ATAC-Seq / DNase-Seq Pipeline
This pipeline is designed for automated end-to-end quality control and processing of ATAC-seq or DNase-seq data. The pipeline can be run on compute clusters with job submission engines or stand alone machines. It inherently makes uses of parallelized/distributed computing. Pipeline installation is also easy as most dependencies are automatically installed. The pipeline can be run end-to-end i.e. starting from raw FASTQ files all the way to peak calling and signal track generation; or can be started from intermediate stages as well (e.g. alignment files). The pipeline supports single-end or paired-end ATAC-seq or DNase-seq data (with or without replicates). The pipeline produces pretty HTML reports that include quality control measures specifically designed for ATAC-seq and DNase-seq data, analysis of reproducibility, stringent and relaxed thresholding of peaks, fold-enrichment and pvalue signal tracks. The pipeline also supports detailed error reporting and easy resuming of runs. The pipeline has been tested on human, mouse and yeast ATAC-seq data and human and mouse DNase-seq data.
The ATAC-seq pipeline specification is also the official pipeline specification of the Encyclopedia of DNA Elements (ENCODE) consortium. The ATAC-seq pipeline protocol definition is here. Some parts of the ATAC-seq pipeline were developed in collaboration with Jason Buenrostro, Alicia Schep and Will Greenleaf at Stanford.
The DNase-seq pipeline specification is here. Note that this is NOT the same as the official ENCODE DNase-seq pipeline (which is based on John Stam lab's processing pipeline).
- Go to Genomic pipelines in Kundaje lab
- Go to Discussion channel
- Jump to Usage
- Jump to Output directory structure and file naming
- Jump to ENCODE accession guideline
- Jump to Troubleshooting
Install software/database in a correct order according to your system. For example on Kundaje lab's clusters, you only need to install one software Pipeline.
Kundaje lab's clusters
Stanford NEW SCG cluster
Stanford OLD SCG cluster
Stanford Sherlock cluster
Install Java 8 (jdk >= 1.8 or jre >= 1.8) on your system. If you don't have super-user privileges on your system, locally install it and add it to your
For Debian/Ubuntu (>14.10) based Linux:
$ sudo apt-get install git openjdk-8-jre
For Fedora/Red-Hat based Linux:
$ sudo yum install git java-1.8.0-openjdk
For Ubuntu 14.04 (trusty):
$ sudo add-apt-repository ppa:webupd8team/java -y $ sudo apt-get update $ sudo apt-get install oracle-java8-installer
REMOVE ANY ANACONDA OR OTHER VERSIONS OF CONDA FROM YOUR BASH STARTUP SCRIPT. WE CANNOT GUARANTEE THAT PIPELINE WORKS WITH OTHER VERSIONS OF CONDA. ALSO REMOVE R AND OTHER CONFLICTING MODULES FROM IT TOO. Remove any other Anaconda from your
$PATH. Check your loaded modules with
$ module list and unload any Anaconda modules in your bash startup scripts (
unset PYTHONPATH to your bash start up scripts.
$ wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh $ bash Miniconda3-latest-Linux-x86_64.sh
yes for the final question. If you choose
no, you need to manually add Miniconda3 to your
Do you wish the installer to prepend the Miniconda3 install location to PATH in your /your/home/.bashrc ? [yes|no] [no] >>> yes
Open a new terminal after installation.
$ wget https://github.com/leepc12/BigDataScript/blob/master/distro/bds_Linux.tgz?raw=true -O bds_Linux.tgz $ mv bds_Linux.tgz $HOME && cd $HOME && tar zxvf bds_Linux.tgz
export PATH=$PATH:$HOME/.bds to your
$HOME/.bashrc. If Java memory occurs, add
export _JAVA_OPTIONS="-Xms256M -Xmx728M -XX:ParallelGCThreads=1" too.
Get the latest version of the pipeline. Don't forget to add
--recursive. ATAC-Seq pipeline uses modules from an external git repo (ataqc). ATAQC will not work correctly without
$ git clone https://github.com/kundajelab/atac_dnase_pipelines --recursive
Install software dependencies automatically. It will create two conda environments (bds_atac and bds_atac_py3) under your conda.
