1-2)][Glc(b1-3)]Glc6AcFruf(b2-1)[Glc(b1-2)][Glc(b1-4)Glc(b1-3)]GlcFruf(b2-1)[Glc(b1-2)][Glc(b1-4)Glc(b1-3)]Glc6AcFruf(b2-1)[Glc(b1-2)][Glc(b1-4)Glc6Ac(b1-3)]GlcFruf(b2-1)[Glc(b1-2)][Glc(b1-4)Glc6Ac(b1-3)]Glc6AcFruf(b2-1)[Glc(b1-2)][Glc6Ac(b1-3)]GlcFruf(b2-1)[Glc(b1-2)][Glc6Ac(b1-3)]Glc6AcFruf(b2-1)[Glc(b1-4)Glc6Ac(b1-3)]Glc6AcFruf(b2-1)[Glc3Ac(b1-2)]GlcFruf(b2-1)[Glc6Ac(b1-2)]GlcFruf1Ac(b2-1)Glc2Ac4Ac6AcFuc(a1-2)Gal(b1-2)Xyl(a1-6)GlcFuc(a1-2)Gal(b1-2)Xyl(a1-6)Glc(b1-4)GlcFuc(a1-2)Gal(b1-2)Xyl(a1-6)[Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)]Glc(b1-4)GlcFuc(a1-2)Gal(b1-2)Xyl(a1-6)[Glc(b1-4)]Glc(b1-4)GlcFuc(a1-2)Gal(b1-4)XylFuc(a1-4)GlcNAc(b1-2)Man(a1-3)[Gal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcFuc(a1-6)GlcNAc(b1-2)[Man(a1-6)]Man(a1-6)[Xyl(b1-2)][Man(a1-3)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(?1-?)Gal(b1-4)[Fuc(a1-3)]GlcNAc(b1-2)Man(a1-3)[Man(a1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(?1-?)[Gal(?1-?)]GlcNAc(?1-?)[Fuc(a1-3)]GlcNAc(b1-2)Man(a1-3)[Gal(?1-?)Man(a1-3)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(a1-4)GalGal(a1-6)GalGal(a1-6)Gal(a1-6)GalGal(a1-6)Gal(a1-6)Gal(a1-6)Gal(a1-6)Glc(a1-2)FrufGal(a1-6)Gal(a1-6)Gal(a1-6)Gal(a1-6)[Fruf(b2-1)]GlcGal(a1-6)Gal(a1-6)Gal(a1-6)GlcGal(a1-6)Gal(a1-6)Gal(a1-6)Glc(a1-2)FrufGal(a1-6)Gal(a1-6)GlcGal(a1-6)Gal(a1-6)Glc(a1-2)FrufGal(a1-6)Glc(a1-2)FrufGal(a1-6)ManGal(a1-6)Man(b1-4)ManGal(a1-6)Man(b1-4)Man(b1-4)Man(b1-4)ManGal(a1-6)Man(b1-4)Man(b1-4)Man(b1-4)[Gal(a1-6)]Man(b1-4)Man(b1-4)Man(b1-4)[Gal(a1-6)]ManGal(a1-6)Man(b1-4)Man(b1-4)[Gal(a1-6)]ManGal(a1-6)Man(b1-4)[Gal(a1-6)]ManGal(b1-2)GlcAGal(b1-2)GlcA6MeGal(b1-2)Xyl(a1-6)Glc(b1-4)[Fuc(a1-2)Gal(b1-2)Xyl(a1-6)]Glc(b1-4)GlcGal(b1-2)Xyl(a1-6)[Glc(b1-4)]Glc(b1-4)[Fuc(a1-2)Gal(b1-2)Xyl(a1-6)]Glc(b1-4)GlcGal(b1-2)Xyl(a1-6)[Glc(b1-4)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)GlcGal(b1-2)[Xyl(b1-3)]GlcAGal(b1-3)GlcNAc(b1-2)Man(a1-3)[Gal(b1-3)GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)GlcNAc(b1-2)Man(a1-3)[Gal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)GlcNAc(b1-2)Man(a1-3)[GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)GlcNAc(b1-2)Man(a1-3)[Xyl(b1-2)][Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)GlcNAc(b1-2)Man(a1-6)[GlcNAc(b1-2)Man(a1-3)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)GlcNAc(b1-4)Man(a1-3)[Gal(b1-3)GlcNAc(b1-4)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)GlcNAc(b1-4)Man(a1-3)[GlcNAc(b1-4)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)GlcNAc(b1-4)Man(a1-3)[Xyl(b1-2)][Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)GlcNAc(b1-4)Man(a1-6)[GlcNAc(b1-4)Man(a1-3)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)GlcNAc(b1-4)Man(a1-6)[Xyl(b1-2)][Man(a1-3)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-3)[Gal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(a1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-3)[Gal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-3)[GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-3)[Xyl(b1-2)][Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-6)[GlcNAc(b1-2)Man(a1-3)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-6)[Xyl(b1-2)][Man(a1-3)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-?)[Gal(b1-3)GlcNAc(b1-2)Man(a1-?)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-?)[GlcNAc(b1-2)Man(a1-?)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-?)[Man(a1-?)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)[Man(a1-6)]Man(a1-6)[Xyl(b1-2)][Man(a1-3)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-6)]GlcNAc(b1-2)[Man(a1-6)]Man(a1-6)[Xyl(b1-2)][Man(a1-3)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-4)Gal(b1-4)ManGal(b1-4)Gal(b1-4)ManOMeGal(b1-4)GlcAGal(b1-4)GlcNAc(b1-2)[Gal(b1-4)GlcNAc(b1-4)]Man(a1-3)[Gal(b1-4)GlcNAc(b1-2)[Gal(b1-4)GlcNAc(b1-6)]Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcGal(b1-4)Man(b1-4)ManGal(b1-4)Man(b1-4)Man(b1-4)GalGal(b1-4)Xyl(b1-4)Rha(a1-2)D-FucGal(b1-4)Xyl(b1-4)Rha(a1-2)D-Fuc1CoumOMeGal(b1-4)Xyl(b1-4)Rha(a1-2)D-Fuc1FerOMeGal(b1-4)Xyl(b1-4)Rha(a1-2)[Rha(a1-3)]D-FucGal(b1-4)Xyl(b1-4)Rha(a1-2)[Rha(a1-3)]D-Fuc1CoumOMeGal(b1-4)Xyl(b1-4)Rha(a1-2)[Rha(a1-3)]D-FucOMeOSinGal(b1-4)Xyl(b1-4)[D-Apif(b1-3)]Rha(a1-2)D-FucGal(b1-4)Xyl(b1-4)[D-Apif(b1-3)]Rha(a1-2)D-Fuc1CoumOMeGal(b1-4)Xyl(b1-4)[D-Apif(b1-3)]Rha(a1-2)[Rha(a1-3)]D-FucGal(b1-4)Xyl(b1-4)[D-Apif(b1-3)]Rha(a1-2)[Rha(a1-3)]D-Fuc1CoumOMeGal(b1-4)[Fuc(a1-3)]GlcNAc(b1-2)Man(a1-6)[GlcNAc(b1-2)Man(a1-3)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)GlcNAcGalA(a1-2)[Araf(a1-5)Araf(a1-4)]Rha(b1-4)GalAGalA(a1-4)GalA(a1-4)GalA(a1-4)GalA(a1-2)Rha(a1-4)GalA(a1-2)Rha(a1-4)GalA(a1-2)GalAGalA(a1-4)GalA(a1-4)GalA(a1-4)GalA(a1-4)GalA(a1-4)GalA(a1-4)GalA(a1-4)GalA(a1-4)GalA(a1-4)GalA(a1-4)GalA(a1-4)GalAGalOMe(b1-2)[XylOMe(b1-3)]GlcAOMeGalOMe(b1-4)XylOMe(b1-4)RhaOMe(a1-2)D-FucOMeGalOMe(b1-4)XylOMe(b1-4)RhaOMe(a1-2)[RhaOMe(a1-3)]D-FucOMeGalOMe(b1-4)XylOMe(b1-4)[D-ApifOMe(b1-3)]RhaOMe(a1-2)[RhaOMe(a1-3)]D-FucOMeGalf(b1-2)[Galf(b1-4)]ManGlc(a1-2)Glc(a1-3)Glc(a1-3)Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-2)Man(a1-3)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcGlc(a1-2)Glc(a1-3)Glc(a1-3)Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-3)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcGlc(a1-2)Glc(a1-3)Glc(a1-3)Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcGlc(a1-2)Glc(a1-3)Glc(a1-3)Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcNGlc(a1-2)Rha(a1-6)GlcGlc(a1-3)Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-2)Man(a1-3)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcGlc(a1-3)Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-2)Man(a1-3)[Man(a1-2)Man(a1-6)]Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcGlc(a1-3)Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-2)Man(a1-3)[Man(a1-6)]Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcGlc(a1-4)Glc(a1-2)Rha(a1-6)GlcGlc(a1-4)Glc(a1-4)Glc(a1-6)GlcGlc(a1-4)Glc(a1-4)GlcAGlc(a1-4)GlcA(b1-2)GlcAGlc(b1-2)AraGlc(b1-2)Ara(a1-2)GlcAGlc(b1-2)Gal(b1-2)Gal(b1-2)GlcAGlc(b1-2)Gal(b1-2)GlcAGlc(b1-2)Gal(b1-2)GlcA(b1-3)[Glc(b1-3)]AraGlc(b1-2)GlcGlc(b1-2)Glc(a1-2)FrufOBzOCinGlc(b1-2)Glc(b1-2)GlcGlc(b1-2)GlcAGlc(b1-2)[Ara(a1-3)]GlcA6MeGlc(b1-2)[Ara(a1-3)]GlcAOMeGlc(b1-2)[Ara(a1-6)]GlcGlc(b1-2)[Glc(b1-3)]Glc(a1-2)FrufGlc(b1-2)[Glc(b1-3)]Glc1Fer6Ac(a1-2)Fruf1FerOBzGlc(b1-2)[Glc6Ac(b1-3)]Glc1Fer(a1-2)Fruf1FerOBzGlc(b1-2)[Rha(a1-3)]GlcAGlc(b1-2)[Xyl(b1-2)Ara(a1-6)]GlcGlc(b1-2)[Xyl(b1-2)D-Fuc(b1-6)]GlcGlc(b1-3)AraGlc(b1-3)GlcGlc(b1-3)Glc(b1-3)[Glc(b1-2)]Glc(a1-2)FrufGlc(b1-3)Glc6Ac(b1-3)[Glc(b1-2)]Glc(a1-2)FrufGlc(b1-3)Glc6Ac(b1-3)[Glc(b1-2)]Glc1Coum6Ac(a1-2)Fruf1CoumOBzGlc(b1-3)Glc6Ac(b1-3)[Glc(b1-2)]Glc1Fer(a1-2)Fruf1CoumOBzGlc(b1-3)Glc6Ac(b1-3)[Glc(b1-2)]Glc1Fer(a1-2)Fruf1FerOBzGlc(b1-3)Glc6Ac(b1-3)[Glc(b1-2)]Glc1Fer6Ac(a1-2)Fruf1CoumOBzGlc(b1-3)Glc6Ac(b1-3)[Glc(b1-2)]Glc1Fer6Ac(a1-2)Fruf1FerOBzGlc(b1-3)Glc6Ac(b1-3)[Glc(b1-2)][Rha(a1-4)]Glc1Coum6Ac(a1-2)Fruf1CoumOBzGlc(b1-3)Glc6Ac(b1-3)[Glc(b1-2)][Rha(a1-4)]Glc1Fer6Ac(a1-2)Fruf1CoumOBzGlc(b1-3)Rha1Fer(a1-4)Fruf(b2-1)GlcOBzGlc(b1-3)[Araf(a1-4)]Rha(a1-2)GlcGlc(b1-3)[Xyl(b1-4)]Rha(a1-2)D-FucOMeGlc(b1-4)Glc(b1-3)[Glc(b1-2)]Glc(a1-2)FrufGlc(b1-4)Glc(b1-3)[Glc(b1-2)]Glc1Coum6Ac(a1-2)Fruf1FerOBzGlc(b1-4)Glc(b1-3)[Glc(b1-2)]Glc1Fer(a1-2)Fruf1FerOBzGlc(b1-4)Glc(b1-3)[Glc(b1-2)]Glc1Fer6Ac(a1-2)Fruf1CoumOBzGlc(b1-4)Glc(b1-3)[Glc(b1-2)]Glc1Fer6Ac(a1-2)Fruf1FerOBzGlc(b1-4)Glc(b1-4)GlcGlc(b1-4