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CUT&RUNTools usage

We provide a small CUT&RUN experiment qzhudfci/datasets/src that new users can use to test drive our tool. This is GATA1 chr11 only. First put the R1_001 and R2_001 fastq files in a folder and note its path (in my case /n/scratch2/qz64/Nan_18_aug23/Nan_run_19). Then write a JSON configuration file (named config.json) as shown below.

Defining a configuration file

CUT&RUNTools requires a JSON configuration file (named as config.json) which specifies all that is needed to run an analysis. A sample configuration file is below.

{
	"Rscriptbin": "/n/app/R/3.3.3/bin",
	"pythonbin": "/n/app/python/2.7.12/bin",
	"perlbin": "/n/app/perl/5.24.0/bin",
	"javabin": "/n/app/java/jdk-1.8u112/bin",
	"trimmomaticbin": "/n/app/trimmomatic/0.36/bin",
	"trimmomaticjarfile": "trimmomatic-0.36.jar",
	"bowtie2bin": "/n/app/bowtie2/2.2.9/bin",
	"samtoolsbin": "/n/app/samtools/1.3.1/bin",
	"adapterpath": "/home/qz64/cutrun_pipeline/adapters", 
	"picardbin": "/n/app/picard/2.8.0/bin",
	"picardjarfile": "picard-2.8.0.jar",
	"macs2bin": "/n/app/macs2/2.1.1.20160309/bin",
	"macs2pythonlib": "/n/app/macs2/2.1.1.20160309/lib/python2.7/site-packages",
	"kseqbin": "/home/qz64/cutrun_pipeline", 
	"memebin": "/n/app/meme/4.12.0/bin", 
	"bedopsbin": "/n/app/bedops/2.4.30", 
	"bedtoolsbin": "/n/app/bedtools/2.26.0/bin",
	"makecutmatrixbin": "/home/qz64/.local/bin",
	"bt2idx": "/n/groups/shared_databases/bowtie2_indexes",
	"genome_sequence": "/home/qz64/chrom.hg19/hg19.fa",
	"extratoolsbin": "/home/qz64/cutrun_pipeline", 
	"extrasettings": "/home/qz64/cutrun_pipeline", 
	"input/output": {
		"fastq_directory": "/n/scratch2/qz64/Nan_18_aug23/Nan_run_19",
		"workdir": "/n/scratch2/qz64/workdir",
		"fastq_sequence_length": 42,
		"organism_build": "hg19"
	},
	"motif_finding": {
		"num_bp_from_summit": 150,
		"num_peaks": 5000,
		"total_peaks": 15000,
		"motif_scanning_pval": 0.0005,
		"num_motifs": 20
	},
	"cluster": {
		"email": "johndoe@gmail.com",
		"step_alignment": {
			"queue": "short",
			"memory": 32000,
			"time_limit": "0-12:00"
		},
		"step_process_bam": {
			"queue": "short",
			"memory": 32000,
			"time_limit": "0-12:00"
		},
		"step_motif_find": {
			"queue": "short",
			"memory": 32000,
			"time_limit": "0-12:00"
		},
		"step_footprinting": {
			"queue": "short",
			"memory": 32000,
			"time_limit": "0-12:00"
		}
	}
}

By now you should have followed INSTALL.md and successfully installed the prerequisites, so you can skip over lines regarding software installation paths.

The specific settings in config.json applicable for a new analysis are: adapterpath (line 8), bt2idx (line 17); genome_sequence (line 18); section input/output: fastq_directory (line 22), workdir (line 23), fastq_sequence_length (line 24), organism_build (line 25); section cluster: email (line 35).

  • fastq_directory is the directory containing paired-end CUT&RUN sequences (should contain _R1_001.fastq.gz and _R2_001.fastq.gz for each sample).
  • workdir is the output directory, where results are stored.
  • organism_build is one of supported genome assemblies: hg38, hg19, mm10, and mm9.
  • adapterpath contains Illumina Truseq3-PE adapter sequences (we provide them).
  • genome_sequence is the whole-genome masked sequence which matches with the appropriate organism build.

Create job submission scripts

./validate.py config.json

This script checks that your configuration file is correct and all paths are correct. You will get an empty line if the validate.py script runs without errors.

./create_scripts.py config.json

This creates a set of Slurm job-submission scripts based on the configuration file above (named config.json) in the workdir directory. The scripts can be directly, easily executed.

