A package for training and interpreting an ensemble of neural networks for chromatin accessibility
Project description
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 |
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Source Distribution
Built Distribution
Hashes for deepaccess-0.1.3-py3-none-any.whl
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| MD5 | 2a739c29509ddd4753d15c3433d74e2c |
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| BLAKE2b-256 | ec2f18e3a3447aa9c9444cdfe457f8f20eb2c4151b969340e426e17f1c241655 |
