• XenoCP
  • Quick Start
  • Introduction to XenoCP
  • Reference Files
    • BWA for DNA Reads
    • STAR for RNA Reads
  • Local Usage without Docker
    • Prerequisites
    • Obtain and Build XenoCP
    • Inputs
    • Run
  • Local Usage with Docker
    • Build Docker image
    • Run
    • Singularity as a Docker alternative
  • WDL workflow
    • WDL reference files
    • Running WDL
  • Evaluate test data results
  • St. Jude Cloud
  • Availability
  • Seeking help
  • Citing XenoCP
  • Common Issues

XenoCP is a tool for cleansing mouse reads in xenograft BAMs. XenoCP can be easily incorporated into any workflow, as it takes a BAM file as input and efficiently cleans up the mouse contamination. The output is a clean human BAM file that could be used for downstream genomic analysis.

XenoCP can be run in the cloud on DNAnexus at https://platform.dnanexus.com/app/stjude_xenocp

The easiest way to get XenoCP running locally is using Docker, as docker build creates an image with all of the dependencies:

git clone https://github.com/stjude/XenoCP.git
cd XenoCP
docker build -t xenocp .

wget -r -np -R "index.html*" -nH --cut-dirs=3 https://ftp.stjude.org/pub/software/xenocp/reference/MGSCv37

# Test run on small dataset
mkdir results
docker run \
  --mount type=bind,source=$(pwd)/sample_data/input_data,target=/data,readonly \
  --mount type=bind,source=$(pwd)/reference,target=/reference,readonly \
  --mount type=bind,source=$(pwd)/results,target=/results \
  xenocp \
  /data/inputs.yml

XenoCP takes a BAM with xenograft reads mapped to the graft genome (e.g., human). It extracts aligned reads and remaps to the host genome (e.g., mouse) to determine whether the reads are from the host or graft. The output is a copy of the original BAM with host reads marked as unmapped.

XenoCP workflow:

XenoCP performs mapping against the host genome, so it requires indexes for the host reference genome and mapper being used.

A common use case is cleansing DNA reads with a mouse host. For this use case, you can download the a BWA index for MGSCv37 from https://ftp.stjude.org/pub/software/xenocp/reference/MGSCv37

To build your own reference files, first download the FASTA file for your genome assembly. Then, create the index for your mapper:

$ bwa index -p $FASTA $FASTA

Download an annotation file such as gencode, and then run:

$ STAR --runMode genomeGenerate --genomeDir STAR --genomeFastaFiles $FASTA --sjdbGTFfile $ANNOTATION --sjdbOverhang 125

First, install the following prerequisites. Note that if you are only using one of the two mappers, bwa and STAR, you can omit the other.

• bwa =0.7.13
• STAR =2.7.1a
• gawk*
• Java SE Development Kit ~1.8
  • Gradle ~5.3
• Node.js ~10.15.3
• Python ~3.6.1
  • cwltool ~1.0
  • html5lib ~1.0.1
• samtools† ~1.9
  • zlib
• sambamba ~0.7.0

* XenoCP requires the GNU inplementation of awk.

† XenoCP only supports BAM files. When compiling samtools, CRAM block codecs (bz2 and lmza) can be disabled. ncurses (for samtools tview) can also be disabled.

Clone XenoCP from GitHub:

git clone https://github.com/stjude/XenoCP.git

Build XenoCP using Gradle:

$ gradle installDist

Add the artifacts under build/install/xenocp to your PATH and your Java CLASSPATH:

export PATH=$PATH:`pwd`/build/install/xenocp/bin
export CLASSPATH=$CLASSPATH:`pwd`/build/install/xenocp/lib/*

