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ataqv: ATAC-seq QC and visualization

What is it?

A toolkit for measuring and comparing ATAC-seq results, made in the Parker lab at the University of Michigan. We wrote it to help us understand how well our ATAC-seq assays had worked, and to make it easier to spot differences that might be caused by library prep or sequencing.

The main program, ataqv, examines your aligned reads and reports some basic metrics, including:

  • reads mapped in proper pairs
  • optical or PCR duplicates
  • reads mapping to autosomal or mitochondrial references
  • the ratio of short to mononucleosomal fragment counts
  • mapping quality
  • various kinds of problematic alignments

If you also have a file of peaks called on your data, that file can be examined to report read coverage of the peaks.

With a file of transcription start sites, ataqv can report a TSS enrichment metric based on the transposition activity around those locations.

The report is printed as plain text to standard output, and detailed metrics are written to JSON files for further processing.

A web-based visualization and comparison tool and a script to prepare the JSON output for it are also provided. The web viewer includes interactive tables of the metrics and plots of fragment length, distance from a fragment length reference distribution, mapping quality, counts of reads overlapping peaks, and peak territory.

Web viewer demo: https://parkerlab.github.io/ataqv/demo/

Where does it run?

It's tested on Linux and Macs. It may compile and run on other UNIX systems.

Help

If you have questions or suggestions, mail us at parkerlab-software@umich.edu, or file a GitHub issue.

Citing

Ataqv is now published in Cell Systems: https://doi.org/10.1016/j.cels.2020.02.009

Getting started

There are several ways to get ataqv running on your system: install a binary package; install it with Homebrew or Linuxbrew; or build it from source.

Binary packages (Linux only)

We provide several Linux binary packages under recent releases on Github. Install .deb files with dpkg, .rpm files with dnf or yum, or download and extract the ataqv-x.x.x.tar.gz file and add the full path to the resulting ataqv-x.x.x/bin subdirectory to your PATH environment variable.

Homebrew (Mac or Linux)

The easiest way to install ataqv from source is via Homebrew on Macs, or Linuxbrew on Linux, using our tap. At a shell prompt:

brew tap ParkerLab/tap
brew install ataqv

Building from source manually

Prerequisites

To build ataqv, you need:

  • Linux or a Mac (it may work on other UNIX systems, but it's untested)
  • C++11 compiler (gcc 4.9 or newer, or clang on OS X)
  • Boost
  • HTSlib

The mkarv script that collects ataqv results and sets up a web application to visualize them requires Python 2.7 or newer.

To run the test suite, you'll also need LCOV, which can be installed via Homebrew or Linuxbrew.

On Debian-based Linux distributions, you can install dependencies with:

sudo apt install libboost-all-dev libhts-dev ncurses-dev libtinfo-dev zlib1g-dev lcov

and the latest supported option among:

sudo apt install libstdc++-6-dev
sudo apt install libstdc++-5-dev
sudo apt install libstdc++-4.9-dev

Building

At your shell prompt:

git clone https://github.com/ParkerLab/ataqv
cd ataqv
make

If Boost and htslib are not available in default system locations (for example if you're using environment modules, or compiling in your home directory) you'll probably need to give make some hints via the CPPFLAGS and LDFLAGS variables:

make CPPFLAGS="-I/path/to/boost/include -I/path/to/htslib/include" LDFLAGS="-L/path/to/boost/lib -L/path/to/htslib/lib"

If the environment variables BOOST_ROOT or HTSLIB_ROOT are set to directories containing include and lib subdirectories, the compiler configuration can be made simpler:

make BOOST_ROOT=/path/to/boost HTSLIB_ROOT=/path/to/htslib

Or you can specify directories in BOOST_INCLUDE, BOOST_LIB, HTSLIB_INCLUDE, and HTSLIB_LIB separately.

If you use custom locations like this, you will probably need to set LD_LIBRARY_PATH for the shared libraries to be found at runtime:

export LD_LIBRARY_PATH=/path/to/boost/lib:/path/to/htslib/lib:$LD_LIBRARY_PATH

Dependency notes

Boost

If your Boost installation used their "tagged" layout, the libraries will include metadata in their names; on Linux this usually just means that they'll have a -mt suffix to indicate multithreading support. Specify BOOST_TAGGED=yes in your make commands to link with those.