$ bash install_dependencies.sh
If you don't use
install_dependencies.sh, manually replace BDS's default
bds.config with a correct one:
$ cp bds.config ./utils/bds_scr $HOME/.bds
install_dependencies.sh fails, run
./uninstall_dependencies.sh, fix problems and then try
bash install_dependencies.sh again.
Install genome data for a specific genome
mm10 are available. Specify a directory
[DATA_DIR] to download genome data. A species file generated on
[DATA_DIR] will be automatically added to your
./default.env so that the pipeline knows that you have installed genome data using
install_genome_data.sh. If you want to install multiple genomes make sure that you use the same directory
[DATA_DIR] for them. Each genome data will be installed on
[DATA_DIR]/[GENOME]. If you use other BDS pipelines, it is recommended to use the same directory
[DATA_DIR] to save disk space.
install_genome_data.sh can take longer than an hour for downloading data and building index. DO NOT run the script on a login node, use
qlogin for SGE and
srun --pty bash for SLURM.
# DO NOT run this on a login node $ bash install_genome_data.sh [GENOME] [DATA_DIR]
If you have super-user privileges on your system, it is recommended to install genome data on
/your/data/bds_pipeline_genome_data and share them with others.
$ sudo su $ bash install_genome_data.sh [GENOME] /your/data/bds_pipeline_genome_data
You can find a species file
/your/data/bds_pipeline_genome_data for each pipeline type. Then others can use the genome data by adding
-species_file [SPECIES_FILE_PATH] to the pipeline command line. Or they need to add
species_file = [SPECIES_FILE_PATH] to the section
[default] in their
Installation for internet-free computers
The pipeline does not need internet connection but installers (
install_genome_data.sh) do need it. So the workaround should be that first install dependencies and genome data on a computer that is connected to the internet and then move Conda and genome database directories to your internet-free one. Both computers should have THE SAME LINUX VERSION.
On your computer that has an internet access,
- Follow the installation instruction for general computers
- Move your Miniconda3 directory to
$HOME/miniconda3on your internet-free computer.
- Move your genome database directory, which has
bds_atac_species.confand directories per species, to
$HOME/genome_dataon your internet-free computer.
$HOME/genome_dataon your internet-free computer should have
- Move your BDS directory
$HOME/.bdson your internet-free computer.
- Move your pipeline directory
$HOME/atac_dnase_pipelines/on your internet-free computer.
On your internet-free computer,
miniconda3/binand BDS binary to
$PATHin your bash initialization script (
export PATH="$PATH:$HOME/miniconda3/bin" export PATH="$PATH:$HOME/.bds"
[default] conda_bin_dir=$HOME/miniconda3/bin species_file=$HOME/genome_data/bds_atac_species.conf
Modify all paths in
$HOME/genome_data/bds_atac_species.confso that they correctly point to the right files.
Check BDS version.
$ bds -version Bds 0.99999e (build 2016-08-26 06:34), by Pablo Cingolani
Make sure that your java rumtime version is >= 1.8.
$ java -version java version "1.8.0_111" Java(TM) SE Runtime Environment (build 1.8.0_111-b14) Java HotSpot(TM) 64-Bit Server VM (build 25.111-b14, mixed mode)
We recommend using BASH to run pipelines.
For general use, use the following command line: (for PE data set)
$ bds atac.bds -species [SPECIES; hg19, mm9, ... ] -nth [NUM_THREADS] -fastq1_1 [READ1] -fastq1_2 [READ2]
For DNase-seq: (it's NOT
The pipeline does not trim adapters by default. To automatically detect adapters,
To specify adapter for each fastq, add
-adapter[REPLICATE_ID]_[PAIR_ID]:[POOL_ID] for paired end dataset and
-adapter[REPLICATE_ID]:[POOL_ID] for single ended one.
You can skip
:[POOL_ID] if you have single fastq per replicate (SE) or single pair of fastqs per replicate (PE).
IMPORTANT! If your data set is SINGLE ENDED add the following to the command line, otherwise the pipeline works for PE by default.
You can also individually specify endedness for each replicate.
-se[REPLICATE_ID] # for exp. replicates,
-se1 -pe2 -se3 ...