XenoCP requires three inputs, defined in a YAML file as CWL inputs. E.g., inputs.yml:

bam:
  class: File
  path: sample.bam
ref_db_prefix: /references/ref.fa
aligner: "bwa aln"

bam is the input sample BAM. ref_db_prefix, the basename of the reference assembly that should be cleansed. For example, a prefix of MGSCv37.fa would assume for bwa alignment that the following files in the same directory exist: MGSCv37.fa.amb, MGSCv37.fa.ann, MGSCv37.fa.bwt, MGSCv37.fa.pac, and MGSCv37.fa.sa. index should be the path to that folder. For STAR alignment, index should be a directory and it would assume the following files exist in the directory: chrLength.txt, chrNameLength.txt, chrName.txt, chrStart.txt, exonGeTrInfo.tab, exonInfo.tab, geneInfo.tab, Genome, genomeParameters.txt, SA, SAindex, sjdbInfo.txt, sjdbList.fromGTF.out.tab, sjdbList.out.tab, and transcriptInfo.tab. The aligner option specifies the mapping algorithm to use for aligning to the host genome, currently supported options are bwa aln, bwa mem, and star.

Several optional input paramters can be changed in the inputs file.

suffix_length: 4
keep_mates_together: true
validation_stringency: SILENT
output_prefix: xenocp-
output_extension: bam

XenoCP uses CWL to describe its workflow.

To run an example workflow, update sample_data/input_data/inputs_local.yml with the path to a reference genome. Then run the following.

$ mkdir results
$ cwltool --preserve-environment CLASSPATH --no-container --outdir results cwl/xenocp.cwl sample_data/input_data/inputs_local.yml

XenoCP provides a Dockerfile that builds an image with all the included dependencies. To use this image, install Docker for your platform.

In the XenoCP project directory, build the Docker image.

$ docker build --tag xenocp .

The Docker image does not provide an entrypoint.

The image assumes three working directories: /data for inputs, /reference for reference files, and /results for outputs. /data and /reference can be read-only, where as /results needs write access.

The paths given in the input parameters file must be from inside the container, not the host, e.g.,

bam:
  class: File
  path: /data/sample.bam
ref_db_prefix: ref.fa
index:
  class: Directory
  path: /reference
aligner: "bwa aln"

The following is an example run command where the data files are stored in the current directory under sample_data/input_data. Outputs are saved in results in the current directory. The path to the reference files on the host machine needs to be provided.

This example assumes you are running against Mus musculus (genome build MGSCv37). Set the path to the folder containing your reference data and run the following command to produce output from the included sample data. Test output for comparison is located at sample_data/output_data.

$ mkdir $(pwd)/results
$ docker run \
  --mount type=bind,source=$(pwd)/sample_data/input_data,target=/data,readonly \
  --mount type=bind,source=/path/to/reference,target=/reference,readonly \
  --mount type=bind,source=$(pwd)/results,target=/results \
  ghcr.io/stjude/xenocp:latest \
  cwl-runner \
  --parallel \
  --outdir results \
  --no-container \
  /opt/xenocp/cwl/xenocp.cwl \
  /data/inputs.yml

Singularity is an experimental container solution that is an HPC-friendly alternative to Docker. For many reasons, singularity is not a drop-in replacement for Docker. Many applications require modification to fully run with singularity. This alternative is provided on a best-effort basis. If issues are encountered, please open an issue on this repository with details and the maintainers will try to provide support as possible.

$ mkdir $(pwd)/results
$ singularity run \
  --containall \ # Isolate container from host
  -W /path/to/directory \ # Provide a directory with sufficient space to use for working directory
  -B $(pwd)/sample_data/input_data:/data \
  -B /path/to/reference:/reference \
  -B $(pwd)/results:/results \
  docker:https://ghcr.io/stjude/xenocp:latest \
  cwl-runner \
  --parallel \
  --outdir results \
  --no-container \
  /opt/xenocp/cwl/xenocp.cwl \
  /data/inputs.yml

Note: when running using Singularity on an HPC, problems can arise if the default temporary file location, /tmp, is small. To solve this, include -W <dir> when executing via Singularity to redirect temp files to a larger directory <dir>.

Note: By default, singularity makes many host resources available inside the container. This is in contrast with Docker's native isolation. This also tends to cause conflicts and errors when running Docker-based workflows. Therefore we recommend always using the --containall option to Singularity.

XenoCP includes a WDL workflow implementation. This can be run locally or on a supported HPC system. It can also use Docker or Singularity for containerization.

As of v1.2, WDL does not support directory inputs. Therefore the reference files provided to the WDL workflow must be compressed (.tar.gz) before running. The compressed reference files can be downloaded from Zenodo.

