maxATAC

Transcription Factor Binding Prediction from ATAC-seq and scATAC-seq with Deep Neural Networks

View the Project on GitHub MiraldiLab/maxATAC

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maxATAC: genome-scale transcription-factor binding prediction from ATAC-seq with deep neural networks

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Introduction

maxATAC is a Python package for transcription factor (TF) binding prediction from ATAC-seq signal and DNA sequence in human cell types. maxATAC works with both population-level (bulk) ATAC-seq and pseudobulk ATAC-seq profiles derived from single-cell (sc)ATAC-seq. maxATAC makes TF binding site (TFBS) predictions at 32 bp resolution. maxATAC requires three inputs:

maxATAC was trained and evaluated on data generated using the hg38 reference genome. The default paths and files that are used for each function will reference hg38 files. If you want to use maxATAC with any other species or reference, you will need to provide the appropriate chromosome sizes file, blacklist, and .2bit file specific to your data.


Installation

It is best to install maxATAC into a dedicated virtual environment.

This version requires python 3.9, bedtools, samtools, pigz, wget, git, graphviz, and bedGraphToBigWig in order to run all functions.

The total install requirements for maxATAC with reference data are ~2 GB.

Installing with Conda

  1. Create a conda environment for maxATAC with conda create -n maxatac -c bioconda python=3.9 samtools wget bedtools ucsc-bedgraphtobigwig pigz

If you get an error installing ucsc-bedgraphtobigwig try conda install -c bioconda ucsc-bedgraphtobigwig

If you get an error regarding graphviz while training a model, re-install graphviz with conda install graphviz

  1. Install maxATAC with pip install maxatac

  2. Test installation with maxatac -h

  3. Download reference data with maxatac data

If you have an error related to pybigwig, reference issues: 96 and 87

Installing with python virtualenv

  1. Create a virtual environment for maxATAC with virtualenv -p python3.9 maxatac.

  2. Install required packages and make sure they are on your PATH: samtools, bedtools, bedGraphToBigWig, wget, git, pigz.

  3. Install maxatac with pip install maxatac

  4. Test installation with maxatac -h

  5. Download reference data with maxatac data

Downloading required reference data

In order to run the maxATAC models that were described in the maxATAC pre-print, the following files are required to be downloaded from the maxATAC_data repository and installed in the correct directory:

The easiest option is to use the command maxatac data to download the data to the required directory. The maxatac data function will download the maxATAC_data repo and reference data into your ~/opt/ directory under ~/opt/maxatac. Only the hg38 reference genome has been extensively tested.

Using custom reference data

The directory ~/opt/maxatac/data is the default location where maxATAC will look for the maxATAC models, hg38 reference annotations, etc.

If you want to use your own references (e.g., hg19) or models, set the appropriate flags for each file with the path to your custom files. You can also adjust the relative paths in constants.py to be the default values for all functions.


maxATAC Quick Start Overview

maxATAC Quick Start Overview

Schematic: Overview of a typical maxATAC workflow. First, ATAC-seq data is prepared using the maxatac prepare function. The prepare function processes bulk and scATAC-seq into normalized signal files. The normalized signal track can then be used to make TF binding predictions for the TF of interest. The IGV screenshot shows the maxATAC-normalized ATAC-seq signal (blue) and maxATAC TFBS predictions for the FOXP1 model (magenta), predictions are represented as signal tracks (.bw, bigwig) and TFBS (.bed files), the default outputs from maxATAC.

Inputs

Outputs

ATAC-seq Data Requirements

As described in the maxATAC pre-print, maxATAC processing of ATAC-seq signal is critical to maxATAC prediction. Key maxATAC processing steps, summarized in a single command maxatac prepare, include identification of Tn5 cut sites from ATAC-seq fragments, ATAC-seq signal smoothing, filtering with an extended “maxATAC” blacklist, and robust, min-max-like normalization.

The maxATAC models were trained on paired-end ATAC-seq data in human. For this reason, we recommend paired-end sequencing with sufficient sequencing depth (e.g., ~20M reads for bulk ATAC-seq). Until these models are benchmarked in other species, we recommend limiting their use to human ATAC-seq datasets.