If you want to just align your data (no peak calling or further steps like IDR).
If you don't want ATAQC, add the following to command line.
If you have just one replicate (PE), specify fastqs with
-fastq1_1 [READ_PAIR1] -fastq1_2 [READ_PAIR2] \
For multiple replicates (PE), specify fastqs with
-fastq_ for each replicate and pair to the command line:replicates.
-fastq1_1 [READ_REP1_PAIR1] -fastq1_2 [READ_REP1_PAIR2] -fastq2_1 [READ_REP2_PAIR1] -fastq2_2 [READ_REP2_PAIR2] ...
For multiple replicates (SE), specify fastqs with
-se -fastq1 [READ_REP1] -fastq2 [READ_REP2] ...
You can also specify an adapter to be trimmed for each fastq. Example:
-adapter1_1 [ADAPTER1_1] -adapter1_2 [ADAPTER1_2] ... for PE or
-adapter1 [ADAPTER1] -adapter2 [ADAPTER2] ....
You can start from bam files. There are two kinds of bam files (raw or deduped) and you need to explicitly choose between raw bam (bam) and deduped one (filt_bam) with
-input [BAM_TYPE]. Don't forget to add
-se if they are not paired end (PE). For raw bams,
-bam1 [RAW_BAM_REP1] -bam2 [RWA_BAM_REP1] ...
For deduped (filtered) bams:
-filt_bam1 [FILT_BAM_REP1] -filt_bam2 [FILT_BAM_REP1] ...
For tagaligns (non-tn5-shifted):
-tag1 [TAGALIGN_REP1] -tag2 [TAGALIGN_REP2] ...
You can also mix up any data types.
-bam1 [RAW_BAM_REP1] -tag2 [TAGALIGN_REP2] ...
To subsample beds (tagaligns) add the following to the command line. This is different from subsampling for cross-corr. analysis. Peaks will be called with subsampled tagaligns.
To change # of lines to subsample for cross-corr. analysis. This will not affect tasks downstream (peak calling and IDR).
To disable pseudo replicate generation, add the following. By default, peak calling and IDR will be done for true replicates and pseudo replicates, but if you have
-true_rep in the command line, you will also get IDR on true replicates only.
IDR analysis is optional in the pipeline by default. If there are more than two replicates, IDR will be done for every pair of replicates. to enable IDR add the following:
For multimapping, (multimapping is disabled by default). Using this parameter implies
-mapq_thresh 30 (MapQ Thresh for the pipeline is fixed at 30).
-multimapping [MULTIMAPPING; use 4 for ENCODE samples]
To force a set of parameters (
-smooth_win 73 -idr_thresh 0.05 -multimapping 4) for ENCODE3.
You can also define parameters in a configuration file. Key names in a configruation file are identical to parameter names on command line.
$ bds atac.bds [CONF_FILE] $ cat [CONF_FILE] species = [SPECIES; hg19, mm9, ...] nth = [NUM_THREADS] fastq1_1= [READ1] fastq1_2= [READ2] ...
List of all parameters
To list all parameters and default values for them,
$ bds atac.bds
Stopping / Resuming pipeline
Press Ctrl + C on a terminal or send any kind of kill signals to it. Please note that this will delete all intermediate files and incomplete outputs for the running tasks. The pipeline automatically determines if each task has finished or not (by comparing timestamps of input/output files for each task). To run the pipeline from the point of failure, correct error first and then just run the pipeline with the same command that you started the pipeline with. There is no additional parameter for restarting the pipeline.
Running pipelines with a cluster engine
On servers with a cluster engine (such as Sun Grid Engine and SLURM), DO NOT QSUB/SBATCH BDS COMMAND LINE. Run BDS command directly on login nodes. BDS is a task manager and it will automatically submit(qsub/sbatch) and manage its sub tasks.
IMPORTANT! Please read this section carefully if you run pipelines on Stanford SCG4 and Sherlock cluster.