HTSlib

If HTSlib was built to use libcurl, you'll need to link with that as well:

make HTSLIBCURL=yes

Installation

The Makefile supports the common DESTDIR and prefix variables. To install to /usr/local:

make install prefix=/usr/local

Support for the Environment Modules system is also included. You can install to the modules tree by defining the MODULES_ROOT and MODULEFILES_ROOT variables. If your modules are kept under /opt/modules, with their accompanying module files under /opt/modulefiles, run:

make install-module MODULES_ROOT=/opt/modules MODULEFILE_ROOT=/opt/modulefiles

And then you should be able to run module load ataqv to have everything available in your environment.

Usage

Prerequisites

You'll need to have a BAM file containing alignments of your ATAC-seq reads to your reference genome. If you want accurate duplication metrics, you'll also need to have marked duplicates in that BAM file. If you have a BED file containing peaks called on your data, ataqv can produce some additional metrics using that.

Verifying ataqv results with data from a variety of common tools is on our to-do list, but so far, we've only used bwa, Picard's MarkDuplicates, and MACS2 for these steps. A pipeline like ours can be generated with the included make_ataqv_pipeline script. Its output product starts from a BAM file of aligned reads, marks duplicates and calls peaks, then runs ataqv and produces a web viewer for the output.

Running

The main program is ataqv, which is run as follows:

ataqv [options] organism alignment-file

where:
    organism is the subject of the experiment, which determines the list of autosomes
    (see "Reference Genome Configuration" below).

    alignment-file is a BAM file with duplicate reads marked.

Basic options
-------------

--help: show this usage message.
--verbose: show more details and progress updates.
--version: print the version of the program.
--threads <n>: the maximum number of threads to use (right now, only for calculating TSS enrichment).

Optional Input
--------------

--peak-file "file name"
    A BED file of peaks called for alignments in the BAM file. Specify "auto" to use the
    BAM file name with ".peaks" appended, or if the BAM file contains read groups, to
    assume each read group has a peak file whose name is the read group ID with ".peaks"
    appended. If you specify a single filename instead of "auto" with read groups, the
    same peaks will be used for all reads -- be sure this is what you want.

--tss-file "file name"
    A BED file of transcription start sites for the experiment organism. If supplied,
    a TSS enrichment score will be calculated according to the ENCODE data standards.
    This calculation requires that the BAM file of alignments be indexed.

--tss-extension "size"
    If a TSS enrichment score is requested, it will be calculated for a region of
    "size" bases to either side of transcription start sites. The default is 1000bp.

--excluded-region-file "file name"
    A BED file containing excluded regions. Peaks or TSS overlapping these will be ignored.
    May be given multiple times.

Output
------

--metrics-file "file name"
    The JSON file to which metrics will be written. The default filename will be based on
    the BAM file, with the suffix ".ataqv.json".

--log-problematic-reads
    If given, problematic reads will be logged to a file per read group, with names
    derived from the read group IDs, with ".problems" appended. If no read groups
    are found, the reads will be written to one file named after the BAM file.

--less-redundant
    If given, output a subset of metrics that should be less redundant. If this flag is used,
    the same flag should be passed to mkarv when making the viewer.

Metadata
--------

The following options provide metadata to be included in the metrics JSON file.
They make it easier to compare results in the ataqv web interface.

--name "name"
  A label to be used for the metrics when there are no read groups. If there are read
  groups, each will have its metrics named using its ID field. With no read groups and
  no --name given, your metrics will be named after the alignment file.

--ignore-read-groups
  Even if read groups are present in the BAM file, ignore them and combine metrics
  for all reads under a single sample and library named with the --name option. This
  also implies that a single peak file will be used for all reads; see the --peak option.

--description "description"
  A short description of the experiment.

--url "URL"
  A URL for more detail on the experiment (perhaps using a DOI).

--library-description "description"
  Use this description for all libraries in the BAM file, instead of using the DS
  field from each read group.

Reference Genome Configuration
------------------------------

ataqv includes lists of autosomes for several organisms:

  Organism  Autosomal References
   -------  ------------------
       fly  2R 2L 3R 3L 4
     human  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
     mouse  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
       rat  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
      worm  I II III IV V
     yeast  I II III IV V VI VII VIII IX X XI XII XIII XIV XV XVI

  The default autosomal reference lists contain names with "chr" prefixes
  ("chr1") and without ("1"). If you need a different set of autosomes, you can
  supply a list with --autosomal-reference-file.

--autosomal-reference-file "file name"
  A file containing autosomal reference names, one per line. The names must match
  the reference names in the alignment file exactly, or the metrics based on counts
  of autosomal alignments will be wrong.