Four-step process for executing CUT&RUNTools

With the scripts created, we can next perform the analysis.

Step 1. Read trimming, alignment. We suppose the workdir is defined as /n/scratch2/qz64/workdir

cd /n/scratch2/qz64/workdir
sbatch ./integrated.sh GATA1_D7_30min_chr11_R2_001.fastq.gz

The parameter is the fastq file. Even though we specify the _R1_001.fastq.gz, CUT&RUNTools actually checks that both forward and reverse fastq files are present. Always use the _R1_001 of the pair as parameter of this command.

Step 2. BAM processing, peak calling. It marks duplicates in bam files, and filter fragments by size. Then it performs peak calling using MACS2 and SEACR. It runs both.

cd aligned.aug10
sbatch ./integrated.step2.sh GATA1_D7_30min_chr11_aligned_reads.bam

CUT&RUNTools varies through different peak calling settings and generates multiple results for these settings in the following directories. Based on the results, users should select only one setting to go to steps 3 & 4.

Directory (in ../) Tool Config Fragments Use duplicates (y/n)
macs2.narrow.aug18 MACS2 narrowPeak <120bp y
macs2.broad.aug18 MACS2 broadPeak <120bp y
macs2.narrow.all.frag.aug18 MACS2 narrowPeak all y
macs2.broad.all.frag.aug18 MACS2 broadPeak all y
macs2.narrow.aug18.dedup MACS2 narrowPeak <120bp n
macs2.broad.aug18.dedup MACS2 broadPeak <120bp n
macs2.narrow.all.frag.aug18.dedup MACS2 narrowPeak all n
macs2.broad.all.frag.aug18.dedup MACS2 broadPeak all n
seacr.aug12 SEACR stringent <120bp y
seacr.aug12.all.frag SEACR stringent all y
seacr.aug12.dedup SEACR stringent <120bp n
seacr.aug12.all.frag.dedup SEACR stringent all n

Which directory to use: if TF CUT&RUN, I prefer macs2.narrow.aug18 or macs2.narrow.aug18.dedup. If histone CUT&RUN, use macs2.broad.all.frag.aug18. If SEACR, use seacr.aug12.all.frag (histone) or seacr.aug12 (TF) and use the stringent peaks within each folder.

For large fragment (>120bp), the peak results in directories *.all.frag.* should be used.

Step 3. Motif finding. CUT&RUNTools uses MEME-chip for de novo motif finding on sequences surrounding the peak summits.

#Use macs2.narrow.aug18 or any of the peak calling result directory in the above table
cd ../macs2.narrow.aug18
#For narrow setting, the peak file ends in .narrowPeak
#For broad setting, the peak file ends in .broadPeak
#For seacr setting, the peak file ends in .stringent.sort.bed
#Use the right peak file accordingly
sbatch ./integrate.motif.find.sh GATA1_D7_30min_chr11_aligned_reads_peaks.narrowPeak

Similar procedure applies in other peak calling directories for broadPeaks, all fragment results, or SEACR peaks.

#SEACR
cd ../seacr.aug12.all.frag
sbatch ./integrate.motif.find.sh GATA1_D7_30min_chr11_aligned_reads_treat.stringent.sort.bed

Step 4. Motif footprinting.

cd ../macs2.narrow.aug18
sbatch ./integrate.footprinting.sh GATA1_D7_30min_chr11_aligned_reads_peaks.narrowPeak

Beautiful footprinting figures will be located in the directory fimo.result. Footprinting figures are created for every motif found by MEME-chip, but only the right motif (associated with TF) will have a proper looking profile. Users can scan through all the motifs' footprints.

Outputs

CUT&RUNTools generates several outputs including: 1) de novo motifs found by motif searching, 2) aggregate motif footprinting figure, 3) individual motif site binding probability score.

For example, suppose our sample is named "GATA1_D7_30min_S11". We can do the following to access each output.