Most clusters have a policy to limit number of threads and memory per user on a login node. One BDS process, as a Java-based task manager, takes up to 1GB of memory and 50 threads even though it just submits/monitors subtasks. So if you want to run more than 50 pipelines in parallel, your cluster will kill BDS processes due to resource limit on a login node (check resource limit per user with
ulimit -a). For example of 50 pipelines, 50 GB of memory and 2500 threads will be taken by 50 BDS processes. So the Workaround for this is to make an interactive node to keep all BDS processes alive. Such interactive node must have long walltime enough to wait for all pipelines in it to finish. Recommended resource setting is 0.5GB memory per pipeline.
SGE example to make an interactive node for 100 pipelines: 1 cpu, 100GB memory, 3 days walltime.
$ qlogin -l h_rt=72:00:00 -l h_vmem=100G
SLURM example to make an interactive node for 100 pipelines: 1 cpus, 100GB memory, 3 days walltime.
$ srun -n 1 --mem 100G -t 3-0 -p [YOUR_PARTITON] --qos normal --pty /bin/bash -i -l
Once you get an interactive node, repeat the following commands per sample to run a pipeline with using
-q_for_slurm_account to the command line to use the parameter
-q for SLURM account (
sbatch --acount) instead of partition (
$ cd [WORK_DIR] $ bds_scr [SCREEN_NAME] [LOG_FILE_PATH] atac.bds -system [CLUSTER_ENGINE: slurm or sge] -q [SGE_QUEUE_OR_SLURM_PARTITION] -nth [MAX_NUM_THREAD_PER_PIPELINE] ... $ sleep 2 # wait for 2 seconds for safety
Then you can monitor your pipelines with
screen -ls and
tail -f [LOG_FILE_PATH]. If you want to run more than 200 pipelines, you would want to make multiple interactive nodes and distribute your samples to them.
For completely serialized jobs, add
-no_par to the command line. Individual tasks can still go multi-threaded.
IMPORTANT! You can set up a limit for total number of threads with
-nth [MAX_TOTAL_NO_THREADS]. Total number of threads used by a pipeline will not exceed this limit.
-nth for each cluster is defined on
./default.env (e.g. 16 on SCG and 8 on Kundaje lab cluster)
The pipeline automatically distributes
[MAX_TOTAL_NO_THREADS] threads for jobs according to corresponding input file sizes. For example of two fastqs (1GB and 2GB) with
-nth 6, 2 and 4 threads are allocated for aligning 1GB and 2GB fastqs, respectively. The same policy applies to other multi-threaded tasks like deduping and peak calling.
However, all multi-threaded tasks (like bwa, bowtie2, spp and macs2) still have their own max. memory (
-mem_APPNAME [MEM_APP]) and walltime (
-wt_APPNAME [WALLTIME_APP]) settings. Max. memory is NOT PER CPU. For example on Kundaje lab cluster (with SGE flag activated
bds -s sge bds_atac.bds ...) or on SCG4, the actual shell command submitted by BDS for each task is like the following:
qsub -V -pe shm [NTH_ALLOCATED_FOR_APP] -h_vmem=[MEM_APP]/[NTH_ALLOCATED_FOR_APP] -h_rt=[WALLTIME_APP] -s_rt=[WALLTIME_APP] ...
This ensures that total memory reserved for a cluster job equals to
[MEM_APP]. The same policy applies to SLURM.
Specifying a cluster queue/partition
You can specifiy a queue
[QUEUE_NAME] on Sun Grid Engine or partition/account on SLURM. But you cannot specify both account and partition at the same time for SLURM. You can skip
-q_for_slurm_account on Stanford SCG cluster since the pipeline will automatically detect SCG servers and add it.
bds atac.bds -system sge -q [SGE_QUEUE_NAME] ... bds atac.bds -system slurm -q [SLURM_PARTITON_NAME] ... # Sherlock example bds atac.bds -system slurm -q_for_slurm_account -q [SLURM_ACCOUNT_NAME] ... # SCG example
Managing multiple pipelines
./utils/bds_scr is a BASH script to create a detached screen for a BDS script and redirect stdout/stderr to a log file
[LOG_FILE_NAME]. If a log file already exists, stdout/stderr will be appended to it.
Monitor the pipeline with
tail -f [LOG_FILE_NAME]. The only difference between
bds is that you have
[SCR_NAME] [LOG_FILE_NAME] between
bds command and its parameters (or a BDS script name).