Preparing the ATAC-seq signal

The current maxatac predict function requires a normalized ATAC-seq signal in a bigwig format. Use maxatac prepare to generate a normalized signal track from a .bam file of aligned reads. See the prepare documentation for more details about the expected outputs and file name descriptions.

Bulk ATAC-seq

The function maxatac prepare was designed to take an input BAM file that has aligned to the hg38 reference genome. The inputs to maxatac prepare are the input bam file, the output directory, and the filename prefix.

maxatac prepare -i SRX2717911.bam -o ./output -prefix SRX2717911 -dedup

This function took 38 minutes for a sample with 52,657,164 reads in the BAM file. This was tested on a 2019 Macbook Pro with a 2.6 GHz 6-Core Intel Core i7 and 16 GB of memory.

Pseudo-bulk scATAC-seq

First, convert the .tsv.gz output fragments file from CellRanger into pseudo-bulk specific fragment files. Then, use maxatac prepare with each of the fragment files in order to generate a normalized bigwig file for input into maxatac predict.

maxatac prepare -i HighLoading_GM12878.tsv -o ./output -prefix HighLoading_GM12878

The prediction parameters and steps are the same for scATAC-seq data after normalization.

Predicting TF binding from ATAC-seq

Following maxATAC-specific processing of ATAC-seq signal inputs, use the maxatac predict function to predict TF binding with a maxATAC model.

TF binding predictions can be made genome-wide, for a single chromosome, or, alternatively, the user can provide a .bed file of genomic intervals for maxATAC predictions to be made.

Whole genome prediction

Example command for TFBS prediction across the whole genome:

maxatac predict -tf CTCF --signal GM12878_IS_slop20_RP20M_minmax01.bw -o outputdir/

If data has been installed with maxATAC data, then the following command will use the best model and call peaks using the TF specific threshold statistics.

maxatac predict -tf CTCF -s GM12878_IS_slop20_RP20M_minmax01.bw  -o outputdir/

Prediction in a specific genomic region(s)

For TFBS predictions within specific regions of the genome, a BED file of genomic intervals, roi (regions of interest) are supplied:

maxatac predict -tf CTCF --signal GM12878_IS_slop20_RP20M_minmax01.bw  --roi ROI.bed

Prediction on a specific chromosome(s)

For TFBS predictions on a single chromosome or subset of chromosomes, these can be provided using the --chromosomes argument:

maxatac predict -tf CTCF --signal GM12878_IS_slop20_RP20M_minmax01.bw  --chromosomes chr3 chr5

Raw signal tracks (prediction bigwigs) are large

Each output prediction file for a whole genome is ~700 MB per TF.

The output bed files are ~60Mb.

There are 127 TF models x ~700MB per TF model = ~88.9 GB of bigwig files for a single ATAC-seq input track. (Note: it only makes sense to generate maxATAC predicitons for TFs expressed in your cell type / conditions of interest, so this is a worst-case estimate.)


maxATAC functions

Subcommand Description
prepare Prepare input data
average Average ATAC-seq signal tracks
normalize Minmax normalize ATAC-seq signal tracks
train Train a model
predict Predict TF binding
benchmark Benchmark maxATAC predictions against ChIP-seq
peaks Call “peaks” on maxATAC signal tracks
variants Predict sequence specific TF binding

Publication

The maxATAC pre-print is currently available on bioRxiv.

maxATAC: genome-scale transcription-factor binding prediction from ATAC-seq with deep neural networks
Tareian Cazares, Faiz W. Rizvi, Balaji Iyer, Xiaoting Chen, Michael Kotliar, Joseph A. Wayman, Anthony Bejjani, Omer Donmez, Benjamin Wronowski, Sreeja Parameswaran, Leah C. Kottyan, Artem Barski, Matthew T. Weirauch, VB Surya Prasath, Emily R. Miraldi
bioRxiv 2022.01.28.478235; doi: https://doi.org/10.1101/2022.01.28.478235