- Pipeline
- Installation and Requirements
- Basic Usage
- Advanced Usage
- Example Output
- Container Usage
- Contact Information
To install, this package and all python3 dependencies:
python3 -m pip install tfea
This should take no longer than several minutes.
Once successfully installed, you should be able to run the tfea command from anywhere, try:
TFEA --help
Note: If you plan to run TFEA only on FIJI using the --sbatch flag, then you only need to install DESeq and DESeq2. Otherwise, follow the instructions below for installing all TFEA dependencies.
TFEA uses DESeq or DESeq2 (depending on replicate number) to rank inputted bed files based on fold change significance. If on FIJI, make sure all gcc modules are unloaded before installing DESeq or DESeq2. This can be accomplished with:module unload gcc
or
module purge
To install DESeq and DESeq2 type in your terminal:
R
> if (!requireNamespace("BiocManager", quietly = TRUE))
> install.packages("BiocManager")
> BiocManager::install("DESeq")
> BiocManager::install("DESeq2")
If you are on FIJI compute cluster, bedtools is available as a module:
module load bedtools/2.25.0
If you are on FIJI compute cluster, bedtools is available as a module:
module load samtools/1.8
MEME Download and Installation
If you are on FIJI compute cluster, the meme suite is available as a module:
module load meme/5.0.3
identify -version
If you do not have Image Magick installed, follow these instructions:
Image Magick Download and Installation
Below is a summary of all FIJI modules needed to run TFEA.module load python/3.6.3
module load python/3.6.3/matplotlib/1.5.1
module load python/3.6.3/scipy/0.17.1
module load python/3.6.3/numpy/1.14.1
module load python/3.6.3/htseq/0.9.1
module load python/3.6.3/pybedtools/0.7.10
module load samtools/1.8
module load bedtools/2.25.0
module load meme/5.0.3
Note: If you chose to skip installations because you were going to run TFEA using the --sbatch flag, make sure you load the appropriate modules on FIJI or these tests will fail.
TFEA --test-install
TFEA --test-full
These should each take no longer than several minutes to run
If on a compute cluster with slurm the --sbatch flag is compatible with --test-full and is recommended on FIJI. Execute like so:
TFEA --test-full --sbatch your_email@address.com
TFEA --output ./TFEA/test/test_files/test_output \
--bed1 ./TFEA/test/test_files/SRR1105736.tfit_bidirs.chr22.bed ./TFEA/test/test_files/SRR1105737.tfit_bidirs.chr22.bed \
--bed2 ./TFEA/test/test_files/SRR1105738.tfit_bidirs.chr22.bed ./TFEA/test/test_files/SRR1105739.tfit_bidirs.chr22.bed \
--bam1 ./TFEA/test/test_files/SRR1105736.sorted.chr22.subsample.bam ./TFEA/test/test_files/SRR1105737.sorted.chr22.subsample.bam \
--bam2 ./TFEA/test/test_files/SRR1105738.sorted.chr22.subsample.bam ./TFEA/test/test_files/SRR1105739.sorted.chr22.subsample.bam \
--label1 DMSO --label2 Nutlin \
--genomefasta ./TFEA/test/test_files/chr22.fa \
--fimo_motifs ./TFEA/test/test_files/test_database.meme
TFEA --config ./TFEA/test/test_files/test_config.ini
This can be helpful to keep track of different TFEA runs and because you can use variables within the config file to clean up your input. For documentation on config files and what you can do with them see Supported INI File Structure and Interpolation of values (ExtendedInterpolation)
Notes:
- Section headers (ex:
[OUTPUT]
) don't matter but you need to have at least ONE section header to be a viable .ini file. - Capitalization of variables doesn't matter.
- Feel free to specify any additional variables you like (variables are bash-like), TFEA will only parse variables that match a flag input.
- If an input is provided both as a flag and in a configuration file, TFEA prioritizes the command line flag input.