You can skip
[LOG_FILE_NAME] then a log file
[SCR_NAME].log will be generated on the working directory.
You can also add any BDS parameters (like
-s). The following example is for running a pipeline on Sun Grid Engine.
$ bds_scr [SCR_NAME] [LOG_FILE_NAME] atac.bds ... $ bds_scr [SCR_NAME] atac.bds ... $ bds_scr [SCR_NAME] -s sge atac.bds ...
Once the pipeline run is done, the screen will be automatically closed. To kill a pipeline manually while it's running, use
screen -X quit:
$ screen -X -S [SCR_NAME] quit $ kill_scr [SCR_NAME]
Java issues (memory and temporary directory)
Picard tools is used for marking dupes in the reads and it's based on Java. If you see any Java heap space errors then increase memory limit for Java with
-mem_ataqc [MEM] (default:
-mem_dedup [MEM] (default:
/tmp quickly fills up and you want to change temporary directory for all Java apps in the pipeline, then add the following line to your bash startup script (
$HOME/.bashrc). Our pipeline takes in
$TMP) for all Java apps.
Another quick workaround for dealing with Java issues is not to use Picard tools in the pipeline. Add
-use_sambamba_markdup to your command line and then you can use
sambamba markdup instead of
How to customize genome data installer?
Please refer to the section
Installer for genome data on BDS pipeline programming.
Useful HTML reports
There are two kinds of HTML reports provided by the pipeline.
BigDataScript HTML report for debugging: Located at the working folder with name atac_[TIMESTAMP]_report.html. This report is automatically generated by BigDataScript and is useful for debugging since it shows summary, timeline, Stdout and Stderr for each job.
ATAC-Seq pipeline report for QC and result: The pipeline automatically generate a nice HTML report (Report.html) on its output directory (specified with -out_dir or just './out'). It summarizes files and directory structure, includes QC reports and show a workflow diagram and genome browser tracks for peaks and signals (bigwigs for pValue and fold change). Move your output directory to a web directory (for example, /var/www/somewhere) or make a softlink of it to a web directory. For genome browser tracks, specify your web directory root for your output While keeping its structure. Make sure that you have bgzip and tabix installed on your system. Add the following to the command line:
-url_base http://your/url/to/output -title [PREFIX_FOR_YOUR_REPORT]
Output directory structure and file naming
For more details, refer to the file table section in an HTML report generated by the pipeline. Files marked as (E) are outputs to be uploaded during ENCODE accession.
out # root dir. of outputs │ ├ *report.html # HTML report ├ *tracks.json # Tracks datahub (JSON) for WashU browser ├ ENCODE_summary.json # Metadata of all datafiles and QC results │ ├ align # mapped alignments │ ├ rep1 # for true replicate 1 │ │ ├ *.trim.fastq.gz # adapter-trimmed fastq │ │ ├ *.bam # raw bam │ │ ├ *.nodup.bam (E) # filtered and deduped bam │ │ ├ *.tagAlign.gz # tagAlign (bed6) generated from filtered bam │ │ ├ *.tn5.tagAlign.gz # TN5 shifted tagAlign for ATAC pipeline (not for DNase pipeline) │ │ └ *.