)Glc(b1-4)Glc(b1-4)ManGlc(b1-4)Glc6Ac(b1-3)Glc1Fer6Ac(a1-2)Fruf1FerOBzGlc(b1-4)Glc6Ac(b1-3)[Glc(b1-2)]Glc1Coum6Ac(a1-2)Fruf1FerOBzGlc(b1-4)Glc6Ac(b1-3)[Glc(b1-2)]Glc1Fer(a1-2)Fruf1FerOBzGlc(b1-4)Glc6Ac(b1-3)[Glc(b1-2)]Glc1Fer6Ac(a1-2)Fruf1CoumOBzGlc(b1-4)Glc6Ac(b1-3)[Glc(b1-2)]Glc1Fer6Ac(a1-2)Fruf1FerOBzGlc(b1-4)Man(b1-4)GlcGlc(b1-4)RhaGlc(b1-4)Rha1Fer(a1-4)Fruf(b2-1)GlcOBzGlc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)GlcGlc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)GlcGlc(b1-6)Glc(b1-3)GlcGlc1CerGlc2Ac(b1-4)[D-Apif(b1-3)Xyl(b1-2)]GlcGlc2Ac3Ac4Ac6Ac(b1-3)AraGlc6Ac(b1-2)Glc(a1-2)FrufOBzOCinGlc6Ac(b1-3)Glc6Ac(b1-3)[Glc6Ac(b1-2)]Glc1Fer6Ac(a1-2)Fruf1CoumOAcOBzGlc6Ac(b1-3)Glc6Ac(b1-3)[Glc6Ac(b1-2)][RhaOAc(a1-4)]Glc1Fer6Ac(a1-2)Fruf1CoumOAcOBzGlc6Ac(b1-3)[Glc(b1-2)]Glc1Coum(a1-2)Fruf1CoumOBzGlc6Ac(b1-3)[Glc(b1-2)]Glc1Fer(a1-2)Fruf1CoumOBzGlc6Ac(b1-3)[Glc(b1-2)]Glc1Fer(a1-2)Fruf1FerOBzGlc6Ac(b1-3)[Glc(b1-2)]Glc1Fer6Ac(a1-2)Fruf1FerOBzGlcA(b1-2)GlcGlcA(b1-2)GlcAGlcA(b1-2)GlcA(b1-2)RhaGlcA4Me(a1-2)[Xyl(b1-4)Xyl(b1-4)Xyl(b1-4)Xyl(b1-4)]XylGlcA4Me(a1-2)[Xyl(b1-4)Xyl(b1-4)Xyl(b1-4)]XylGlcA4Me(a1-2)[Xyl(b1-4)]XylGlcNAc(b1-2)Man(a1-3)[Gal(b1-3)GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGlcNAc(b1-2)Man(a1-3)[GlcNAc(b1-2)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGlcNAc(b1-2)Man(a1-3)[GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)GlcNAcGlcNAc(b1-2)Man(a1-3)[GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGlcNAc(b1-2)Man(a1-3)[GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Gal(a1-3)]GlcNAcGlcNAc(b1-2)Man(a1-3)[Man(a1-3)[Man(a1-6)]Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)GlcNAcGlcNAc(b1-2)Man(a1-3)[Man(a1-3)[Man(a1-6)]Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGlcNAc(b1-2)Man(a1-3)[Xyl(b1-2)][Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcGlcNAc(b1-2)Man(a1-3)[Xyl(b1-2)][Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGlcNAc(b1-2)Man(a1-6)[Man(a1-3)]Man(b1-4)GlcNAc(b1-4)GlcNAcGlcNAc(b1-2)Man(a1-6)[Man(a1-3)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGlcNAc(b1-2)Man(a1-6)[Xyl(b1-2)][Man(a1-3)]Man(b1-4)GlcNAc(b1-4)GlcNAcGlcNAc(b1-2)Man(a1-6)[Xyl(b1-2)][Man(a1-3)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGlcNAc(b1-2)Man(a1-?)[Man(a1-?)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)GlcNAcGlcNAc(b1-2)Man(a1-?)[Man(a1-?)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGlcNAc(b1-2)Man(a1-?)[Xyl(b1-2)][Man(a1-?)]Man(b1-4)GlcNAc(b1-4)GlcNAcGlcNAc(b1-2)Man(a1-?)[Xyl(b1-2)][Man(a1-?)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGlcNAc(b1-4)Man(a1-3)[GlcNAc(b1-4)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGlcNAc(b1-4)Man(a1-3)[Xyl(b1-2)][Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcGlcNAc(b1-4)Man(a1-3)[Xyl(b1-2)][Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGlcNAc(b1-4)Man(a1-6)[Xyl(b1-2)][Man(a1-3)]Man(b1-4)GlcNAc(b1-4)GlcNAcGlcNAc(b1-4)Man(a1-6)[Xyl(b1-2)][Man(a1-3)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGlcNAc(b1-?)Man(a1-3)[GlcNAc(b1-?)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGlcOMe(b1-3)[XylOMe(b1-4)]RhaOMe(a1-2)D-FucOMeGlcf(b1-2)Xyl(b1-4)Rha(b1-4)[Xyl(b1-3)]XylHexf(?1-?)Xyl(b1-4)Rha(b1-4)[Xyl(a1-3)]XylL-Lyx(a1-2)Ara(a1-2)GlcALyx(a1-2)Ara(a1-2)GlcAMan(a1-2)Man(a1-2)Man(a1-2)Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-2)Man(a1-2)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcMan(a1-2)Man(a1-2)Man(a1-2)Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-2)Man(a1-2)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcNMan(a1-2)Man(a1-2)Man(a1-2)Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-2)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcMan(a1-2)Man(a1-2)Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-2)Man(a1-2)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcMan(a1-2)Man(a1-2)Man(a1-3)[Man(a1-2)Man(a1-2)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcMan(a1-2)Man(a1-2)Man(a1-3)[Man(a1-2)Man(a1-3)[Man(a1-6)]Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcMan(a1-2)Man(a1-2)Man(a1-3)[Man(a1-3)[Man(a1-6)]Man(a1-6)]Man(b1-4)GlcNAcMan(a1-2)Man(a1-2)Man(a1-3)[Man(a1-3)[Man(a1-6)]Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcNMan(a1-2)Man(a1-2)Man(a1-3)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcMan(a1-2)Man(a1-2)Man(a1-6)[Man(a1-2)Man(a1-3)]Man(a1-3)[Man(a1-2)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcMan(a1-2)Man(a1-2)[Man(a1-6)]Man(a1-3)[Man(a1-3)[Man(a1-2)Man(a1-6)]Man(a1-6)]Man(b1-4)GlcNAcMan(a1-2)Man(a1-3)Man(b1-4)GlcNAc(b1-4)GlcNAcMan(a1-2)Man(a1-3)[Man(a1-2)Man(a1-2)Man(a1-6)]Man(a1-3)[Man(a1-2)Man(a1-2)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcMan(a1-2)Man(a1-3)[Man(a1-2)Man(a1-6)]Man(a1-3)[Man(a1-2)Man(a1-2)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcMan(a1-2)Man(a1-3)[Man(a1-2)Man(a1-6)]Man(a1-6)[Man(a1-2)Man(a1-2)Man(a1-3)]Man(b1-4)GlcNAcMan(a1-2)Man(a1-3)[Man(a1-2)Man(a1-6)]Man(a1-6)[Man(a1-2)Man(a1-2)Man(a1-3)]Man(b1-4)GlcNAc(b1-4)GlcNAcNMan(a1-2)Man(a1-3)[Man(a1-2)Man(a1-6)]Man(a1-6)[Man(a1-2)Man(a1-3)]Man(b1-4)GlcNAc(b1-4)GlcNAcMan(a1-2)Man(a1-3)[Man(a1-3)[Man(a1-6)]Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcMan(a1-2)Man(a1-3)[Man(a1-6)]Man(a1-6)[Man(a1-2)Man(a1-2)Man(a1-3)]Man(b1-4)GlcNAc(b1-4)GlcNAcNMan(a1-2)Man(a1-3)[Man(a1-6)]Man(a1-6)[Man(a1-2)Man(a1-3)]Man(b1-4)GlcNAc(b1-4)GlcNAcMan(a1-2)Man(a1-3)[Man(a1-6)]Man(a1-6)[Man(a1-2)Man(a1-3)]Man(b1-4)GlcNAc(b1-4)GlcNAcNMan(a1-2)Man(a1-6)[Man(a1-2)Man(a1-3)]Man(a1-6)[Man(a1-2)Man(a1-2)Man(a1-3)]Man(b1-4)GlcNAc(b1-4)GlcNAcNMan(a1-2)Man(a1-6)[Man(a1-3)]Man(a1-3)[Man(a1-2)Man(a1-6)[Man(a1-3)]Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcNMan(a1-2)Man(a1-6)[Man(a1-3)]Man(a1-6)[Man(a1-2)Man(a1-2)Man(a1-3)]Man(b1-4)GlcNAc(b1-4)GlcNAcNMan(a1-2)Man(a1-6)[Man(a1-3)]Man(a1-6)[Man(a1-2)Man(a1-3)]Man(b1-4)GlcNAc(b1-4)GlcNAcMan(a1-2)Man(a1-6)[Man(a1-3)]Man(a1-6)[Man(a1-3)]Man(b1-4)GlcNAc(b1-4)GlcNAcMan(a1-2)[Man(a1-3)]Man(a1-6)[Xyl(b1-2)][Man(a1-3)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcMan(a1-3)Man(a1-6)[Man(a1-3)]Man(b1-4)GlcNAc(b1-4)GlcNAcMan(a1-3)Man(a1-6)[Xyl(b1-2)][Man(a1-3)]Man(a1-6)Man(b1-4)GlcNAc(b1-4)GlcNAcMan(a1-3)Man(a1-6)[Xyl(b1-2)][Man(a1-3)]Man(b1-4)GlcNAc(b1-4)GlcNAcMan(a1-3)Man(b1-4)GlcNAc(b1-4)GlcNAcMan(a1-3)Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcMan(a1-3)[Gal(b1-3)GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcMan(a1-3)[GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcMan(a1-3)[Man(a1-2)Man(a1-6)]Man(a1-3)[Man(a1-3)[Man(a1-2)Man(a1-6)]Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcMan(a1-3)[Man(a1-2)Man(a1-6)]Man(a1-6)[Man(a1-2)Man(a1-2)Man(a1-3)]Man(b1-4)GlcNAc(b1-4)GlcNAcNMan(a1-3)[Man(a1-3)[Man(a1-6)]Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcMan(a1-3)[Man(a1-6)]Man(a1-6)[Man(a1-2)Man(a1-3)]Man(b1-4)GlcNAcMan(a1-3)[Man(a1-6)]Man(a1-6)[Man(a1-3)]Man(b1-4)GlcNAcMan(a1-3)[Man(a1-6)]Man(a1-6)[Man(a1-3)]Man(b1-4)GlcNAc(b1-4)GlcNAcMan(a1-3)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcMan(a1-3)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcMan(a1-3)[Man(a1-6)][Xylf(a1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcMan(a1-3)[Xyl(b1-2)][Man(a1-6)]Man(b1-4)GlcNAcMan(a1-3)[Xyl(b1-2)][Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc-olMan(a1-3)[Xyl(b1-2)][Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcNMan(a1-3)[Xyl(b1-2)][Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]HexMan(a1-3)[Xyl(b1-2)][Man(a1-6)]Man(b1-4)ManNAcMan(a1-3)[Xylf(b1-2)][Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcMan(a1-6)[Man(a1-3)]Man(a1-6)[Man(a1-3)]Man(b1-4)GlcNAc(b1-4)GlcNAcMan(a1-6)[Xyl(b1-2)][Man(a1-3)]Man(a1-6)Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcMan(a1-?)Man(a1-6)[Man(a1-3)]Man(b1-4)GlcNAcMan(a1-?)Man(a1-6)[Man(a1-3)]Man(b1-4)GlcNAc(b1-4)GlcNAcMan(a1-?)Man(a1-6)[Xyl(b1-2)][Man(a1-3)]Man(b1-4)GlcNAc(b1-4)GlcNAcMan(a1-?)Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcMan(b1-2)ManMan(b1-4)Gal(b1-4)Gal(b1-4)ManMan(b1-4)Gal(b1-4)Gal(b1-4)ManOMeMan(b1-4)ManMan(b1-4)Man(b1-4)ManMan(b1-4)Man(b1-4)Man(b1-4)ManMan(b1-4)Man(b1-4)Man(b1-4)Man(b1-4)ManMan(b1-4)Man(b1-4)Man(b1-4)[Gal(a1-6)]ManMan(b1-4)Man(b1-4)[Gal(a1-6)]ManMan(b1-4)Man(b1-4)[Gal(a1-6)]Man(b1-4)[Gal(a1-6)]Man(b1-4)Man(b1-4)Man(b1-4)ManMan(b1-4)Man(b1-4)[Gal(a1-6)]Man(b1-4)[Gal(a1-6)]Man(b1-4)Man(b1-4)Man(b1-4)[Man(b1-6)]Man(b1-4)[Man(b1-6)]Man(b1-4)Man(b1-4)Man(b1-4)[Gal(a1-6)]Man(b1-4)[Gal(a1-3)Gal(a1-3)Gal(a1-6)]Man(b1-4)Man(b1-4)Man(b1-4)[Man(b1-6)]Man(b1-4)[Man(b1-6)]Man(b1-4)Man(b1-4)Man(b1-4)[Man(b1-6)]Man(b1-4)[Man(b1-6)]Man(b1-4)Man(b1-4)ManMan(b1-4)[Gal(a1-6)]ManMan(b1-4)[Gal(a1-6)]Man(b1-4)ManMan(b1-4)[Gal(a1-6)]Man(b1-4)Man(b1-4)ManMan(b1-4)[Gal(a1-6)]Man(b1-4)[Gal(a1-6)]ManMan(b1-4)[Gal(a1-6)]Man(b1-4)[Gal(a1-6)]Man(b1-4)[Gal(a1-6)]Man(b1-4)ManMan(b1-4)[Gal(a1-6)]Man(b1-4)[Gal(a1-6)]Man(b1-4)[Gal(a1-6)]Man(b1-4)[Gal(a1-6)]ManMan(b1-6)GlcNeu5Ac(a2-6)Gal(b1-4)GlcNAc(b1-2)Man(a1-3)[Neu5Ac(a2-6)Gal(b1-4)GlcNAc(b1-2)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-6)]GlcNAcNeu5Ac(a2-6)Gal(b1-4)GlcNAc(b1-2)[Neu5Ac(a2-3)Gal(b1-4)GlcNAc(b1-4)]Man(a1-3)[Neu5Ac(a2-6)Gal(b1-4)GlcNAc(b1-2)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcRha(a1-2)AraRha(a1-2)Ara(a1-2)GlcARha(a1-2)Ara(a1-2)GlcA6MeRha(a1-2)Ara(a1-2)GlcAOMeRha(a1-2)D-Ara(b1-2)GlcARha(a1-2)Gal(b1-2)GlcRha(a1-2)Gal(b1-2)GlcARha(a1-2)Gal(b1-2)GlcA6MeRha(a1-2)Gal(b1-2)GlcAOMeRha(a1-2)Glc(b1-2)GlcRha(a1-2)Glc(b1-2)GlcARha(a1-2)Glc(b1-2)GlcA6MeRha(a1-2)Glc(b1-2)GlcAOMeRha(a1-2)Glc(b1-6)GlcRha(a1-2)GlcA(b1-2)GlcARha(a1-2)GlcAOMe(b1-2)GlcAOMeRha(a1-2)Rha(a1-2)Gal(b1-4)[Glc(b1-2)]GlcARha(a1-2)XylRha(a1-2)Xyl(b1-2)GlcARha(a1-2)Xyl(b1-2)GlcA6MeRha(a1-2)Xyl(b1-2)GlcAOMeRha(a1-2)Xyl3AcRha(a1-2)Xyl4AcRha(a1-2)[Glc(b1-3)]GlcRha(a1-2)[Glc(b1-6)]Gal(b1-2)GlcA6MeRha(a1-2)[Rha(a1-4)]GlcRha(a1-2)[Rha(a1-6)]GalRha(a1-2)[Rha(a1-6)]GlcRha(a1-2)[Xyl(b1-4)]GlcRha(a1-2)[Xyl(b1-4)]Glc(b1-6)GlcRha(a1-3)GlcARha(a1-4)Gal(b1-2)GlcARha(a1-4)Gal(b1-2)GlcAOMeRha(a1-4)Gal(b1-2)GlcOMeRha(a1-4)Gal(b1-4)Gal(b1-4)GalGroRha(a1-4)Xyl(b1-2)GlcRha(a1-4)Xyl(b1-2)GlcARha(a1-4)Xyl(b1-2)GlcAOMeRha(a1-6)[Xyl(b1-3)Xyl(b1-2)]Glc(b1-2)GlcRha(b1-2)Glc(b1-2)GlcARha1Fer(a1-4)Fruf(b2-1)GlcOBzRhaOMe(a1-2)[RhaOMe(a1-6)]GlcOMe-olRhaOMe(a1-6)GlcOMe(b1-2)GlcOMe-olXyl(a1-6)Glc(b1-4)GlcXyl(a1-6)Glc(b1-4)Glc-olXyl(a1-6)Glc(b1-4)[Fuc(a1-2)Gal(b1-2)Xyl(a1-6)]Glc(b1-4)GlcXyl(a1-6)Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)GlcXyl(a1-6)Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc-olXyl(a1-6)Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Fuc(a1-2)Gal(b1-2)Xyl(a1-6)]Glc(b1-4)GlcXyl(a1-6)Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)GlcXyl(a1-6)Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)GlcXyl(a1-6)Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)GlcXyl(a1-6)Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)GlcXyl(a1-6)Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc-olXyl(a1-6)Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)GlcXyl(a1-6)Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)GlcXyl(a1-6)Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc-olXyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)GlcXyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc-olXyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Fuc(a1-2)Gal(b1-2)Xyl(a1-6)]Glc(b1-4)GlcXyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)GlcXyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)GlcXyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)GlcXyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)GlcXyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc-olXyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)GlcXyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)GlcXyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)GlcXyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)GlcXyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)GlcXyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc-olXyl(b1-2)Ara(a1-6)GlcXyl(b1-2)Ara(a1-6)GlcNAcXyl(b1-2)Ara(a1-6)[Glc(b1-2)]GlcXyl(b1-2)Ara(a1-6)[Glc(b1-4)]GlcNAcXyl(b1-2)D-Fuc(b1-6)GlcXyl(b1-2)D-Fuc(b1-6)GlcNAcXyl(b1-2)D-Fuc(b1-6)[Glc(b1-2)]GlcXyl(b1-2)Fuc(a1-6)GlcXyl(b1-2)Fuc(a1-6)GlcNAcXyl(b1-2)Gal(b1-2)GlcA6MeXyl(b1-2)Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcXyl(b1-2)Rha(a1-2)AraXyl(b1-2)[Glc(b1-3)]AraXyl(b1-2)[Man(a1-3)][Man(a1-3)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcXyl(b1-2)[Man(a1-3)][Man(a1-6)]Man(a1-3)Man(b1-4)GlcNAc(b1-4)GlcNAcXyl(b1-2)[Man(a1-3)][Man(a1-6)]Man(a1-3)Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcXyl(b1-2)[Man(a1-3)][Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcXyl(b1-2)[Man(a1-3)][Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcXyl(b1-2)[Man(a1-3)][Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcNXyl(b1-2)[Man(a1-3)][Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-6)]GlcNAcXyl(b1-2)[Man(a1-6)]Man(a1-3)Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcXyl(b1-2)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcXyl(b1-2)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcXyl(b1-2)[Rha(a1-3)]GlcAXyl(b1-3)AraXyl(b1-3)Xyl(b1-2)[Rha(a1-6)]Glc(b1-2)GlcXyl(b1-3)Xyl(b1-4)Rha(a1-2)[Rha(a1-6)]GlcXyl(b1-3)Xyl(b1-4)Rha(a1-2)[Rha(a1-6)]Glc(b1-2)GlcXyl(b1-4)Rha(a1-2)AraXyl(b1-4)Rha(a1-2)D-FucXyl(b1-4)Rha(a1-2)D-FucOMeXyl(b1-4)Rha(a1-2)[Rha(a1-6)]GlcXyl(b1-4)Xyl(b1-4)Xyl(b1-4)Xyl3Ac(b1-4)Xyl(b1-4)Xyl(b1-4)[GlcA(a1-2)]Xyl(b1-4)XylXyl(b1-4)Xyl(b1-4)Xyl(b1-4)Xyl3Ac(b1-4)Xyl(b1-4)Xyl(b1-4)[GlcA(a1-2)]Xyl3Ac(b1-4)XylXyl(b1-4)Xyl(b1-4)Xyl(b1-4)Xyl3Ac(b1-4)Xyl(b1-4)Xyl(b1-4)[GlcA4Me(a1-2)]Xyl(b1-4)XylXyl(b1-4)Xyl(b1-4)Xyl(b1-4)Xyl3Ac(b1-4)Xyl(b1-4)Xyl(b1-4)[GlcA4Me(a1-2)]Xyl3Ac(b1-4)XylXyl(b1-4)Xyl(b1-4)[GlcA(a1-2)]Xyl(b1-4)Xyl(b1-4)Xyl(b1-4)Xyl(b1-4)XylXyl(b1-4)[GlcAOMe(a1-2)]Xyl(b1-4)Xyl(b1-4)Xyl(b1-4)XylXyl2Ac3Ac4Ac(b1-3)AraXylOMe(b1-2)[RhaOMe(a1-6)]GlcOMe(b1-2)GlcOMe-olXylOMe(b1-3)XylOMe(b1-2)[RhaOMe(a1-6)]GlcOMe(b1-2)GlcOMe-olXylOMe(b1-4)RhaOMe(a1-2)D-FucOMeXylOMe(b1-4)RhaOMe(a1-2)[RhaOMe(a1-6)]GlcOMeXylOMe(b1-4)RhaOMe(a1-2)[RhaOMe(a1-6)]GlcOMe-olXylf(b1-2)Xyl(b1-3)[Rha(b1-2)Rha(b1-4)]Xyl[Araf(a1-3)Gal(b1-3)Gal(b1-6)]Gal(b1-3)Gal[Araf(a1-3)Gal(b1-6)]Gal(b1-3)Gal[Gal(a1-4)Gal(a1-6)]Man(b1-4)[Gal(a1-6)]Man(b1-4)[Man(b1-4)Man(b1-4)Man(b1-4)Gal(a1-6)]Man(b1-2)[Gal(a1-6)]Man(b1-2)[Gal(a1-4)Gal(a1-6)]Man(b1-4)Man[Gal(a1-6)]Man(b1-4)Man[Gal(a1-6)]Man(b1-4)Man(b1-4)Man[Gal(a1-6)]Man(b1-4)Man(b1-4)Man(b1-4)Man(b1-4)Man[Gal(a1-6)]Man(b1-4)[Gal(a1-6)]Man(b1-4)Man(b1-4)Man[Gal(a1-6)]Man(b1-4)[Gal(a1-6)]Man(b1-4)[Gal(a1-6)]Man(b1-4)Man[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Gal(b1-3)Gal(b1-6)[Araf(a1-3)]Gal(b1-6)]Gal(b1-3)Gal[Gal(b1-3)Gal(b1-6)]Gal(b1-3)Gal[Gal(b1-6)Gal(b1-6)Gal(b1-6)]Gal(b1-3)Gal[Gal(b1-6)Gal(b1-6)]Gal(b1-3)Gal[Gal(b1-6)]Gal(b1-3)Gal(b1-3)Gal(b1-3)Gal(b1-3)Gal(b1-3)Gal(b1-3)Gal[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Araf(a1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Araf(a1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Araf(a1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Araf(a1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-5)Araf(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-5)Araf(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Fuc(a1-2)Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-5)Araf(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-5)Araf(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc
Family
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
Fagaceae00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000011100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000Fagaceae00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000011100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
Polygalaceae00000010000000111000111111111111111111110000000000000000000000000000000000000000000000000000000000211221111200000111000000000000000000010000011100000111221111100111110011111001000100111111100000000000000000000000000000011000000000000000000000000000000000000000000000000000000000000000000000000001000000000000000000000000000010000000000000100000000000000000000000000000000000000000000000000000000000100000000000000100000000000000000000000000000Polygalaceae00000010000000111000111111111111111111110000000000000000000000000000000000000000000000000000000000211221111200000111000000000000000000010000011100000111221111100111110011111001000100111111100000000000000000000000000000011000000000000000000000000000000000000000000000000000000000000000000000000001000000000000000000000000000010000000000000100000000000000000000000000000000000000000000000000000000000100000000000000100000000000000000000000000000
Quillajaceae00000000000011000011000000000000000000000000000000000000000000000000001000000000000000000000000000000000000000001000000000000000000000000000000000000000000000001000000000000000000000000000000000000000000000000000000000100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000110000000000100000000000000000000000000000000Quillajaceae00000000000011000011000000000000000000000000000000000000000000000000001000000000000000000000000000000000000000001000000000000000000000000000000000000000000000001000000000000000000000000000000000000000000000000000000000100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000110000000000100000000000000000000000000000000
\n" @@ -8656,16 +8624,15 @@ "\n", "### get_possible_linkages\n", "\n", - "> get_possible_linkages (wildcard, linkage_list={'1-6', 'a1-2', 'b1-1',\n", - "> '?2-6', 'a1-6', 'a1-8', 'a2-11', '1-4', 'b1-3',\n", - "> 'b1-?', '?1-4', 'a1-7', 'a2-2', '?2-8', 'a2-3',\n", - "> 'a1-5', 'b2-7', 'a1-3', 'a2-6', 'a2-9', 'b1-8',\n", - "> 'b1-9', 'b1-4', 'b1-5', 'b2-2', 'a2-?', 'b2-4',\n", - "> 'b1-6', 'b2-5', 'b2-6', '?1-3', 'a2-8', 'a2-7',\n", - "> 'b2-8', 'b2-3', 'a1-9', 'a2-1', 'a2-5', '?1-6',\n", - "> 'a1-4', 'a1-?', 'b1-7', 'a1-1', 'a1-11', '?1-?',\n", - "> '?2-?', 'b1-2', 'b2-1', '?1-2', '?2-3', 'a2-4'},\n", - "> libr=None)\n", + "> get_possible_linkages (wildcard, linkage_list={'?2-3', 'a1-3', 'a1-5',\n", + "> 'b1-2', 'a1-1', 'b1-?', '?1-6', 'b2-1', 'a1-4',\n", + "> 'b1-6', '?1-3', 'a2-9', 'b1-8', 'b1-3', 'b1-5',\n", + "> '?2-6', 'a2-11', 'a2-2', '?1-4', '?1-?', 'a1-6',\n", + "> 'a2-8', 'b1-4', '1-4', 'a1-2', 'b1-7', 'a1-9',\n", + "> 'b2-4', 'a1-?', 'b2-6', 'a2-5', 'a1-8', 'a2-7',\n", + "> 'b2-7', 'a1-11', '?2-?', 'b2-2', 'b2-5', '?1-2',\n", + "> 'b1-9', '1-6', '?2-8', 'a2-?', 'a2-1', 'b2-3',\n", + "> 'a2-3', 'a2-4', 'b1-1', 'a2-6', 'a1-7', 'b2-8'})\n", "\n", "Retrieves all linkages that match a given wildcard pattern from a list of linkages\n", "\n", @@ -8673,7 +8640,6 @@ "| :-\n", "| wildcard (string): The pattern to match, where '?' can be used as a wildcard for any single character.\n", "| linkage_list (list): List of linkages as strings to search within; default:linkages\n", - "| libr (dict): dictionary of form glycoletter:index\n", "\n", "| Returns:\n", "| :-\n", @@ -8684,16 +8650,15 @@ "\n", "### get_possible_linkages\n", "\n", - "> get_possible_linkages (wildcard, linkage_list={'1-6', 'a1-2', 'b1-1',\n", - "> '?2-6', 'a1-6', 'a1-8', 'a2-11', '1-4', 'b1-3',\n", - "> 'b1-?', '?1-4', 'a1-7', 'a2-2', '?2-8', 'a2-3',\n", - "> 'a1-5', 'b2-7', 'a1-3', 'a2-6', 'a2-9', 'b1-8',\n", - "> 'b1-9', 'b1-4', 'b1-5', 'b2-2', 'a2-?', 'b2-4',\n", - "> 'b1-6', 'b2-5', 'b2-6', '?1-3', 'a2-8', 'a2-7',\n", - "> 'b2-8', 'b2-3', 'a1-9', 'a2-1', 'a2-5', '?1-6',\n", - "> 'a1-4', 'a1-?', 'b1-7', 'a1-1', 'a1-11', '?1-?',\n", - "> '?2-?', 'b1-2', 'b2-1', '?1-2', '?2-3', 'a2-4'},\n", - "> libr=None)\n", + "> get_possible_linkages (wildcard, linkage_list={'?2-3', 'a1-3', 'a1-5',\n", + "> 'b1-2', 'a1-1', 'b1-?', '?1-6', 'b2-1', 'a1-4',\n", + "> 'b1-6', '?1-3', 'a2-9', 'b1-8', 'b1-3', 'b1-5',\n", + "> '?2-6', 'a2-11', 'a2-2', '?1-4', '?1-?', 'a1-6',\n", + "> 'a2-8', 'b1-4', '1-4', 'a1-2', 'b1-7', 'a1-9',\n", + "> 'b2-4', 'a1-?', 'b2-6', 'a2-5', 'a1-8', 'a2-7',\n", + "> 'b2-7', 'a1-11', '?2-?', 'b2-2', 'b2-5', '?1-2',\n", + "> 'b1-9', '1-6', '?2-8', 'a2-?', 'a2-1', 'b2-3',\n", + "> 'a2-3', 'a2-4', 'b1-1', 'a2-6', 'a1-7', 'b2-8'})\n", "\n", "Retrieves all linkages that match a given wildcard pattern from a list of linkages\n", "\n", @@ -8701,7 +8666,6 @@ "| :-\n", "| wildcard (string): The pattern to match, where '?' can be used as a wildcard for any single character.\n", "| linkage_list (list): List of linkages as strings to search within; default:linkages\n", - "| libr (dict): dictionary of form glycoletter:index\n", "\n", "| Returns:\n", "| :-\n", @@ -8726,16 +8690,16 @@ { "data": { "text/plain": [ - "['a1-2',\n", - " 'a1-6',\n", - " 'a1-8',\n", - " 'a1-7',\n", + "['a1-3',\n", " 'a1-5',\n", - " 'a1-3',\n", - " 'a1-9',\n", + " 'a1-1',\n", " 'a1-4',\n", + " 'a1-6',\n", + " 'a1-2',\n", + " 'a1-9',\n", " 'a1-?',\n", - " 'a1-1']" + " 'a1-8',\n", + " 'a1-7']" ] }, "execution_count": null, @@ -8760,14 +8724,13 @@ "\n", "### get_possible_monosaccharides\n", "\n", - "> get_possible_monosaccharides (wildcard, libr=None)\n", + "> get_possible_monosaccharides (wildcard)\n", "\n", "Retrieves all matching common monosaccharides of a type, given the type\n", "\n", "| Arguments:\n", "| :-\n", "| wildcard (string): Monosaccharide type, from \"HexNAc\", \"Hex\", \"dHex\", \"Sia\", \"HexA\", \"Pen\"\n", - "| libr (dict): dictionary of form glycoletter:index\n", "\n", "| Returns:\n", "| :-\n", @@ -8778,14 +8741,13 @@ "\n", "### get_possible_monosaccharides\n", "\n", - "> get_possible_monosaccharides (wildcard, libr=None)\n", + "> get_possible_monosaccharides (wildcard)\n", "\n", "Retrieves all matching common monosaccharides of a type, given the type\n", "\n", "| Arguments:\n", "| :-\n", "| wildcard (string): Monosaccharide type, from \"HexNAc\", \"Hex\", \"dHex\", \"Sia\", \"HexA\", \"Pen\"\n", - "| libr (dict): dictionary of form glycoletter:index\n", "\n", "| Returns:\n", "| :-\n", @@ -8810,7 +8772,7 @@ { "data": { "text/plain": [ - "['HexNAc', 'ManNAc', 'GlcNAc', 'GalNAc']" + "{'GalNAc', 'GlcNAc', 'HexNAc', 'ManNAc'}" ] }, "execution_count": null, @@ -8919,14 +8881,13 @@ "\n", "### get_insight\n", "\n", - "> get_insight (glycan, libr=None, motifs=None)\n", + "> get_insight (glycan, motifs=None)\n", "\n", "prints out meta-information about a glycan\n", "\n", "| Arguments:\n", "| :-\n", "| glycan (string): glycan