Below is an example of a configuration file (./test_files/test_config.ini):
[OUTPUT]
OUTPUT='./TFEA/test/test_files/test_output/'
LABEL1='Condition 1'
LABEL2='Condition 2'
[DATA]
TEST_FILES='./TFEA/test/test_files/'
BED1=[${TEST_FILES}+'SRR1105736.tfit_bidirs.chr22.bed',${TEST_FILES}+'SRR1105737.tfit_bidirs.chr22.bed']
BED2=[${TEST_FILES}+'SRR1105738.tfit_bidirs.chr22.bed',${TEST_FILES}+'SRR1105739.tfit_bidirs.chr22.bed']
BAM1=[${TEST_FILES}+'SRR1105736.sorted.chr22.subsample.bam', ${TEST_FILES}+'SRR1105737.sorted.chr22.subsample.bam']
BAM2=[${TEST_FILES}+'SRR1105738.sorted.chr22.subsample.bam', ${TEST_FILES}+'SRR1105739.sorted.chr22.subsample.bam']
[MODULES]
TEST_FILES='./TFEA/test/test_files/' #You need to re-initialize variables within each [MODULE]
FIMO_MOTIFS=${TEST_FILES}+'test_database.meme'
GENOMEFASTA=${TEST_FILES}+'chr22.fa'
[OPTIONS]
OUTPUT_TYPE='html'
PLOTALL=True
The presence of batch effects is common in sequencing data. TFEA can account for batch effects when performing ROI ranking using DE-Seq using built in functions. To correct for batch effects, specify a comma-separated list of batch labels to apply to your bam files in order of bam1 then bam2. For example:
--bam1 condition1_batch1 condition1_batch2 condition1_batch3
--bam2 condition2_batch1 condition2_batch2 condition2_batch3
--batch 1,2,3,1,2,3
Specifying the --sbatch
flag will submit TFEA to a compute cluster assuming you are logged into one. If the --sbatch
flag is specified, it MUST be followed by an e-mail address to send job information to. For example:
TFEA --config ./TFEA/test/test_files/test_config.ini --sbatch your_email@address.com
Additionally, the following flags can be used to change some of the job parameters and specify a python virtual environment:
--cpus CPUS Number of processes to run in parallel. Warning:
Increasing this value will significantly increase
memory footprint. Default: 1
--mem MEM Amount of memory to request forsbatch script. Default:
50gb
--venv VENV Full path to virtual environment.
Note: --cpus
also works without the --sbatch
flag. The --venv
flag takes the root venv directory, it then activates the venv by calling source <venv path>/bin/activate
A sorted (by chrom, start, stop) bed file containing regions of interest
Example (./test_files/test_combined_file.bed)
#chrom start stop
chr22 10683195 10683999
chr22 16609343 16609405
chr22 16901069 16902599
chr22 17036962 17037636
chr22 17158022 17160214
...
Usage with TFEA
TFEA --output ./TFEA/test/test_files/test_output \
--combined_file ./TFEA/test/test_files/test_combined_file.bed \
--bam1 ./TFEA/test/test_files/SRR1105736.sorted.chr22.subsample.bam ./test_files/SRR1105737.sorted.chr22.subsample.bam \
--bam2 ./TFEA/test/test_files/SRR1105738.sorted.chr22.subsample.bam ./test_files/SRR1105739.sorted.chr22.subsample.bam \
--label1 condition1 --label2 condition2 \
--genomefasta ./TFEA/test/test_files/chr22.fa \
--fimo_motifs ./TFEA/test/test_files/test_database.meme
A ranked bed file with regions of interest. Important: The regions must be 3000bp wide (1.5kb around the original middle (or mu) of the region) for proper motif distance analysis.
Note: Specifying a ranked_file turns off some plotting functionality
Example (./test_files/test_ranked_file.bed)
#chrom start stop
chr22 50794870 50797870
chr22 21554591 21557591
chr22 50304644 50307644
chr22 39096295 39099295
chr22 31176104 31179104
...
Usage with TFEA
TFEA --output ./TFEA/test/test_files/test_output \
--ranked_file ./TFEA/test/test_files/test_ranked_file.bed \
--label1 condition1 --label2 condition2 \
--genomefasta ./TFEA/test/test_files/chr22.fa \
--fimo_motifs ./TFEA/test/test_files/test_database.meme
A ranked fasta file with regions of interest (sequences must have unique names but these names aren't used for anything).