De novo motif finding results

cd macs2.narrow.aug18/random.10000
ls -ltr
total 4580
drwxrwxr-x 34 qz64 qz64    4096 Dec  2 23:51 MEME_LRF_HDP2_30min_S15_aligned_reads_shuf
-rw-rw-r--  1 qz64 qz64 1033018 Dec  2 23:51 GATA1_D7_30min_S11_aligned_reads_peaks.narrowPeak
drwxrwxr-x  2 qz64 qz64    4096 Dec  2 23:51 summits
drwxrwxr-x  2 qz64 qz64    4096 Dec  2 23:51 padded.fa
drwxrwxr-x 52 qz64 qz64    4096 Dec  2 23:51 MEME_GATA1_D9_30min_S12_aligned_reads_shuf
-rw-rw-r--  1 qz64 qz64  824697 Dec  2 23:51 GATA1_KO_30min_S14_aligned_reads_peaks.narrowPeak
drwxrwxr-x  2 qz64 qz64    4096 Dec  2 23:51 padded
-rw-rw-r--  1 qz64 qz64 1039845 Dec  2 23:51 GATA1_HDP2_30min_S13_aligned_reads_peaks.narrowPeak
drwxrwxr-x 56 qz64 qz64    4096 Dec  2 23:51 MEME_GATA1_HDP2_30min_S13_aligned_reads_shuf
drwxrwxr-x 46 qz64 qz64    4096 Dec  2 23:51 MEME_GATA1_KO_30min_S14_aligned_reads_shuf
-rw-rw-r--  1 qz64 qz64  720325 Dec  2 23:51 LRF_HDP2_30min_S15_aligned_reads_peaks.narrowPeak
-rw-rw-r--  1 qz64 qz64 1030170 Dec  2 23:51 GATA1_D9_30min_S12_aligned_reads_peaks.narrowPeak
drwxrwxr-x 54 qz64 qz64    4096 Dec  3 11:25 MEME_GATA1_D7_30min_S11_aligned_reads_shuf

cd MEME_GATA1_HDP2_30min_S13_aligned_reads_shuf
cat summary.tsv
MOTIF_INDEX	MOTIF_SOURCE	MOTIF_ID	ALT_ID	CONSENSUS	WIDTH	SITES	E-VALUE	E-VALUE_SOURCE	MOST_SIMILAR_MOTIF_SOURCE	MOST_SIMILAR_MOTIF	URL
1	MEME	AGATAAGV	MEME-1	AGATAAGV	8	410	1.6e-1288	MEME	 	 	 
2	DREME	HGATAA	DREME-1	HGATAA	6	7298	1.8e-1161	DREME	 	 	 
3	DREME	GATAAR	DREME-24	GATAAR	6	321	9.8e-816	DREME	 	 	 
4	DREME	AGATA	DREME-22	AGATA	5	826	4.2e-654	DREME	 	 	 
5	DREME	CWGATA	DREME-14	CWGATA	6	821	1.1e-432	DREME	 	 	 
6	DREME	AGATABS	DREME-4	AGATABS	7	1422	1.4e-147	DREME	 	 	 
7	DREME	CMCRCCC	DREME-2	CMCRCCC	7	1559	5.5e-100	DREME	 	 	 
8	DREME	TGAGTCAB	DREME-3	TGAGTCAB	8	744	2.0e-095	DREME	 	 	 
9	DREME	HGATTA	DREME-7	HGATTA	6	1662	2.7e-091	DREME	 	 	 
10	MEME	TGAGTCAY	MEME-4	TGAGTCAY	8	55	6.5e-082	MEME	 	 	 
11	DREME	RVCCACA	DREME-5	RVCCACA	7	1558	1.4e-045	DREME	 	 	 
12	DREME	CHGCC	DREME-6	CHGCC	5	8862	9.0e-033	DREME

The file summary.tsv shows the list of motifs that are found by motif searching.

Download the entire folder MEME_GATA1_D7_30min_S11_aligned_reads_shuf. Then go in and open index.html to see a comprehensive motif report.

Motif footprinting result and binding log odds scores

CUT&RUNTools generates a motif footprinting plot for each de novo motif that is found by the previous step. Motif footprinting results are located in fimo.result directory.