*M.tagAlign.gz # subsampled tagAlign for cross-corr. analysis │ ├ rep2 # for true repilicate 2 │ ... │ ├ pooled_rep # for pooled replicate │ ├ pseudo_reps # for self pseudo replicates │ │ ├ rep1 # for replicate 1 │ │ │ ├ pr1 # for self pseudo replicate 1 of replicate 1 │ │ │ ├ pr2 # for self pseudo replicate 2 of replicate 1 │ │ ├ rep2 # for repilicate 2 │ │ ... │ └ pooled_pseudo_reps # for pooled pseudo replicates │ ├ ppr1 # for pooled pseudo replicate 1 (rep1-pr1 + rep2-pr1 + ...) │ └ ppr2 # for pooled pseudo replicate 2 (rep1-pr2 + rep2-pr2 + ...) │ ├ peak # peaks called │ └ macs2 # peaks generated by MACS2 │ ├ rep1 # for replicate 1 │ │ ├ *.narrowPeak.gz # narrowPeak (p-val threshold = 0.01) │ │ ├ *.filt.narrowPeak.gz (E) # blacklist filtered narrowPeak │ │ ├ *.narrowPeak.bb (E) # narrowPeak bigBed │ │ ├ *.narrowPeak.hammock.gz # narrowPeak track for WashU browser │ │ ├ *.pval0.1.narrowPeak.gz # narrowPeak (p-val threshold = 0.1) │ │ └ *.pval0.1.*K.narrowPeak.gz # narrowPeak (p-val threshold = 0.1) with top *K peaks │ ├ rep2 # for replicate 2 │ ... │ ├ pseudo_reps # for self pseudo replicates │ ├ pooled_pseudo_reps # for pooled pseudo replicates │ ├ overlap # naive-overlapped peaks │ │ ├ *.naive_overlap.narrowPeak.gz # naive-overlapped peak │ │ └ *.naive_overlap.filt.narrowPeak.gz # naive-overlapped peak after blacklist filtering │ └ idr # IDR thresholded peaks │ ├ true_reps # for replicate 1 │ │ ├ *.narrowPeak.gz # IDR thresholded narrowPeak │ │ ├ *.filt.narrowPeak.gz (E) # IDR thresholded narrowPeak (blacklist filtered) │ │ └ *.12-col.bed.gz # IDR thresholded narrowPeak track for WashU browser │ ├ pseudo_reps # for self pseudo replicates │ │ ├ rep1 # for replicate 1 │ │ ... │ ├ optimal_set # optimal IDR thresholded peaks │ │ └ *.filt.narrowPeak.gz (E) # IDR thresholded narrowPeak (blacklist filtered) │ ├ conservative_set # optimal IDR thresholded peaks │ │ └ *.filt.narrowPeak.gz (E) # IDR thresholded narrowPeak (blacklist filtered) │ ├ pseudo_reps # for self pseudo replicates │ └ pooled_pseudo_reps # for pooled pseudo replicate │ │ │ ├ qc # QC logs │ ├ *IDR_final.qc # Final IDR QC │ ├ rep1 # for true replicate 1 │ │ ├ *.align.log # Bowtie2 mapping stat log │ │ ├ *.dup.qc # Picard (or sambamba) MarkDuplicate QC log │ │ ├ *.pbc.qc # PBC QC │ │ ├ *.nodup.flagstat.qc # Flagstat QC for filtered bam │ │ ├ *M.cc.qc # Cross-correlation analysis score for tagAlign │ │ ├ *M.cc.plot.pdf/png # Cross-correlation analysis plot for tagAlign │ │ └ *_qc.html/txt # ATAQC report │ ... │ ├ signal # signal tracks │ ├ macs2 # signal tracks generated by MACS2 │ │ ├ rep1 # for true replicate 1 │ │ │ ├ *.pval.signal.bigwig (E) # signal track for p-val │ │ │ └ *.fc.signal.bigwig (E) # signal track for fold change │ ... │ └ pooled_rep # for pooled replicate │ ├ report # files for HTML report └ meta # text files containing md5sum of output files and other metadata
ENCODE accession guideline
For each pipeline rune,
ENCODE_summary.json file is generated under the output directory (
-out_dir) for ENCODE accession (uploading pipeline outputs to the ENCODE portal). This JSON file includes all metadata and QC metrics required for ENCODE accession.
For ENCODE3, Please make sure that you run pipelines with
Parameters required for ENCODE accesssion:
# required -ENCODE_accession <string> : ENCODE experiment accession ID (or dataset). -ENCODE_award <string> : ENCODE award (e.g. /awards/U41HG007000/). -ENCODE_lab <string> : Lab (e.g. /labs/anshul-kundaje/) -ENCODE_assembly <string> : hg19, GRCh38, mm9, mm10. -ENCODE_alias_prefix <string> : Alias = Alias_prefix + filename # optional -ENCODE_award_rfa <string> : ENCODE award RFA (e.g. ENCODE3). -ENCODE_assay_category <string> : ENCODE assay category. -ENCODE_assay_title <string> : ENCODE assay title.