Note: Specifying a fasta_file turns off some plotting functionality
Example (./test_files/test_fasta_file.bed)
>chr22:50794870-50797870
ccgccccacactgacgcagt...ccgcctcagcctcctaaa
>chr22:21554591-21557591
cttggggagagcagaagcca...gtgcagtggtgcaatctt
>chr22:50304644-50307644
CTGAGCACCCCCCACCAGCCA...GGAGACGGGGCCTTTGT
...
Usage with TFEA
TFEA --output ./TFEA/test/test_files/test_output \
--fasta_file ./TFEA/test/test_files/test_fasta_file.fa \
--label1 condition1 --label2 condition2 \
--genomefasta ./TFEA/test/test_files/chr22.fa \
--fimo_motifs ./TFEA/test/test_files/test_database.meme
TFEA --output ./TFEA/test/test_files/test_output \
--combined_file ./TFEA/test/test_files/test_combined_file.bed \
--bam1 ./TFEA/test/test_files/SRR1105736.sorted.chr22.subsample.bam ./TFEA/test/test_files/SRR1105737.sorted.chr22.subsample.bam \
--bam2 ./TFEA/test/test_files/SRR1105738.sorted.chr22.subsample.bam ./TFEA/test/test_files/SRR1105739.sorted.chr22.subsample.bam \
--label1 condition1 --label2 condition2 \
--genomefasta ./TFEA/test/test_files/chr22.fa \
--fimo_motifs ./TFEA/test/test_files/test_database.meme \
--md --mdd
These secondary analyses can also take pre-processed input similar to TFEA. See the 'Secondary Analysis Inputs' section in the help message for more information.
TFEA will also measure the FPKM of TF genes within your data if desired. This requires input into the `--motif_annotations` flag which is a bed file with motif names as the 4th column. Example:chr1 3698045 3733079 P73_HUMAN.H11MO.0.A 0 +
chr1 6579990 6589212 ZBT48_HUMAN.H11MO.0.C 0 +
chr1 15941868 15948495 ZBT17_HUMAN.H11MO.0.A 0 -
chr1 23359447 23368005 ZN436_HUMAN.H11MO.0.C 0 -
This special bed file can be generated from a .meme database file using a tab-separated 2-column file containing motif names to gene names and a gene annotation file:
Example of a motif_to_gene.tsv (this was generated on the HOCOMOCO v11 website):
Model Transcription factor
ANDR_HUMAN.H11MO.0.A AR
AP2A_HUMAN.H11MO.0.A TFAP2A
AP2C_HUMAN.H11MO.0.A TFAP2C
ASCL1_HUMAN.H11MO.0.A ASCL1
Example of gene annotations:
chr1 11873 14409 DDX11L1;NR_046018;chr1:11873-14409 0 +
chr1 14361 29370 WASH7P;NR_024540;chr1:14361-29370 0 -
chr1 17368 17436 MIR6859-1;NR_106918;chr1:17368-17436 0 -
chr1 17368 17436 MIR6859-4;NR_128720;chr1:17368-17436 0 -
chr1 17368 17436 MIR6859-3;NR_107063;chr1:17368-17436 0 -
The script works by looking for gene names that correspond to each motif within the 4th column of the gene annotation file. It expects the 4th column to be ';' delimited.