in IUPAC-condensed format\n", - "| libr (dict): dictionary of form glycoletter:index\n", "| motifs (dataframe): dataframe of glycan motifs (name + sequence); default:motif_list" ], "text/plain": [ @@ -8934,14 +8895,13 @@ "\n", "### get_insight\n", "\n", - "> get_insight (glycan, libr=None, motifs=None)\n", + "> get_insight (glycan, motifs=None)\n", "\n", "prints out meta-information about a glycan\n", "\n", "| Arguments:\n", "| :-\n", "| glycan (string): glycan in IUPAC-condensed format\n", - "| libr (dict): dictionary of form glycoletter:index\n", "| motifs (dataframe): dataframe of glycan motifs (name + sequence); default:motif_list" ] }, @@ -9085,7 +9045,7 @@ "\n", "### get_match\n", "\n", - "> get_match (pattern, glycan, libr=None, return_matches=True)\n", + "> get_match (pattern, glycan, return_matches=True)\n", "\n", "finds matches for a glyco-regular expression in a glycan\n", "\n", @@ -9093,7 +9053,6 @@ "| :-\n", "| pattern (string): glyco-regular expression in the form of \"Hex-HexNAc-([Hex|Fuc]){1,2}-HexNAc\"; accepts pre-compiled pattern\n", "| glycan (string): glycan sequence in IUPAC-condensed\n", - "| libr (dict): dictionary of form glycoletter:index; default:glycowork-internal libr\n", "| return_matches (bool): whether to return True/False or return the matches as a list of strings; default:True\n", "\n", "| Returns:\n", @@ -9105,7 +9064,7 @@ "\n", "### get_match\n", "\n", - "> get_match (pattern, glycan, libr=None, return_matches=True)\n", + "> get_match (pattern, glycan, return_matches=True)\n", "\n", "finds matches for a glyco-regular expression in a glycan\n", "\n", @@ -9113,7 +9072,6 @@ "| :-\n", "| pattern (string): glyco-regular expression in the form of \"Hex-HexNAc-([Hex|Fuc]){1,2}-HexNAc\"; accepts pre-compiled pattern\n", "| glycan (string): glycan sequence in IUPAC-condensed\n", - "| libr (dict): dictionary of form glycoletter:index; default:glycowork-internal libr\n", "| return_matches (bool): whether to return True/False or return the matches as a list of strings; default:True\n", "\n", "| Returns:\n", @@ -9262,7 +9220,7 @@ { "data": { "text/plain": [ - "[1780, 2313, 1780, 2316, 1780]" + "[None, None, None, None, None]" ] }, "execution_count": null, @@ -9341,7 +9299,7 @@ { "data": { "text/plain": [ - "[1780, 2313, 1780, 2316, 1780, 2410, 2410]" + "[None, None, None, None, None, 25, 25]" ] }, "execution_count": null, @@ -9762,14 +9720,13 @@ "\n", "### condense_composition_matching\n", "\n", - "> condense_composition_matching (matched_composition, libr=None)\n", + "> condense_composition_matching (matched_composition)\n", "\n", "Given a list of glycans matching a composition, find the minimum number of glycans characterizing this set\n", "\n", "| Arguments:\n", "| :-\n", "| matched_composition (list): list of glycans matching to a composition\n", - "| libr (dict): dictionary of form glycoletter:index; default:lib\n", "\n", "| Returns:\n", "| :-\n", @@ -9780,14 +9737,13 @@ "\n", "### condense_composition_matching\n", "\n", - "> condense_composition_matching (matched_composition, libr=None)\n", + "> condense_composition_matching (matched_composition)\n", "\n", "Given a list of glycans matching a composition, find the minimum number of glycans characterizing this set\n", "\n", "| Arguments:\n", "| :-\n", "| matched_composition (list): list of glycans matching to a composition\n", - "| libr (dict): dictionary of form glycoletter:index; default:lib\n", "\n", "| Returns:\n", "| :-\n", @@ -9859,7 +9815,7 @@ "> abundances=None, mode='negative',\n", "> mass_value='monoisotopic', sample_prep='underivatized',\n", "> mass_tolerance=0.5, reduced=False, df_use=None,\n", - "> filter_out=set(), libr=None, verbose=False)\n", + "> filter_out=set(), verbose=False)\n", "\n", "wrapper function to map precursor masses to structures, condense them, and match them with relative intensities\n", "\n", @@ -9876,7 +9832,6 @@ "| reduced (bool): whether glycans are reduced at reducing end; default:False\n", "| df_use (dataframe): species-specific glycan dataframe to use for mapping; default: df_glycan\n", "| filter_out (set): set of monosaccharide types to ignore during composition finding; default:None\n", - "| libr (dict): dictionary of form glycoletter:index; default:lib\n", "| verbose (bool): whether to print any non-matching compositions; default:False\n", "\n", "| Returns:\n", @@ -9892,7 +9847,7 @@ "> abundances=None, mode='negative',\n", "> mass_value='monoisotopic', sample_prep='underivatized',\n", "> mass_tolerance=0.5, reduced=False, df_use=None,\n", - "> filter_out=set(), libr=None, verbose=False)\n", + "> filter_out=set(), verbose=False)\n", "\n", "wrapper function to map precursor masses to structures, condense them, and match them with relative intensities\n", "\n", @@ -9909,7 +9864,6 @@ "| reduced (bool): whether glycans are reduced at reducing end; default:False\n", "| df_use (dataframe): species-specific glycan dataframe to use for mapping; default: df_glycan\n", "| filter_out (set): set of monosaccharide types to ignore during composition finding; default:None\n", - "| libr (dict): dictionary of form glycoletter:index; default:lib\n", "| verbose (bool): whether to print any non-matching compositions; default:False\n", "\n", "| Returns:\n", @@ -10057,7 +10011,7 @@ "\n", "> compositions_to_structures (composition_list, glycan_class='N',\n", "> kingdom='Animalia', abundances=None,\n", - "> df_use=None, libr=None, verbose=False)\n", + "> df_use=None, verbose=False)\n", "\n", "wrapper function to map compositions to structures, condense them, and match them with relative intensities\n", "\n", @@ -10068,7 +10022,6 @@ "| kingdom (string): taxonomic kingdom for choosing a subset of glycans to consider; default:'Animalia'\n", "| abundances (dataframe): every row one composition (matching composition_list in order), every column one sample;default:pd.DataFrame([range(len(composition_list))]*2).T\n", "| df_use (dataframe): glycan dataframe for searching glycan structures; default:df_glycan\n", - "| libr (dict): dictionary of form glycoletter:index; default:lib\n", "| verbose (bool): whether to print any non-matching compositions; default:False\n", "\n", "| Returns:\n", @@ -10082,7 +10035,7 @@ "\n", "> compositions_to_structures (composition_list, glycan_class='N',\n", "> kingdom='Animalia', abundances=None,\n", - "> df_use=None, libr=None, verbose=False)\n", + "> df_use=None, verbose=False)\n", "\n", "wrapper function to map compositions to structures, condense them, and match them with relative intensities\n", "\n", @@ -10093,7 +10046,6 @@ "| kingdom (string): taxonomic kingdom for choosing a subset of glycans to consider; default:'Animalia'\n", "| abundances (dataframe): every row one composition (matching composition_list in order), every column one sample;default:pd.DataFrame([range(len(composition_list))]*2).T\n", "| df_use (dataframe): glycan dataframe for searching glycan structures; default:df_glycan\n", - "| libr (dict): dictionary of form glycoletter:index; default:lib\n", "| verbose (bool): whether to print any non-matching compositions; default:False\n", "\n", "| Returns:\n", @@ -10283,14 +10235,13 @@ "\n", "### structure_to_basic\n", "\n", - "> structure_to_basic (glycan, libr=None)\n", + "> structure_to_basic (glycan)\n", "\n", "converts a monosaccharide- and linkage-defined glycan structure to the base topology\n", "\n", "| Arguments:\n", "| :-\n", "| glycan (string): glycan in