pwd
/n/scratch2/qz64/Nan_18_aug28_gata/macs2.narrow.aug18/random.10000/MEME_GATA1_HDP2_30min_S13_aligned_reads_shuf
cd ../../fimo.result
ls -ltr

total 20
drwxrwxr-x 38 qz64 qz64 4096 Dec  2 23:51 GATA1_D7_30min_S11_aligned_reads_peaks
drwxrwxr-x 36 qz64 qz64 4096 Dec  2 23:51 GATA1_D9_30min_S12_aligned_reads_peaks
drwxrwxr-x 34 qz64 qz64 4096 Dec  2 23:51 GATA1_KO_30min_S14_aligned_reads_peaks
drwxrwxr-x 19 qz64 qz64 4096 Dec  2 23:51 LRF_HDP2_30min_S15_aligned_reads_peaks
drwxrwxr-x 43 qz64 qz64 4096 Dec  2 23:51 GATA1_HDP2_30min_S13_aligned_reads_peaks

The fimo.result directory is organized by samples. Within each sample, you will see the footprints organized by de novo motifs.

cd GATA1_HDP2_30min_S13_aligned_reads_peaks/
ls

fimo2.DREME-10.CCWATCAG  fimo2.DREME-20.CCCTYCC   fimo2.DREME-5.RVCCACA               fimo2.MEME-31.TGGGCASMSTGCCAG
fimo2.DREME-11.CTCCWCCC  fimo2.DREME-21.ASAGGAAG  fimo2.DREME-6.CHGCC                 fimo2.MEME-32.TSAGAGGCAGC
fimo2.DREME-12.CACGTG    fimo2.DREME-22.AGATA     fimo2.DREME-7.HGATTA                fimo2.MEME-39.TTMKCAGCTGGGTRSASCASC
fimo2.DREME-13.CAKCTGB   fimo2.DREME-23.ACAGAMA   fimo2.DREME-8.ACTTCCKB              fimo2.MEME-3.RGGGYGGGGCC
fimo2.DREME-14.CWGATA    fimo2.DREME-24.GATAAR    fimo2.DREME-9.GTTTCY                fimo2.MEME-46.CCYCCTAGTGR
fimo2.DREME-15.CACACASA  fimo2.DREME-25.AAAAAAAA  fimo2.MEME-10.GCCCTGGSYGBTGGC       fimo2.MEME-4.TGAGTCAY
fimo2.DREME-16.CAKTTCC   fimo2.DREME-26.AAACAYA   fimo2.MEME-17.CRGGGRCTGGGCAG        fimo2.MEME-50.GTGGCCAC
fimo2.DREME-17.AGAAAAC   fimo2.DREME-27.CGCADGCG  fimo2.MEME-18.ACCACARACCA           fimo2.MEME-6.CAATTAYTTGVTMAGAGCAAACYWGAAGG
fimo2.DREME-18.GTCAC     fimo2.DREME-2.CMCRCCC    fimo2.MEME-1.AGATAAGV
fimo2.DREME-19.CCAATCGB  fimo2.DREME-3.TGAGTCAB   fimo2.MEME-2.GCAYBCTGGGAADYGTAGTYY
fimo2.DREME-1.HGATAA     fimo2.DREME-4.AGATABS    fimo2.MEME-30.CYTCCCACAGC

The ID of each motif corresponds to that in the summary.tsv in previous step. Let us take a look at the motif footprint of HGATAA motif.

cd fimo2.DREME-1.HGATAA
ls 
cisml.xml  fimo.bed  fimo.cuts.freq.txt  fimo.gff  fimo.html  fimo.lambda.txt  fimo.logratio.txt  fimo.png  fimo.postpr.txt  fimo.txt  fimo.xml

The file fimo.png contains the motif footprinting figure.

HGATAA footprint

The file fimo.logratio.txt shows the posterior binding log-odds score for all motif sites (as shown in fimo.bed file). We can sort all the sites by binding log odds score by the following.

paste fimo.bed fimo.logratio.txt|sort -t $'\t' -g -k7 -r > fimo.logratio.sorted.bed
cat fimo.logratio.sorted.bed