To run the WDL workflow, you will need a WDL engine. We suggest miniwdl, though the Cromwell engine should work, but is untested with XenoCP.

After acquiring the reference files for your chosen aligner, you can run the sample data through the WDL workflow with the following command.

miniwdl run https://raw.githubusercontent.com/stjude/XenoCP/main/wdl/workflows/xenocp.wdl input_bam=https://github.com/stjude/XenoCP/raw/main/sample_data/input_data/SJRB001_X.subset.bam input_bai=https://github.com/stjude/XenoCP/raw/main/sample_data/input_data/SJRB001_X.subset.bam.bai reference_tar_gz=MGSCv37_bwa.tar.gz aligner='bwa aln'

This will run all of the steps on the local machine with Docker. The WDL runner miniwdl supports alternative execution modes, such as the Singularity container engine, Slurm for batch systems, and LSF for batch systems. Alternative execution modes can be specified using miniwdl's configuration system.

If you have bcftools and a GRCh37-lite reference file, the following will show two variants in the input file. The variant on chromosome 1 is an artifact of mouse reads. The variant on chromosome 9 is a variant in the graft genome.

$ bcftools mpileup -R sample_data/output_data/regions.bed -f ref/GRCh37-lite/GRCh37-lite.fa sample_data/input_data/SJRB001_X.subset.bam | bcftools call -m - | tail -n 3

Output:

[mpileup] 1 samples in 1 input files
#CHROM	POS	ID	REF	ALT	QUAL	FILTER	INFO	FORMAT	H_LC-SJRB001-X-SJ39.3-8L
1	156044156	.	C	T	67	.	DP=49;VDB=0.525878;SGB=-0.670168;RPB=0.999118;MQB=0.00218214;MQSB=0.948436;BQB=0.743365;MQ0F=0;ICB=1;HOB=0.5;AC=1;AN=2;DP4=15,19,2,8;MQ=52	GT:PL	0/1:102,0,255
9	19451994	.	G	A	182	.	DP=26;VDB=0.130558;SGB=-0.680642;RPB=0.887078;MQB=0.948139;MQSB=0.955682;BQB=0.053431;MQ0F=0;ICB=1;HOB=0.5;AC=1;AN=2;DP4=5,8,6,6;MQ=58	GT:PL	0/1:215,0,255

After running XenoCP, the host genome variant is removed, as the supporting reads will be unmapped. The following command demonstrates the removal of the variant on chromosome 1 in the output of the sample data.

$ bcftools mpileup -R sample_data/output_data/regions.bed -f ref/GRCh37-lite/GRCh37-lite.fa sample_data/output_data/SJRB001_X.subset.xenocp.bam | bcftools call -m - | tail -n 3

Output:

[mpileup] 1 samples in 1 input files
#CHROM	POS	ID	REF	ALT	QUAL	FILTER	INFO	FORMAT	H_LC-SJRB001-X-SJ39.3-8L
1	156044156	.	C	.	285	.	DP=41;MQSB=0.633762;MQ0F=0;AN=2;DP4=16,20,0,0;MQ=57	GT	0/0
9	19451994	.	G	A	191	.	DP=27;VDB=0.198993;SGB=-0.683931;RPB=0.729125;MQB=0.945959;MQSB=0.960078;BQB=0.0425475;MQ0F=0;ICB=1;HOB=0.5;AC=1;AN=2;DP4=5,8,6,7;MQ=58	GT:PL	0/1:224,0,255

To run XenoCP in St. Jude Cloud, please follow the directions at https://www.stjude.cloud/docs/guides/tools/xenocp/

Copyright 2019 St. Jude Children's Research Hospital

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

https://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

For questions and bug reports, please open an issue on the GitHub project page.

XenoCP is published in bioRxiv.

Please cite Rusch M, Ding L, et al. 2019. XenoCP: Cloud-based BAM cleansing tool for RNA and DNA from Xenograft. bioRxiv doi:10.1101/843250.

Sambamba uses a large number of temporary files while merging the final bam file. Depending on your system, the default open file limit may be too low. You can check the limit with ulimit -n and set the limit higher with ulimit -Sn <value>.