IUPAC-condensed nomenclature\n", - "| libr (dict): dictionary of form glycoletter:index\n", "\n", "| Returns:\n", "| :-\n", @@ -10301,14 +10252,13 @@ "\n", "### structure_to_basic\n", "\n", - "> structure_to_basic (glycan, libr=None)\n", + "> structure_to_basic (glycan)\n", "\n", "converts a monosaccharide- and linkage-defined glycan structure to the base topology\n", "\n", "| Arguments:\n", "| :-\n", "| glycan (string): glycan in IUPAC-condensed nomenclature\n", - "| libr (dict): dictionary of form glycoletter:index\n", "\n", "| Returns:\n", "| :-\n", @@ -10580,6 +10530,95 @@ "composition_to_mass({'Neu5Ac': 2, 'Hex': 1, 'HexNAc': 1, 'S': 1})" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "d77997e2-1538-483a-ba67-73cac530c517", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "---\n", + "\n", + "### get_unique_topologies\n", + "\n", + "> get_unique_topologies (composition, glycan_type, df_use=None,\n", + "> universal_replacers={}, taxonomy_rank='Kingdom',\n", + "> taxonomy_value='Animalia')\n", + "\n", + "given a composition, retrieves all observed and unique base topologies\n", + "\n", + "| Arguments:\n", + "| :-\n", + "| composition (dict): composition in form monosaccharide:count\n", + "| glycan_type (string): which glycan class to search, 'N', 'O', 'lipid', 'free', or 'repeat'\n", + "| df_use (dataframe): species-specific glycan dataframe to use for mapping; default: df_glycan\n", + "| universal_replacers (dictionary): dictionary of form base monosaccharide : specific monosaccharide\n", + "| taxonomy_rank (string): at which taxonomic rank to filter; default: Kingdom\n", + "| taxonomy_value (string): which value to filter at taxonomy_rank; default: Animalia\n", + "\n", + "| Returns:\n", + "| :-\n", + "| Returns a list of observed base topologies for the given composition" + ], + "text/plain": [ + "---\n", + "\n", + "### get_unique_topologies\n", + "\n", + "> get_unique_topologies (composition, glycan_type, df_use=None,\n", + "> universal_replacers={}, taxonomy_rank='Kingdom',\n", + "> taxonomy_value='Animalia')\n", + "\n", + "given a composition, retrieves all observed and unique base topologies\n", + "\n", + "| Arguments:\n", + "| :-\n", + "| composition (dict): composition in form monosaccharide:count\n", + "| glycan_type (string): which glycan class to search, 'N', 'O', 'lipid', 'free', or 'repeat'\n", + "| df_use (dataframe): species-specific glycan dataframe to use for mapping; default: df_glycan\n", + "| universal_replacers (dictionary): dictionary of form base monosaccharide : specific monosaccharide\n", + "| taxonomy_rank (string): at which taxonomic rank to filter; default: Kingdom\n", + "| taxonomy_value (string): which value to filter at taxonomy_rank; default: Animalia\n", + "\n", + "| Returns:\n", + "| :-\n", + "| Returns a list of observed base topologies for the given composition" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "show_doc(get_unique_topologies)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fceba897-6089-406d-9826-d4a0696edc94", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['HexNAc(?1-?)Hex(?1-?)HexNAc',\n", + " 'Hex(?1-?)HexNAc(?1-?)HexNAc',\n", + " 'Hex(?1-?)[HexNAc(?1-?)]HexNAc']" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "get_unique_topologies({'HexNAc':2, 'Hex':1}, 'O', universal_replacers = {'dHex':'Fuc'})" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/build/lib/glycowork/motif/tokenization.py b/build/lib/glycowork/motif/tokenization.py index e66d0cf1..e103e2d9 100644 --- a/build/lib/glycowork/motif/tokenization.py +++ b/build/lib/glycowork/motif/tokenization.py @@ -603,3 +603,28 @@ def glycan_to_mass(glycan, mass_value = 'monoisotopic', sample_prep = 'underivat stem_libr = stem_lib comp = glycan_to_composition(glycan, stem_libr = stem_libr) return composition_to_mass(comp, mass_value = mass_value, sample_prep = sample_prep) + + +@rescue_compositions +def get_unique_topologies(composition, glycan_type, df_use = None, universal_replacers = {}, + taxonomy_rank = "Kingdom", taxonomy_value = "Animalia"): + """given a composition, retrieves all observed and unique base topologies\n + | Arguments: + | :- + | composition (dict): composition in form monosaccharide:count + | glycan_type (string): which glycan class to search, 'N', 'O', 'lipid', 'free', or 'repeat' + | df_use (dataframe): species-specific glycan dataframe to use for mapping; default: df_glycan + | universal_replacers (dictionary): dictionary of form base monosaccharide : specific monosaccharide + | taxonomy_rank (string): at which taxonomic rank to filter; default: Kingdom + | taxonomy_value (string): which value to filter at taxonomy_rank; default: Animalia\n + | Returns: + | :- + | Returns a list of observed base topologies for the given composition + """ + if df_use is None: + df_use = df_glycan + df_use = df_use[df_use.Composition == composition] + df_use = df_use[df_use.glycan_type == glycan_type] + df_use = df_use[df_use[taxonomy_rank].apply(lambda x: taxonomy_value in x)].glycan.values + df_use = list(set([structure_to_basic(k) for k in df_use])) + return [[g.replace(k, v) for k,v in universal_replacers.items()][0] for g in df_use if '{' not in g] diff --git a/glycowork.egg-info/PKG-INFO b/glycowork.egg-info/PKG-INFO index 6736b877..b399f885 100644 --- a/glycowork.egg-info/PKG-INFO +++ b/glycowork.egg-info/PKG-INFO @@ -196,14 +196,14 @@ from glycowork.motif.annotate import annotate_dataset out = annotate_dataset(glycans, feature_set = ['known', 'terminal', 'exhaustive']) ``` -| | Terminal_LewisX | Internal_LewisX | LewisY | SialylLewisX | SulfoSialylLewisX | Terminal_LewisA | Internal_LewisA | LewisB | SialylLewisA | SulfoLewisA | H_type2 | H_type1 | A_antigen | B_antigen | Galili_antigen | GloboH | Gb5 | Gb4 | Gb3 | 3SGb3 | 8DSGb3 | 3SGb4 | 8DSGb4 | 6DSGb4 | 3SGb5 | 8DSGb5 | 6DSGb5 | 6DSGb5_2 | 6SGb3 | 8DSGb3_2 | 6SGb4 | 8DSGb4_2 | 6SGb5 | 8DSGb5_2 | 66DSGb5 | Forssman_antigen | iGb3 | I_antigen | i_antigen | PI_antigen | Chitobiose | Trimannosylcore | Internal_LacNAc_type1 | Terminal_LacNAc_type1 | Internal_LacNAc_type2 | Terminal_LacNAc_type2 | Internal_LacdiNAc_type1 | Terminal_LacdiNAc_type1 | Internal_LacdiNAc_type2 | Terminal_LacdiNAc_type2 | bisectingGlcNAc | VIM | PolyLacNAc | Ganglio_Series | Lacto_Series(LewisC) | NeoLacto_Series | betaGlucan | KeratanSulfate | Hyluronan | Mollu_series | Arthro_series | Cellulose_like | Chondroitin_4S | GPI_anchor | Isoglobo_series | LewisD | Globo_series | Sda | SDA | Muco_series | Heparin | Peptidoglycan | Dermatansulfate | CAD | Lactosylceramide | Lactotriaosylceramide | LexLex | GM3 | H_type3 | GM2 | GM1 | cisGM1 | VIM2 | GD3 | GD1a | GD2 | GD1b | SDLex | Nglycolyl_GM2 | Fuc_LN3 | GT1b | GD1 | GD1a_2 | LcGg4 | GT3 | Disialyl_T_antigen | GT1a | GT2 | GT1c | 2Fuc_GM1 | GQ1c | O_linked_mannose | GT1aa | GQ1b | HNK1 | GQ1ba | O_mannose_Lex | 2Fuc_GD1b | Sialopentaosylceramide | Sulfogangliotetraosylceramide | B-GM1 | GQ1aa | bisSulfo-Lewis x | para-Forssman | core_fucose | core_fucose(a1-3) | GP1c | B-GD1b | GP1ca | Isoglobotetraosylceramide | polySia | high_mannose | Gala_series | LPS_core | Nglycan_complex | Nglycan_complex2 | Oglycan_core1 | Oglycan_core2 | Oglycan_core3 | Oglycan_core4 | Oglycan_core5 | Oglycan_core6 | Oglycan_core7 | Xylogalacturonan | Sialosylparagloboside | LDNF | OFuc | Arabinogalactan_type2 | EGF_repeat | Nglycan_hybrid | Arabinan | Xyloglucan | Acharan_Sulfate | M3FX | M3X | 1-6betaGalactan | Arabinogalactan_type1 | Galactomannan | Tetraantennary_Nglycan | Mucin_elongated_core2 | Fucoidan | Alginate | FG | XX | Difucosylated_core | GalFuc_core | Fuc | Gal | GalNAc | GalNAcOS | GlcNAc | Man | Neu5Ac | Xyl | Neu5Ac(a2-3)Gal | Gal(b1-4)GlcNAc | Fuc(a1-3)GlcNAc | GlcNAc(b1-2)Man | Man(a1-3)Man | Gal(b1-3)GlcNAc | Fuc(a1-4)GlcNAc | Man(a1-6)Man | Man(b1-4)GlcNAc | GlcNAc(b1-4)GlcNAc | Fuc(a1-6)GlcNAc | Xyl(b1-2)Man | Fuc(a1-2)Gal | GalNAcOS(b1-4)GlcNAc | GalNAc(b1-4)GlcNAc | Gal(b1-?)GlcNAc | Fuc(a1-?)GlcNAc | GlcNAc(b1-?)Man | Man(a1-?)Man | Fuc(a1-6) | Gal(b1-3) | Man(a1-6) | Gal(b1-4) | GalNAc(b1-4) | Fuc(a1-4) | GalNAcOS(b1-4) | Neu5Ac(a2-3) | Fuc(a1-2) | Xyl(b1-2) | Fuc(a1-3) | Man(a1-3) | Fuc(a1-?) | Man(a1-?) | Gal(b1-?) | GlcNAc(b1-?) | -|------------------------------------------------------------------------------------------------------------------------------------------------|-----------------|-----------------|--------|--------------|-------------------|-----------------|-----------------|--------|--------------|-------------|---------|---------|-----------|-----------|----------------|--------|-----|-----|-----|-------|--------|-------|--------|--------|-------|--------|--------|----------|-------|----------|-------|----------|-------|----------|---------|------------------|------|-----------|-----------|------------|------------|-----------------|-----------------------|-----------------------|-----------------------|-----------------------|-------------------------|-------------------------|-------------------------|-------------------------|-----------------|-----|------------|----------------|----------------------|-----------------|------------|----------------|-----------|--------------|---------------|----------------|----------------|------------|-----------------|--------|--------------|-----|-----|-------------|---------|---------------|-----------------|-----|------------------|-----------------------|--------|-----|---------|-----|-----|--------|------|-----|------|-----|------|-------|---------------|---------|------|-----|--------|-------|-----|--------------------|------|-----|------|----------|------|------------------|-------|------|------|-------|---------------|-----------|------------------------|-------------------------------|-------|-------|------------------|---------------|-------------|-------------------|------|--------|-------|---------------------------|---------|--------------|-------------|----------|-----------------|------------------|---------------|---------------|---------------|---------------|---------------|---------------|---------------|------------------|-----------------------|------|------|-----------------------|------------|----------------|----------|------------|-----------------|------|-----|-----------------|-----------------------|---------------|------------------------|-----------------------|----------|----------|-----|-----|--------------------|-------------|-----|-----|--------|----------|--------|-----|--------|-----|-----------------|-----------------|-----------------|-----------------|--------------|-----------------|-----------------|--------------|-----------------|--------------------|-----------------|--------------|--------------|----------------------|--------------------|-----------------|-----------------|-----------------|--------------|-----------|-----------|-----------|-----------|--------------|-----------|----------------|--------------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|--------------| -| Neu5Ac(a2-3)Gal(b1-4)\[Fuc(a1-3)\]GlcNAc(b1-2)Man(a1-3)\[Gal(b1-3)\[Fuc(a1-4)\]GlcNAc(b1-2)Man(a1-6)\]Man(b1-4)GlcNAc(b1-4)\[Fuc(a1-6)\]GlcNAc | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 2 | 0 | 0 | 4 | 3 | 1 | 0 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 2 | 3 | 2 | 2 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 3 | 0 | 1 | 0 | -| Man(a1-3)\[Man(a1-6)\]Man(b1-4)GlcNAc(b1-4)GlcNAc | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 0 | 0 | -| Man(a1-3)\[Man(a1-6)\]Man(b1-4)GlcNAc(b1-4)GlcNAc | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 0 | 0 | -| GlcNAc(b1-?)Man(a1-3)\[GlcNAc(b1-?)Man(a1-6)\]\[Xyl(b1-2)\]Man(b1-4)GlcNAc(b1-4)\[Fuc(a1-3)\]GlcNAc | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 4 | 3 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 2 | -| Fuc(a1-2)Gal(b1-4)GlcNAc(b1-2)Man(a1-6)\[Gal(b1-4)GlcNAc(b1-2)Man(a1-3)\]Man(b1-4)GlcNAc(b1-4)\[Fuc(a1-6)\]GlcNAc | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 0 | 0 | 4 | 3 | 0 | 0 | 0 | 2 | 0 | 2 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 2 | 1 | 2 | 2 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 2 | 0 | 1 | 0 | -| GalNAcOS(b1-4)GlcNAc(b1-2)Man(a1-3)\[GalNAc(b1-4)GlcNAc(b1-2)Man(a1-6)\]Man(b1-4)GlcNAc(b1-4)GlcNAc | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 4 | 3 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 2 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | +| | Terminal_LewisX | Internal_LewisX | LewisY | SialylLewisX | SulfoSialylLewisX | Terminal_LewisA | Internal_LewisA | LewisB | SialylLewisA | SulfoLewisA | H_type2 | H_type1 | A_antigen | B_antigen | Galili_antigen | GloboH | Gb5 | Gb4 | Gb3 | 3SGb3 | 8DSGb3 | 3SGb4 | 8DSGb4 | 6DSGb4 | 3SGb5 | 8DSGb5 | 6DSGb5 | 6DSGb5_2 | 6SGb3 | 8DSGb3_2 | 6SGb4 | 8DSGb4_2 | 6SGb5 | 8DSGb5_2 | 66DSGb5 | Forssman_antigen | iGb3 | I_antigen | i_antigen | PI_antigen | Chitobiose | Trimannosylcore | Internal_LacNAc_type1 | Terminal_LacNAc_type1 | Internal_LacNAc_type2 | Terminal_LacNAc_type2 | Internal_LacdiNAc_type1 | Terminal_LacdiNAc_type1 | Internal_LacdiNAc_type2 | Terminal_LacdiNAc_type2 | bisectingGlcNAc | VIM | PolyLacNAc | Ganglio_Series | Lacto_Series(LewisC) | NeoLacto_Series | betaGlucan | KeratanSulfate | Hyluronan | Mollu_series | Arthro_series | Cellulose_like | Chondroitin_4S | GPI_anchor | Isoglobo_series | LewisD | Globo_series | Sda | SDA | Muco_series | Heparin | Peptidoglycan | Dermatansulfate | CAD | Lactosylceramide | Lactotriaosylceramide | LexLex | GM3 | H_type3 | GM2 | GM1 | cisGM1 | VIM2 | GD3 | GD1a | GD2 | GD1b | SDLex | Nglycolyl_GM2 | Fuc_LN3 | GT1b | GD1 | GD1a_2 | LcGg4 | GT3 | Disialyl_T_antigen | GT1a | GT2 | GT1c | 2Fuc_GM1 | GQ1c | O_linked_mannose | GT1aa | GQ1b | HNK1 | GQ1ba | O_mannose_Lex | 2Fuc_GD1b | Sialopentaosylceramide | Sulfogangliotetraosylceramide | B-GM1 | GQ1aa | bisSulfo-Lewis x | para-Forssman | core_fucose | core_fucose(a1-3) | GP1c | B-GD1b | GP1ca | Isoglobotetraosylceramide | polySia | high_mannose | Gala_series | LPS_core | Nglycan_complex | Nglycan_complex2 | Oglycan_core1 | Oglycan_core2 | Oglycan_core3 | Oglycan_core4 | Oglycan_core5 | Oglycan_core6 | Oglycan_core7 | Xylogalacturonan | Sialosylparagloboside | LDNF | OFuc | Arabinogalactan_type2 | EGF_repeat | Nglycan_hybrid | Arabinan | Xyloglucan | Acharan_Sulfate | M3FX | M3X | 1-6betaGalactan | Arabinogalactan_type1 | Galactomannan | Tetraantennary_Nglycan | Mucin_elongated_core2 | Fucoidan | Alginate | FG | XX | Difucosylated_core | GalFuc_core | -1+1 | -β-D-GlcpNAc- | -β-D-Manp- | 10n 10:9d | 11n 11:5o | 12d 12:12o | 13d 13:13d | 14n 14:13o | 15d 15:15d | 16n | 1→ | 1→3 | 1→4 | 1→6 | 2+1 | 2n 2:1o | 3 | 3+1 | 3d 3:3d | 4+1 | 4n 4:3o | 5d 5:5o | 6+1 | 6d 6:6o | 7d 7:7d | 8n 8:7o | 9d 9:9o | F | Fuc | Gal | GlcNAc | Ma3 | Ma6 | Man | Mb4GNb4GN;N | Neu5Ac | RES 1b:b-dglc-HEX-1:5 2s:n-acetyl 