deepaccess 0.1.2

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

deepaccess-package

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 https://cgs.csail.mit.edu/deepaccess-package/. 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.

Dependencies

• bedtools (v2.29.2)

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"

Installation

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

Training

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

Arguments

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

Interpretation

To run interpretation of a DeepAccess model

usage: 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, --makeVis

Arguments

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