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A package for training and interpreting an ensemble of neural networks for chromatin accessibility

Project description


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This 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 at deepaccess is available via pip and bioconda. The DeepAccess model trained on ATAC-seq data from 10 mouse cell types is available as a zenodo record.


To run DeepAccess with regions (bedfile format) you must install bedtools and add it to your path. Bedtools binaries are available here.

After installation, you can add bedtools to your path via the terminal or modifying your ~/.bashrc

export PATH="/path/to/bedtools:$PATH"


deepaccess is available on the Python Package Index (PyPI) and can be installed with pip:

pip install deepaccess

and via bioconda:

conda install -c bioconda deepaccess


To train a DeepAccess model for a new task

usage: 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 progress


Argument Description Example
-h, --help show this help message and exit NA
-l --labels list of labels for each bed file C1 C2 C3
-out --out output folder name myoutput
-ref --ref reference fasta; required with bed input mm10.fa
-g --genome genome chromosome sizes; required with bed input default/mm10.chrom.sizes
-beds --bedfiles list of bed files; one of beds or fa input required C1.bed C2.bed C3.bed
-fa --fasta fasta file; one of beds or fa input required C1C2C3.fa
-fasta_labels --fasta_labels text file containing tab delimited labels (0 or 1) for each fasta line with one column for each class C1C2C3.txt
-f --frac_random for bed file input fraction of random outgroup regions to add to training 0.1
-nepochs --nepochs number of training iterations 1
-ho --holdout chromosome name to hold out (only with bed input) chr19
-verbose --verbose print training and evaluation progress NA
-seed --seed set tensorflow seed 2021


To run interpretation of a DeepAccess model

usage: deepaccess interpret [-h] -trainDir TRAINDIR
       		  [-fastas FASTAS [FASTAS ...]]
		  [-l LABELS [LABELS ...]] [
		  [-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 ...]
  -evalMotifs EVALMOTIFS, --evalMotifs EVALMOTIFS
  -evalPatterns EVALPATTERNS, --evalPatterns EVALPATTERNS
  -p POSITION, --position POSITION
  -saliency, --saliency
  -subtract, --subtract
  -bg BACKGROUND, --background BACKGROUND
  -vis, --makeVis


Argument Description Example
-h, --help show this help message and exit NA
-trainDir --trainDir directory containing trained DeepAccess model test/ASCL1vsCTCF
-fastas --fastas list of fasta files to evaulate test/ASCL1vsCTCF/test.fa
-l --labels list of labels for each bed file C1 C2 C3
-c --comparisons list of comparisons between different labels ASCL1vsCTCF ASCL1vsNone runs differential EPE between ASCL1 and CTCF and EPE on ASCL1; C1,C2vsC3 runs differential EPE for (C1 and C2) vs C3
-evalMotifs --evalMotifs PWM or PCM data base of DNA sequence motifs default/HMv11_MOUSE.txt
-evalPatterns --evalPatterns fasta file containing DNA sequence patterns data/ASCL1_space.fa
-bg --bg fasta file containning background sequences default/backgrounds.fa
-saliency --saliency calculate per base nucleotide importance NA
-subtract --subtract use subtraction instead of ratio for EPE / DEPE False
-vis --makeVis to be used with saliency to make plot visualizing results NA

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