chr19   51161579        51161585        1-HGATAA-2-chr19        34.5    +       210.558477451988
chr19   51161526        51161532        1-HGATAA-1-chr19        34.5    +       179.033305023544
chr1    33223206        33223212        1-HGATAA-1-chr1 34.5    +       160.872851137722
chr14   103844534       103844540       1-HGATAA-1-chr14        34.5    -       155.321221174099
chr3    25645929        25645935        1-HGATAA-1-chr3 34.5    +       155.278568036591
chr6    10750100        10750106        1-HGATAA-1-chr6 34.5    -       142.401808525619
chr8    40457444        40457450        1-HGATAA-2-chr8 34.5    -       142.322297190559
chr8    27173054        27173060        1-HGATAA-1-chr8 34.5    -       131.213839588474
chr6    42060095        42060101        1-HGATAA-1-chr6 34.5    +       130.3885615178
chr9    6780043 6780049 1-HGATAA-2-chr9 34.5    -       125.950052746736
chr9    6780054 6780060 1-HGATAA-1-chr9 34.5    +       121.898538411992
chr13   52165301        52165307        1-HGATAA-1-chr13        34.5    +       120.932931896737
chr20   44844659        44844665        1-HGATAA-1-chr20        34.5    -       120.231198400945
chr1    114457022       114457028       1-HGATAA-1-chr1 34.5    +       118.77630603718
chr21   46274925        46274931        1-HGATAA-1-chr21        34.5    +       118.382887040806
chr19   10727908        10727914        1-HGATAA-1-chr19        34.5    -       117.173605700489
chr11   27442450        27442456        1-HGATAA-3-chr11        34.5    +       115.467998429997
chr6    3251929 3251935 1-HGATAA-1-chr6 34.5    -       114.091191286861

The last column above shows the binding log odds score.

In summary, the important output files are located below:

macs2.narrow.aug18/random.10000/MEME_GATA1_HDP2_30min_S13_aligned_reads_shuf/summary.tsv
macs2.narrow.aug18/fimo.result/GATA1_HDP2_30min_S13_aligned_reads_peaks/fimo2.DREME-1.HGATAA/fimo.bed
macs2.narrow.aug18/fimo.result/GATA1_HDP2_30min_S13_aligned_reads_peaks/fimo2.DREME-1.HGATAA/fimo.logratio.txt
macs2.narrow.aug18/fimo.result/GATA1_HDP2_30min_S13_aligned_reads_peaks/fimo2.DREME-1.HGATAA/fimo.png

Single locus cut profile

CUT&RUNTools allows users to obtain a single nucleotide resolution cut profile for a region of interest.

This will require a bam file that CUT&RUNTools created (preferrably a bam in aligned.aug18/dup.marked.120bp folder), and a region of interest (in the format chr11:5245029-5303165).

The script that is needed is get_cuts_single_locus.sh. There is one copy of this in macs2.narrow.aug18 in the work directory, and in each peak calling result directory in the work directory. Use the script in the appropriate peak calling directory for your purpose (for example, if TF, use macs2.narrow.aug18, but if histone, use macs2.broad.all.frag.aug18).

#construct single locus profile keeping duplicates in bam
pwd
/n/scratch2/qz64/workdir
cd macs2.narrow.aug18/
./get_cuts_single_locus.sh chr11:5245029-5303165 ../aligned.aug10/dup.marked.120bp/GATA1_D7_30min_chr11_aligned_reads.bam single.locus

Here, the 3 parameters required are: region, bam file path, output directory. single.locus is the output directory.

Once finished, let us check the outputs.