Already generated motif_annotation.bed files (and also intermediate .tsv files) are located within './motif_files/'
TFEA has a subpackage that is capable of generating simulated data for testing. If you have installed TFEA, it can be invoked with:TFEA-simulate --help
The purpose of this subpackage is to embed motif instances into fasta sequences that can be generated randomly or be from an experimental dataset (e.g. untreated control sample). There are several key flags that control this process (each of these may be a comma-delimited list of values that would indicate multiple instances of motif adding):
--distance_mu
: This flag controls where the center of the distribution is located (note: only normal distributions are supported at this point)
--distance_sigma
: Controls the standard deviation of the normal distribution
--rank_range
: Controls the range of sequences in which to add a motif
--motif_number
: Controls the number of motifs to add to your range of sequences
TFEA --rerun ./TFEA/test/test_files/test_output
The --rerun
flag also supports patterns containing wildcards to rerun all TFEA output folders that match. For example:
TFEA --rerun ./TFEA/test/test_files/test*
This works by looking recursively into all folders and subfolders for rerun.sh scripts and then executing sh rerun.sh
, so use with caution!
usage: TFEA [-h] [--output DIR] [--bed1 [BED1 [BED1 ...]]]
[--bed2 [BED2 [BED2 ...]]] [--bam1 [BAM1 [BAM1 ...]]]
[--bam2 [BAM2 [BAM2 ...]]] [--bg1 [BG1 [BG1 ...]]]
[--bg2 [BG2 [BG2 ...]]] [--label1 LABEL1] [--label2 LABEL2]
[--genomefasta GENOMEFASTA] [--fimo_motifs FIMO_MOTIFS]
[--config CONFIG] [--sbatch SBATCH] [--test-install] [--test-full]
[--combined_file COMBINED_FILE] [--ranked_file RANKED_FILE]
[--fasta_file FASTA_FILE] [--md] [--mdd]
[--md_bedfile1 MD_BEDFILE1] [--md_bedfile2 MD_BEDFILE2]
[--mdd_bedfile1 MDD_BEDFILE1] [--mdd_bedfile2 MDD_BEDFILE2]
[--md_fasta1 MD_FASTA1] [--md_fasta2 MD_FASTA2]
[--mdd_fasta1 MDD_FASTA1] [--mdd_fasta2 MDD_FASTA2]
[--mdd_pval MDD_PVAL] [--mdd_percent MDD_PERCENT]
[--combine {mumerge,intersect/merge,mergeall,tfitclean,tfitremovesmall}]
[--rank {deseq,fc,False}] [--scanner {fimo,genome hits}]
[--enrichment {auc,auc_bgcorrect}] [--fimo_thresh FIMO_THRESH]
[--fimo_background FIMO_BACKGROUND] [--genomehits GENOMEHITS]
[--singlemotif SINGLEMOTIF] [--permutations PERMUTATIONS]
[--largewindow LARGEWINDOW] [--smallwindow SMALLWINDOW]
[--padjcutoff PADJCUTOFF] [--plot_format {png,svg,pdf}]
[--dpi DPI] [--plotall] [--metaprofile] [--output_type {txt,html}]
[--batch BATCH] [--cpus CPUS] [--mem MEM]
[--motif_annotations MOTIF_ANNOTATIONS] [--bootstrap BOOTSTRAP]
[--basemean_cut BASEMEAN_CUT] [--rerun [RERUN [RERUN ...]]]
[--gc GC] [--venv VENV] [--debug]
Transcription Factor Enrichment Analysis (TFEA) v1.1.3
optional arguments:
-h, --help show this help message and exit
Main Inputs:
Inputs required for full pipeline
--output DIR, -o DIR Full path to output directory. If it exists, overwrite
its contents.
--bed1 [BED1 [BED1 ...]]
Bed files associated with condition 1
--bed2 [BED2 [BED2 ...]]
Bed files associated with condition 2
--bam1 [BAM1 [BAM1 ...]]
Sorted bam files associated with condition 1. Must be
indexed.
--bam2 [BAM2 [BAM2 ...]]
Sorted bam files associated with condition 2. Must be
indexed.
--bg1 [BG1 [BG1 ...]]
Sorted bedGraph files associated with condition 1.
Must be indexed.
--bg2 [BG2 [BG2 ...]]
Sorted bedGraph files associated with condition 2.
Must be indexed.
--label1 LABEL1 An informative label for condition 1
--label2 LABEL2 An informative label for condition 2
--genomefasta GENOMEFASTA
Genomic fasta file
--fimo_motifs FIMO_MOTIFS
Full path to a .meme formatted motif databse file.