--mitochondrial-reference-name "name"
  If the reference name for mitochondrial DNA in your alignment file is not "chrM",.
  use this option to supply the correct name. Again, if this name is wrong, all the
  measurements involving mitochondrial alignments will be wrong.

When run, ataqv prints a human-readable summary to its standard output, and writes complete metrics to the JSON file named with the --metrics-file option.

The JSON output can be incorporated into a web application that presents tables and plots of the metrics, and makes it easy to compare results across samples or experiments. Use the mkarv script to create a local instance of the result viewer (run mkarv -h for complete instructions). A web server is not required, though you can use one to publish your result viewer instance.

Given several BAM files (mapped to hg19) and accompanying broadPeak files (along with hg19 TSS files and blacklist), an example workflow might be:

$ # first, run ataqv on each bam file to generate JSON files as well as human-readable output
$ ataqv --peak-file /lab/work/porchard/atacseq/macs2/sample_1_peaks.broadPeak --name sample_1 --metrics-file /lab/work/porchard/atacseq/ataqv/sample_1.ataqv.json.gz --excluded-region-file /lab/work/porchard/atacseq/data/mappability/hg19.blacklist.bed.gz --tss-file /lab/work/porchard/atacseq/data/tss/hg19.tss.refseq.bed.gz --ignore-read-groups human /lab/work/porchard/atacseq/mark_duplicates/sample_1.md.bam > /lab/work/porchard/atacseq/ataqv/sample_1.ataqv.out
$ ataqv --peak-file /lab/work/porchard/atacseq/macs2/sample_2_peaks.broadPeak --name sample_2 --metrics-file /lab/work/porchard/atacseq/ataqv/sample_2.ataqv.json.gz --excluded-region-file /lab/work/porchard/atacseq/data/mappability/hg19.blacklist.bed.gz --tss-file /lab/work/porchard/atacseq/data/tss/hg19.tss.refseq.bed.gz --ignore-read-groups human /lab/work/porchard/atacseq/mark_duplicates/sample_2.md.bam > /lab/work/porchard/atacseq/ataqv/sample_2.ataqv.out
$ ataqv --peak-file /lab/work/porchard/atacseq/macs2/sample_3_peaks.broadPeak --name sample_3 --metrics-file /lab/work/porchard/atacseq/ataqv/sample_3.ataqv.json.gz --excluded-region-file /lab/work/porchard/atacseq/data/mappability/hg19.blacklist.bed.gz --tss-file /lab/work/porchard/atacseq/data/tss/hg19.tss.refseq.bed.gz --ignore-read-groups human /lab/work/porchard/atacseq/mark_duplicates/sample_3.md.bam > /lab/work/porchard/atacseq/ataqv/sample_3.ataqv.out
$
$ # run mkarv on the JSON files to generate the interactive web viewer (in this case, SRR891268 will be used as the reference sample in the viewer):
$ mkarv my_fantastic_experiment /lab/work/porchard/atacseq/ataqv/sample_1.ataqv.json.gz /lab/work/porchard/atacseq/ataqv/sample_2.ataqv.json.gz /lab/work/porchard/atacseq/ataqv/sample_3.ataqv.json.gz
$
$ # to see the viewer, open the file my_fantastic_experiment/index.html in your web browser

Example

The ataqv package includes a script that will set up and run our entire ATAC-seq pipeline on some sample data.

You'll need to have installed ataqv itself, plus Picard tools, samtools, and MACS2 to run the pipeline. On a Mac, you can obtain everything with:

$ brew install ataqv picard-tools samtools
$ pip install MACS2

On Linux, installation of the dependencies is probably specific to your environment and is left as an exercise for the reader. On Debian, apt-get install picard-tools samtools followed by installing MACS2 with pip install MACS2 should be enough.

Once you have the prerequisite programs installed, you can run the example pipeline with:

$ run_ataqv_example /output/path

Comparing your results to others

Part of this project will be publishing ataqv output for as many ATAC-seq experiments as we can get our hands on, so we can compare them and learn how changes to the protocol affect the output. Watch our GitHub docs for updates.

Performance

It's not currently concurrent, so don't allocate it more than a single processor. Memory usage should typically be no more than a few hundred megabytes.

Anecdotally, processing a 41GB BAM file containing 1,126,660,186 alignments of the data from the ATAC-seq paper took just under 20 minutes and 2GB of memory. Adding peak metrics extended the run time to almost 40 minutes, but it still used the same amount of memory.