cd single.locus
ls -ltr

-rw-rw-r-- 1 qz64 qz64 306944 Feb  8 22:41 GATA1_D7_30min_chr11_aligned_reads-chr11-5245029-5303165.frag.ends.txt
-rw-rw-r-- 1 qz64 qz64 144936 Feb  8 22:41 GATA1_D7_30min_chr11_aligned_reads-chr11-5245029-5303165.frag.ends.sorted.bed
-rw-rw-r-- 1 qz64 qz64  72468 Feb  8 22:41 GATA1_D7_30min_chr11_aligned_reads-chr11-5245029-5303165.frag.ends.R2.sorted.bed
-rw-rw-r-- 1 qz64 qz64  53822 Feb  8 22:41 GATA1_D7_30min_chr11_aligned_reads-chr11-5245029-5303165.frag.ends.R2.bw
-rw-rw-r-- 1 qz64 qz64  72468 Feb  8 22:41 GATA1_D7_30min_chr11_aligned_reads-chr11-5245029-5303165.frag.ends.R2.bed
-rw-rw-r-- 1 qz64 qz64  34174 Feb  8 22:41 GATA1_D7_30min_chr11_aligned_reads-chr11-5245029-5303165.frag.ends.R2.bdg
-rw-rw-r-- 1 qz64 qz64  72468 Feb  8 22:41 GATA1_D7_30min_chr11_aligned_reads-chr11-5245029-5303165.frag.ends.R1.sorted.bed
-rw-rw-r-- 1 qz64 qz64  45585 Feb  8 22:41 GATA1_D7_30min_chr11_aligned_reads-chr11-5245029-5303165.frag.ends.R1.bw
-rw-rw-r-- 1 qz64 qz64  72468 Feb  8 22:41 GATA1_D7_30min_chr11_aligned_reads-chr11-5245029-5303165.frag.ends.R1.bed
-rw-rw-r-- 1 qz64 qz64  33738 Feb  8 22:41 GATA1_D7_30min_chr11_aligned_reads-chr11-5245029-5303165.frag.ends.R1.bdg
-rw-rw-r-- 1 qz64 qz64 306944 Feb  8 22:41 GATA1_D7_30min_chr11_aligned_reads-chr11-5245029-5303165.frag.ends.checked.txt
-rw-rw-r-- 1 qz64 qz64  58596 Feb  8 22:41 GATA1_D7_30min_chr11_aligned_reads-chr11-5245029-5303165.frag.ends.bw
-rw-rw-r-- 1 qz64 qz64 144936 Feb  8 22:41 GATA1_D7_30min_chr11_aligned_reads-chr11-5245029-5303165.frag.ends.bed
-rw-rw-r-- 1 qz64 qz64  60309 Feb  8 22:41 GATA1_D7_30min_chr11_aligned_reads-chr11-5245029-5303165.frag.ends.bdg
-rw-rw-r-- 1 qz64 qz64   2944 Feb  8 22:41 GATA1_D7_30min_chr11_aligned_reads-chr11-5245029-5303165.bam.bai
-rw-rw-r-- 1 qz64 qz64 188799 Feb  8 22:41 GATA1_D7_30min_chr11_aligned_reads-chr11-5245029-5303165.bam

Many of the files in ths folder are intermediary files and can be ignored. The important files are the three bigwigs (.frag.ends.R1.bw, .frag.ends.R2.bw, and .frag.ends.bw). The R1 and R2 bigwigs designate strand-specific cut profiles that were created. The frag.ends.bw is one that combines cuts from both strands. The bigwigs can be displayed in any visualization tools such as IGV or UCSC genome browser.

Cut profile visualized with UCSC browser

Footprinting for user-specified motif

CUT&RUNTools can run the motif scanning and footprinting steps on a user-specified motif, such as a motif from the public JASPAR database. The motif should be in the MEME format.

One of the scripts that create_scripts.py has generated is called generate.footprinting.factor.specific.centipede.py that is in the directory macs2.narrow.aug18 or macs2.narrow.aug18.dedup within your workdir that you defined.

Detailed usage is as follows:

Usage: generate.footprinting.factor.specific.centipede.py
where the options mean:
-b     Designated motif in MEME format
-p     P-value for FIMO motif scanning
-n     Name of factor

Example:

pwd
/n/scratch2/qz64/workdir
cd macs2.narrow.aug18
./generate.footprinting.factor.specific.centipede.py -b MA0035.2.GATA1.meme -p 0.001 -n GATA1

Note that option -p is the motif scanning p-value, which we recommend 0.0005, but for this example we will use 0.001 since GATA1 motif is quite short.

This Python script will generate a BASH script called integrate.footprinting.GATA1.centipede.sh, which accepts a CUT&RUN experiment narrowPeak file (generated by MACS2) as input. For example:

sbatch ./integrate.footprinting.GATA1.centipede.sh GATA1_D9_30min_S12_aligned_reads_peaks.narrowPeak

The output files will be located in the fimo.GATA1.result directory:

└----  fimo.GATA1.result
         └--- GATA1_D9_30min_S12_aligned_reads_peaks
                └---  fimo2.MA0035.2.GATA1
                        └--- cisml.xml
                        └--- fimo.bed
                        └--- fimo.cuts.freq.txt
                        └--- fimo.gff
                        └--- fimo.html
                        └--- fimo.lambda.txt
                        └--- fimo.logratio.bed
                        └--- fimo.logratio.txt
                        └--- fimo.png
                        └--- fimo.postpr.txt
                        └--- fimo.txt
                        └--- fimo.xml

The output files are fimo.bed, fimo.cuts.freq.txt, and fimo.logratio.txt, which are the binding site locations, cut frequency matrix, and binding log odds for the provided GATA1 motif.