Package: deepaccess
deepaccess-packageThis is the code for training and interpretation of an ensemble of convolutional neural networks for multi-task classification. Instructions for downloading and getting started with the current release are available athttps://cgs.csail.mit.edu/deepaccess-package/. deepaccess is available viapipandbioconda. The DeepAccess model trained on ATAC-seq data from 10 mouse cell types is available as azenodo record.Dependenciesbedtools(v2.29.2)To run DeepAccess with regions (bedfile format) you must install bedtools and add it to your path. Bedtools binaries are availablehere.After installation, you can add bedtools to your path via the terminal or modifying your ~/.bashrcexport PATH="/path/to/bedtools:$PATH"Installationdeepaccess is available on the Python Package Index (PyPI) and can be installed with pip:pip install deepaccessand via bioconda:conda install -c bioconda deepaccessTrainingTo train a DeepAccess model for a new taskusage: deepaccess train [-h] -l LABELS [LABELS ...]
       		  -out OUT [-ref REFFASTA]
		  [-g GENOME] [-beds BEDFILES [BEDFILES ...]]
		  [-fa FASTA] [-fasta_labels FASTA_LABELS]
                  [-f FRAC_RANDOM] [-nepochs NEPOCHS]
		  [-ho HOLDOUT] [-seed SEED] [-verbose]

optional arguments:
  -h, --help            show this help message and exit
  -l LABELS [LABELS ...], --labels LABELS [LABELS ...]
  -out OUT, --out OUT
  -ref REFFASTA, --refFasta REFFASTA
  -g GENOME, --genome GENOME
                        genome chrom.sizes file
  -beds BEDFILES [BEDFILES ...], --bedfiles BEDFILES [BEDFILES ...]
  -fa FASTA, --fasta FASTA
  -fasta_labels FASTA_LABELS, --fasta_labels FASTA_LABELS
  -f FRAC_RANDOM, --frac_random FRAC_RANDOM
  -nepochs NEPOCHS, --nepochs NEPOCHS
  -ho HOLDOUT, --holdout HOLDOUT
                        chromosome to holdout
  -seed SEED, --seed SEED
  -verbose, --verbose   Print training progressArgumentsArgumentDescriptionExample-h, --helpshow this help message and exitNA-l --labelslist of labels for each bed fileC1 C2 C3-out --outoutput folder namemyoutput-ref --refreference fasta; required with bed inputmm10.fa-g --genomegenome chromosome sizes; required with bed inputdefault/mm10.chrom.sizes-beds --bedfileslist of bed files; one of beds or fa input requiredC1.bed C2.bed C3.bed-fa --fastafasta file;  one of beds or fa input requiredC1C2C3.fa-fasta_labels --fasta_labelstext file containing tab delimited labels (0 or 1) for each fasta line with one column for each classC1C2C3.txt-f  --frac_randomfor bed file input fraction of random outgroup regions to add to training0.1-nepochs --nepochsnumber of training iterations1-ho --holdoutchromosome name to hold out (only with bed input)chr19-verbose --verboseprint training and evaluation progressNA-seed --seedset tensorflow seed2021InterpretationTo run interpretation of a DeepAccess modelusage: deepaccess interpret [-h] -trainDir TRAINDIR
       		  [-fastas FASTAS [FASTAS ...]]
		  [-l LABELS [LABELS ...]] [
		  -c COMPARISONS [COMPARISONS ...]]
		  [-evalMotifs EVALMOTIFS]
                  [-evalPatterns EVALPATTERNS]
		  [-p POSITION] [-saliency]
		  [-subtract] [-bg BACKGROUND] [-vis]

optional arguments:
  -h, --help            show this help message and exit
  -trainDir TRAINDIR, --trainDir TRAINDIR
  -fastas FASTAS [FASTAS ...], --fastas FASTAS [FASTAS ...]
  -l LABELS [LABELS ...], --labels LABELS [LABELS ...]
  -c COMPARISONS [COMPARISONS ...], --comparisons COMPARISONS [COMPARISONS ...]
  -evalMotifs EVALMOTIFS, --evalMotifs EVALMOTIFS
  -evalPatterns EVALPATTERNS, --evalPatterns EVALPATTERNS
  -p POSITION, --position POSITION
  -saliency, --saliency
  -subtract, --subtract
  -bg BACKGROUND, --background BACKGROUND
  -vis, --makeVisArgumentsArgumentDescriptionExample-h, --helpshow this help message and exitNA-trainDir --trainDirdirectory containing trained DeepAccess modeltest/ASCL1vsCTCF-fastas --fastaslist of fasta files to evaulatetest/ASCL1vsCTCF/test.fa-l --labelslist of labels for each bed fileC1 C2 C3-c --comparisonslist of comparisons between different labelsASCL1vsCTCF ASCL1vsNone runs differential EPE between ASCL1 and CTCF and EPE on ASCL1; C1,C2vsC3 runs differential EPE for (C1 and C2) vs C3-evalMotifs --evalMotifsPWM or PCM data base of DNA sequence motifsdefault/HMv11_MOUSE.txt-evalPatterns --evalPatternsfasta file containing DNA sequence patternsdata/ASCL1_space.fa-bg --bgfasta file containning background sequencesdefault/backgrounds.fa-saliency --saliencycalculate per base nucleotide importanceNA-subtract --subtractuse subtraction instead of ratio for EPE / DEPEFalse-vis --makeVisto be used with saliency to make plot visualizing resultsNA