Some databases included in motif_files folder.
--config CONFIG, -c CONFIG
A configuration file that a user may use instead of
specifying flags. Command line flags will overwrite
options within the config file. See examples in the
config_files folder.
--sbatch SBATCH, -s SBATCH
Submits an sbatch (slurm) job. If specified, input an
e-mail address.
--test-install, -ti Checks whether all requirements are installed and
command-line runnable.
--test-full, -t Performs unit testing on full TFEA pipeline.
Processed Inputs:
Input options for pre-processed data
--combined_file COMBINED_FILE
A single bed file combining regions of interest.
--ranked_file RANKED_FILE
A bed file containing each regions rank as the 4th
column.
--fasta_file FASTA_FILE
A fasta file containing sequences to be analyzed,
ranked by the user.
Secondary Analysis Inputs:
Input options for performing MD-Score and Differential MD-Score analysis
--md Switch that controls whether to perform MD analysis.
--mdd Switch that controls whether to perform differential
MD analysis.
--md_bedfile1 MD_BEDFILE1
A bed file for MD-Score analysis associated with
condition 1.
--md_bedfile2 MD_BEDFILE2
A bed file for MD-Score analysis associated with
condition 2.
--mdd_bedfile1 MDD_BEDFILE1
A bed file for Differential MD-Score analysis
associated with condition 1.
--mdd_bedfile2 MDD_BEDFILE2
A bed file for Differential MD-Score analysis
associated with condition 2.
--md_fasta1 MD_FASTA1
A fasta file for MD-Score analysis associated with
condition 1.
--md_fasta2 MD_FASTA2
A fasta file for MD-Score analysis associated with
condition 2.
--mdd_fasta1 MDD_FASTA1
A fasta file for Differential MD-Score analysis
associated with condition 1.
--mdd_fasta2 MDD_FASTA2
A fasta file for Differential MD-Score analysis
associated with condition 2.
--mdd_pval MDD_PVAL P-value cutoff for retaining differential regions.
Default: 0.2
--mdd_percent MDD_PERCENT
Percentage cutoff for retaining differential regions.
Default: False
Modules:
Options for different modules
--combine {mumerge,intersect/merge,mergeall,tfitclean,tfitremovesmall}
Method for combining input bed files. Default: mumerge
--rank {deseq,fc,False}
Method for ranking combined bed file
--scanner {fimo,genome hits}
Method for scanning fasta files for motifs. Default:
fimo
--enrichment {auc,auc_bgcorrect}
Method for calculating enrichment. Default: auc
Scanner Options:
Options for performing motif scanning
--fimo_thresh FIMO_THRESH
P-value threshold for calling FIMO motif hits.
Default: 1e-6
--fimo_background FIMO_BACKGROUND
Options for choosing mononucleotide background
distribution to use with FIMO. Default:
largewindow{'largewindow', 'smallwindow', int, file}
--genomehits GENOMEHITS
A folder containing bed files with pre-calculated
motif hits to a genome. For use with 'genome hits'
scanner option.
--singlemotif SINGLEMOTIF
Option to run analysis on a subset of motifs within
specified motif database or genome hits. Can be a
single motif or a comma-separated list of motifs.
Enrichment Options:
Options for performing enrichment analysis
--permutations PERMUTATIONS
Number of permutations to perfrom for calculating
p-value. Default: 1000
--largewindow LARGEWINDOW
The size (bp) of a large window around input regions
that captures background. Default: 1500
--smallwindow SMALLWINDOW
The size (bp) of a small window arount input regions
that captures signal. Default: 150
Output Options:
Options for the output.
--padjcutoff PADJCUTOFF
A p-adjusted cutoff value that determines some
plotting output.
--plot_format {png,svg,pdf}
Format of saved figures. Default: png
--dpi DPI Resolution of saved figures. Applies to png. Default:
100
--plotall Plot graphs for all motifs.Warning: This will make
TFEA run much slower andwill result in a large output
folder.
--metaprofile Create meta profile plots per quartile. Warning: This
will make TFEA run much slower and consume a lot more
memory.