3b:b-dglc-HEX-1:5 4s:n-acetyl 5b:b-dman-HEX-1:5 6b:a-dman-HEX-1:5 7b:b-dglc-HEX-1:5 8s:n-acetyl 9b:b-dgal-HEX-1:5 10s:sulfate 11s:n-acetyl 12b:a-dman-HEX-1:5 13b:b-dglc-HEX-1:5 14s:n-acetyl 15b:b-dgal-HEX-1:5 16s:n-acetyl LIN 1:1d | WURCS=2.0/5,11,10/a2122h-1b_1-5_2\*NCC/3=Oa1122h-1b_1-5a1122h-1a_1-5a2112h-1b_1-5a1221m-1a_1-5/1-1-2-3-1-4-3-1-4-5-5/a4-b1_a6-k1_b4-c1_c3-d1_c6-g1_d2-e1_e4-f1_g2-h1_h4-i1_i2-j1 | XA2 | α-D-Manp- | Neu5Ac(a2-3)Gal | Gal(b1-4)GlcNAc | Fuc(a1-3)GlcNAc | GlcNAc(b1-2)Man | Man(a1-3)Man | Gal(b1-3)GlcNAc | Fuc(a1-4)GlcNAc | Man(a1-6)Man | Man(b1-4)GlcNAc | GlcNAc(b1-4)GlcNAc | Fuc(a1-6)GlcNAc | F(3)XA2 | Fuc(a1-2)Gal | GalNAcOS(b1-4)GlcNAc | GalNAc(b1-4)GlcNAc | Gal(b1-?)GlcNAc | Fuc(a1-?)GlcNAc | GlcNAc(b1-?)Man | Man(a1-?)Man | Man(b1-?)GlcNAc | GlcNAc(b1-?)GlcNAc | Fuc(a1-6) | Man(a1-6) | Fuc(a1-2) | Fuc(a1-4) | GalNAc(b1-4) | Gal(b1-4) | Neu5Ac(a2-3) | Gal(b1-3) | Man(a1-3) | F(3) | Fuc(a1-3) | GalNAcOS(b1-4) | Gal(b1-?) | Man(a1-?) | Fuc(a1-?) | +|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------|-----------------|--------|--------------|-------------------|-----------------|-----------------|--------|--------------|-------------|---------|---------|-----------|-----------|----------------|--------|-----|-----|-----|-------|--------|-------|--------|--------|-------|--------|--------|----------|-------|----------|-------|----------|-------|----------|---------|------------------|------|-----------|-----------|------------|------------|-----------------|-----------------------|-----------------------|-----------------------|-----------------------|-------------------------|-------------------------|-------------------------|-------------------------|-----------------|-----|------------|----------------|----------------------|-----------------|------------|----------------|-----------|--------------|---------------|----------------|----------------|------------|-----------------|--------|--------------|-----|-----|-------------|---------|---------------|-----------------|-----|------------------|-----------------------|--------|-----|---------|-----|-----|--------|------|-----|------|-----|------|-------|---------------|---------|------|-----|--------|-------|-----|--------------------|------|-----|------|----------|------|------------------|-------|------|------|-------|---------------|-----------|------------------------|-------------------------------|-------|-------|------------------|---------------|-------------|-------------------|------|--------|-------|---------------------------|---------|--------------|-------------|----------|-----------------|------------------|---------------|---------------|---------------|---------------|---------------|---------------|---------------|------------------|-----------------------|------|------|-----------------------|------------|----------------|----------|------------|-----------------|------|-----|-----------------|-----------------------|---------------|------------------------|-----------------------|----------|----------|-----|-----|--------------------|-------------|------|---------------|------------|-----------|-----------|------------|------------|------------|------------|-----|-----|-----|-----|-----|-----|---------|-----|-----|---------|-----|---------|---------|-----|---------|---------|---------|---------|-----|-----|-----|--------|-----|-----|-----|-------------|--------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----|-----------|-----------------|-----------------|-----------------|-----------------|--------------|-----------------|-----------------|--------------|-----------------|--------------------|-----------------|---------|--------------|----------------------|--------------------|-----------------|-----------------|-----------------|--------------|-----------------|--------------------|-----------|-----------|-----------|-----------|--------------|-----------|--------------|-----------|-----------|------|-----------|----------------|-----------|-----------|-----------| +| Neu5Ac(a2-3)Gal(b1-4)\[Fuc(a1-3)\]GlcNAc(b1-2)Man(a1-3)\[Gal(b1-3)\[Fuc(a1-4)\]GlcNAc(b1-2)Man(a1-6)\]Man(b1-4)GlcNAc(b1-4)\[Fuc(a1-6)\]GlcNAc | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 2 | 4 | 0 | 0 | 3 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 2 | 3 | 2 | 2 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 3 | +| Ma3(Ma6)Mb4GNb4GN;N | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 2 | 0 | +| α-D-Manp-(1→3)\[α-D-Manp-(1→6)\]-β-D-Manp-(1→4)-β-D-GlcpNAc-(1→4)-β-D-GlcpNAc-(1→ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 2 | 0 | +| F(3)XA2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | +| WURCS=2.0/5,11,10/\[a2122h-1b_1-5_2\*NCC/3=O\]\[a1122h-1b_1-5\]\[a1122h-1a_1-5\]\[a2112h-1b_1-5\]\[a1221m-1a_1-5\]/1-1-2-3-1-4-3-1-4-5-5/a4-b1_a6-k1_b4-c1_c3-d1_c6-g1_d2-e1_e4-f1_g2-h1_h4-i1_i2-j1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 2 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | +| RES\n1b:b-dglc-HEX-1:5\n2s:n-acetyl\n3b:b-dglc-HEX-1:5\n4s:n-acetyl\n5b:b-dman-HEX-1:5\n6b:a-dman-HEX-1:5\n7b:b-dglc-HEX-1:5\n8s:n-acetyl\n9b:b-dgal-HEX-1:5\n10s:sulfate\n11s:n-acetyl\n12b:a-dman-HEX-1:5\n13b:b-dglc-HEX-1:5\n14s:n-acetyl\n15b:b-dgal-HEX-1:5\n16s:n-acetyl\nLIN\n1:1d(2+1)2n\n2:1o(4+1)3d\n3:3d(2+1)4n\n4:3o(4+1)5d\n5:5o(3+1)6d\n6:6o(2+1)7d\n7:7d(2+1)8n\n8:7o(4+1)9d\n9:9o(-1+1)10n\n10:9d(2+1)11n\n11:5o(6+1)12d\n12:12o(2+1)13d\n13:13d(2+1)14n\n14:13o(4+1)15d\n15:15d(2+1)16n | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | ``` python #using graphs, you can easily check whether a glycan contains a specific motif; how about internal Lewis A/X motifs? diff --git a/glycowork/motif/tokenization.py b/glycowork/motif/tokenization.py index e66d0cf1..e103e2d9 100644 --- a/glycowork/motif/tokenization.py +++ b/glycowork/motif/tokenization.py @@ -603,3 +603,28 @@ def glycan_to_mass(glycan, mass_value = 'monoisotopic', sample_prep = 'underivat stem_libr = stem_lib comp = glycan_to_composition(glycan, stem_libr = stem_libr) return composition_to_mass(comp, mass_value = mass_value, sample_prep = sample_prep) + + +@rescue_compositions +def get_unique_topologies(composition, glycan_type, df_use = None, universal_replacers = {}, + taxonomy_rank = "Kingdom", taxonomy_value = "Animalia"): + """given a composition, retrieves all observed and unique base topologies\n + | Arguments: + | :- + | composition (dict): composition in form monosaccharide:count + | glycan_type (string): which glycan class to search, 'N', 'O', 'lipid', 'free', or 'repeat' + | df_use (dataframe): species-specific glycan dataframe to use for mapping; default: df_glycan + | universal_replacers (dictionary): dictionary of form base monosaccharide : specific monosaccharide + | taxonomy_rank (string): at which taxonomic rank to filter; default: Kingdom + | taxonomy_value (string): which value to filter at taxonomy_rank; default: Animalia\n + | Returns: + | :- + | Returns a list of observed base topologies for the given composition + """ + if df_use is None: + df_use = df_glycan + df_use = df_use[df_use.Composition == composition] + df_use = df_use[df_use.glycan_type == glycan_type] + df_use = df_use[df_use[taxonomy_rank].apply(lambda x: taxonomy_value in x)].glycan.values + df_use = list(set([structure_to_basic(k) for k in df_use])) + return [[g.replace(k, v) for k,v in universal_replacers.items()][0] for g in df_use if '{' not in g]