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/
It's tested on Linux and Macs. It may compile and run on other UNIX systems.
If you have questions or suggestions, mail us at [email protected], or file a GitHub issue.
Ataqv is now published in Cell Systems: https://doi.org/10.1016/j.cels.2020.02.009
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.
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.
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
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
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
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.
If HTSlib was built to use libcurl, you'll need to link with that as well:
make HTSLIBCURL=yes
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.
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.
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.
--tabular-output
If given, the metrics file output will be a tabular (TSV) text file, not JSON. This
output CANNOT be used to generate the HTML report, and excludes several metrics that
would otherwise be included in the JSON output (e.g., the full fragment length
distribution, the full TSS coverage curve, and the full mapping quality distribution).
This option is not recommended when analyzing bulk ATAC-seq data, but may be useful
when analyzing single nucleus ATAC-seq data with large numbers of distinct cell
barcodes (say, >100k); in such a case this option should substantially reduce memory
usage, reduce runtime, and avoid the need to parse a large JSON file in downstream
analysis, while still outputting the metrics commonly used to QC single nucleus
ATAC-seq data (TSS enrichment, read counts, and mitochondrial read counts, amongst others).
--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.
--nucleus-barcode-tag "nucleus_barcode_tag"
Data is single-nucleus, with the barcode stored in this BAM tag.
In this case, metrics will be collected per barcode.
--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
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
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.
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.