--output_type {txt,html}
Specify output type. Selecting html will increase
processing time and memory usage. Default: txt
Miscellaneous Options:
Other options.
--batch BATCH Comma-separated list of batches to assign to bam files
in order of bam1 files then bam2 files. For use only
when ranking with DE-Seq.
--cpus CPUS Number of processes to run in parallel. Warning:
Increasing this value will significantly increase
memory footprint. Default: 1
--mem MEM Amount of memory to request for sbatch script.
Default: 20gb
--motif_annotations MOTIF_ANNOTATIONS
A bed file specifying genomic coordinates for genes
corresponding to motifs. Motif name must be in the 4th
column and match what is in the database.
--bootstrap BOOTSTRAP
Amount to subsample motifhits to. Set to False to turn
off. Default: False
--basemean_cut BASEMEAN_CUT
Basemean cutoff value for inputted regions. Default: 0
--rerun [RERUN [RERUN ...]]
Rerun TFEA in all folders of aspecified directory.
Used as a standalone flag.Default: False
--gc GC Perform GC-correction. Default: True
--venv VENV Full path to virtual environment.
--debug Print memory and CPU usage to stderr. Also retain
temporary files.
./TFEA/test/test_output
│ rerun.sh
│ test_config.ini
│ inputs.txt
│ results.txt
│ md_results.txt
│ mdd_results.txt
│ results.html
│
└───e_and_o
│ TFEA_test_output.err
│ TFEA_test_output.out
│
└───plots
│ logo_rcMOTIF.eps
│ logo_rcMOTIF.png
│ logoMOTIF.eps
│ logoMOTIF.png
│ MOTIF_enrichment_plot.png
│ MOTIF_simulation_plot.png
│ MOTIF.results.html
│
└───temp_files
combined_file.mergeall.bed
count_file.bed
count_file.header.bed
DESeq.R
DESeq.Rout
DESeq.res.txt
markov_background.txt
ranked_file.bed
ranked_file.fa
A brief description of the files contained within this output directory are below:
This bash script can be used at any time to regenerate a TFEA output folder in its entirety, run it using:sh ./TFEA/test/test_output/rerun.sh
TFEA copies the config file you are using into the output directoy. This file is then referenced by rerun.sh.
A .txt file that contains all user-provided inputs into TFEA
Contains TFEA results tab-delimited in .txt format. For example:#TF E-Score Corrected E-Score Events GC FPKM P-adj Corrected P-adj
ZN121_HUMAN.H11MO.0.C 0.03850103634523616 0.10374224445904234 230 0.5825102880658438 nan 0.637 1e-1
SP2_HUMAN.H11MO.0.A -0.14690991996820513 -0.09232220913971645 119 0.7937748120168564 nan 1e0 0.494
Column Descriptions:
- TF - The name of the motif analyzed as it appears in the .meme database or filename within the genome hits directory without the '.bed' extension.
- E-Score - The enrichment score of the given motif. Ranges from -1 to 1.
- Corrected E-Score - The GC-corrected E-Score. Calculated by fitting a linear regression to the E-Score vs. motif GC content of results and correcting to obtain a flat line.
- Events - Number of motif hits within the analyzed regions of interest.
- GC - GC content of the analyzed motif
- FPKM - FPKM of the gene associated with the analyzed motif, see
--motif_annotations
flag. - P-adj - The adjusted p-value of the motif using the Bonferroni correction (to get the original p-value, simply divide this by the total number of motifs analyzed).
- Corrected P-adj - The adjusted p-value after GC-correction.