We also provide an ENCODE fastq downloader. It downloads fastqs matching award_rfa, assay_category and assay_title, and then automatically generate a shell script to run multiple pipelines. Such shell script also includes these ENCODE accession parameter set.
ENCODE accession spreadsheet (CSV) generation
./utils/parse_summary_ENCODE_accession_recursively.py recursively finds
ENCODE_summary.json files and parse them to generate one big CSV spreadsheet for ENCODE accession.
$ python ./utils/parse_summary_ENCODE_accession_recursively.py -h usage: ENCODE_summary.json parser for ENCODE accession [-h] [--out-file OUT_FILE] [--search-dir SEARCH_DIR] [--json-file JSON_FILE] [--sort-by-genome-and-exp] [--ignored-accession-ids-file IGNORED_ACCESSION_IDS_FILE] Recursively find ENCODE_summary.json, parse it and make a CSV for uploading to the ENCODE portal. Use https://github.com/ENCODE-DCC/pyencoded- tools/blob/master/ENCODE_submit_files.py for uploading. optional arguments: -h, --help show this help message and exit --out-file OUT_FILE Output CSV filename) --search-dir SEARCH_DIR Root directory to search for ENCODE_summary.json --json-file JSON_FILE Specify json file name to be parsed --sort-by-genome-and-exp Sort rows by genomes and ENCODE experiment accession ID --ignored-accession-ids-file IGNORED_ACCESSION_IDS_FILE Accession IDs in this text file will be ignored. (1 acc. ID per line)
QC metrics spreadsheet (TSV) generation
./utils/parse_summary_qc_recursively.py recursively finds
ENCODE_summary.json files and parse them to generate one big TSV spreadsheet for QC metrics.
$ python parse_summary_qc_recursively.py -h usage: ENCODE_summary.json parser for QC [-h] [--out-file OUT_FILE] [--search-dir SEARCH_DIR] [--json-file JSON_FILE] Recursively find ENCODE_summary.json, parse it and make a TSV spreadsheet of QC metrics. optional arguments: -h, --help show this help message and exit --out-file OUT_FILE Output TSV filename) --search-dir SEARCH_DIR Root directory to search for ENCODE_summary.json --json-file JSON_FILE Specify json file name to be parsed
Programming with BDS
See more troubleshootings
samtools ncurses bug
Prepend a directory for
install_dependencies.sh for solution.
samtools: symbol lookup error: /lib/x86_64-linux-gnu/libncurses.so.5: undefined symbol: _nc_putchar
Error: could not find environment: bds_atac
Unload any Anaconda Python modules. Remove locally installed Anaconda Python from your
Error: could not find environment: bds_atac
Unload any Anaconda Python modules. Remove locally installed Anaconda Python from your
Alternate Cloud-based Implementations
The Encyclopedia of DNA Elements (ENCODE) Project is in the process of adopting this pipeline for uniform processing of ENCODE ATAC-seq data. The official ENCODE implementation by the ENCODE Data Coordination Center will be an exact mirror of our pipeline on the DNAnexus cloud (i.e. results will be exactly reproducible). Note that using this service requires a user to pay for cloud compute time.
Epinomics provides an independent, free, cloud-based pipeline implementation that adheres to the analysis protocol specifications of our pipeline. This implementation can be accessed at https://open.epigenomics.co/#/encode.
Error: Java disk space error: Disk quota exceeded
This error happens when
/tmp is full so Java cannot write temporary files on it. You can specify Java temporary directory with the following paramter.
- Jin wook Lee - PhD Student, Mechanical Engineering Dept., Stanford University
- Chuan Sheng Foo - PhD Student, Computer Science Dept., Stanford University
- Daniel Kim - MD/PhD Student, Biomedical Informatics Program, Stanford University
- Nathan Boley - Postdoc, Dept. of Genetics, Stanford University
- Anshul Kundaje - Assistant Professor, Dept. of Genetics, Stanford University
We'd also like to acknowledge Jason Buenrostro, Alicia Schep and William Greenleaf who contributed prototype code for some parts of the ATAC-seq pipeline.