The main results html (if --output_type 'html'
specified). For example:
Figure 1: Main Results Page. An example main results page (i.e. results.html
). (a) TFEA GC-Plot. A scatter plot showing the raw calculated E-Score as a function of GC-content. A linear regression is fit (red line) to these data to determine if there is a GC-bias. E-Scores are then corrected to flatten the line by subtracting the y-offset from each motif to yield the corrected E-Score. TFs are also colored on the subsequent correction to be applied. (b) TFEA MA-Plot. A scatter plot showing E-Score vs. Log10(Events). Analogous to an MA-Plot produced from DE-Seq, these are a good way to judge believable motifs. The further you go to the right, the more confidence you have in smaller absolute E-Score values. (c) DE-Seq MA-Plot. A scatter plot showing all input ROIs as an MA-Plot showing fold change in reads vs. average reads across conditions. Colored based on the subsequent rank of each ROI. (d) Links to supplementary infromation, secondary analyses performed, and runtime information. (e) Lists of all motifs analyzed separated on positive and negative E-Scores. Significant motifs (or any if --plotall
is specified) may be clicked to bring up individual motif plots
Each signficant TF motif (or all motifs if --plotall
specified) will produce its own MOTIF.results.html file contained within the plots/ directory in the specified output directory. All images are also self-contained within the plots/ folder. For example:
Figure 2: Individual Motif Results Page. An example individual motif results page. (a) The numerical results for this specific motif. (b) A representation of the running sum statistic which increases from 0 as it travels right based on the distance of an observed motif to the center of each ROI. (c) Representation of the amount added to the running sum at each given location. Similar to GSEA enrichment plots. (d) Scatter plot showing the raw motif hits as a function of distance to ROI center (y-axis) and rank (x-axis). (e) Representation of the ranking metric used to rank ROI. Specifically this is the -log10 of the DE-Seq p-value with an added sign (+/-) based on whether the ROI fold change was positive or negative. (f) Meta plots of all regions that contain a motif hit separated by quartiles (n=number of ROI that go into the plot). (g) Heatmaps that represent motif hit distribution across the n ROI, separated again by quartiles. (h) Forward and reverse motif logos. (i) Simulation plot showing the background simulated distribution in blue, the observed non-corrected E-Score in red, and the GC-corrected E-Score in green.
TFEA provides support for the use of container images with all dependencies built-in. Both Singularity and Docker images are supported. The procedure for building these containers is described below, with detailed instructions for use also available.
If you have up-to-date versions of both Singularity and Docker
installed, the easiest way to build the container images is to run
make
at the root of the repository. This will run the build process
for both Docker and Singularity automatically.
Documentation for installing singularity can be found at
https://sylabs.io/docs/. A singularity definition file for building
TFEA is included in this repository at ./Singularity.
If you have make
available on your system, simply run make singularity
to build the image automatically. Please note that this
requires Singularity >3.3 for use of the 'fakeroot' feature so that
root is not required to build the container.
If you would like to build the container manually, you can do so with
with the following command, adjusting the flags to singularity build
as appropriate for your system:
singularity build -f tfea.sif Singularity/TFEA.def
You can run the singularity container as follows:
singularity exec \
--bind $DATA_PATH:$DATA_PATH \
--bind $OUTPUT_PATH:$OUTPUT_PATH \
tfea.sif TFEA --your-args
It is important that you bind both the path where your data is available and the path to your output folder so that TFEA has access to your data and a writable filesystem for storing intermediate files that are generated. More details on bind paths can be found in the singularity documentation.
Documentation for installing singularity can be found at
https://www.docker.com/. A Docker definition file for building TFEA
is included in this repository at ./Docker. If you have
make
available on your system, simply run make docker
to build the
image automatically. Depending on how Docker is installed on your
system, it may be necessary to run this command as root using sudo
for the build to execute.
If you would like to build the container manually, you can do so with
with the following command run from the root of the repository,
adjusting the flags to docker build
as appropriate for your system:
docker build -t jdrubin/tfea -f "$PWD"/Docker/Dockerfile "$PWD"
You can run the Docker container as follows:
docker run --rm \
--mount type=bind,source=$DATA_PATH,target=$DATA_PATH \
--mount type=bind,source=$OUTPUT_PATH,target=$OUTPUT_PATH \
jdrubin/tfea TFEA --your-args
It is important that you bind both the path where your data is available and the path to your output folder so that TFEA has access to your data and a writable filesystem for storing intermediate files that are generated. More details on bind paths can be found in the Docker documentation.
Jonathan.Rubin@colorado.edu