Long Ranger2.2 (latest), printed on 11/14/2024
Analysis software for 10x Genomics linked read products is no longer supported. Raw data processing pipelines and visualization tools are available for download and can be used for analyzing legacy data from 10x Genomics kits in accordance with our end user licensing agreement without support. |
Long Ranger's Targeted Mode analyzes sequencing data from a Chromium-prepared, targeted library. Generally this is an exome hybrid capture, but targeted mode is compatible with any pull-down panel. This involves the following steps:
Run longranger mkfastq on the Illumina BCL output folder to generate FASTQ files.
Run longranger targeted on each targeted sample that was demultiplexed by longranger mkfastq.
For the following example, assume that the Illumina BCL output is in a folder named /sequencing/140101_D00123_0111_AHAWT7ADXX
.
The target BED file supplied to the pipeline via the --targets is used in computing metrics such as on-target coverage. The SV and CNV calling algorithms in Long Ranger also use the target BED file to define regions of interest. For Agilent SureSelect Human All Exon V6, we strongly recommend using the latest BED file released by Agilent. The BED file is available as SureSelect Human All Exon V6 r2
from Agilent.
Long Ranger includes a CNV caller that detects exon-scale deletions in targeted
regions. It is important to supply the --cnvfilter argument a BED
file which masks problematic regions where baits perform poorly, to
prevent false-positive calls. We have
created a cnvfilter file tailored to the SureSelect Human All Exon V6
r2
BED file, which is available for download: Agilent Exome V6 r2 CNV Filter
BED. We focus on the following
cases:
First, follow the instructions on running longranger mkfastq to generate FASTQ files. For example, if the flowcell serial number was HAWT7ADXX
, then longranger mkfastq will output FASTQ files in HAWT7ADXX/outs/fastq_path
.
To run Long Ranger in targeted mode, use the longranger targeted command with a .bed file as the --targets argument, plus the following common parameters. For a complete list of command-line options, run longranger targeted --help.
For help on which arguments to use to target a particular set of FASTQs, consult Running 10x Pipelines on FASTQ Files. |
Argument | Description |
---|---|
--id | A unique run ID string: e.g. sample345 |
--fastqs | Path of the FASTQ folder generated by longranger mkfastq, e.g. /home/jdoe/runs/HAWT7ADXX/outs/fastq_path |
--vcmode | (required, except when specifying
--precalled ) Must be one of: freebayes, gatk:/path/to/GenomeAnalysisTK.jar, or disable |
--sample | (optional) Sample name as specified in the sample sheet supplied to mkfastq . |
--downsample | (optional) Specify the maximum amount of sequencing data to be used by the pipeline, in gigabases. If more data is available than this request, reads will be randomly downsampled. If less data is available, this option will have no effect. |
--reference | Path to a 10x compatible reference, e.g. /opt/refdata-hg19-2.1.0 .See Installation for how to download and install the default reference. |
--targets | BED file associated with the pulldown used for this Chromium library e.g. /home/jdoe/runs/agilent_exome.bed . See Target Files above for details. |
--cnvfilter | A BED file indicating poorly performing targets or problematic genomic regions that should not generate CNV calls. See Target Files above for details |
--precalled | (optional) Path to a "pre-called" VCF file. Variants in this file will be phased. When setting --precalled do not specify a --vcmode |
--sex | (optional) Sex of the sample: male or female . Sex will be detected based on coverage if not supplied. |
--somatic | (optional) Supply this flag for somatic samples. This will increase the sensitivity of the large-scale SV caller for somatic SVs, by allowing the detection of sub-haplotype events. Note: this option currently does not affect small-scale variant calling. The small scale variant caller is not currently optimized for somatic variants |
After determining these input arguments, run longranger targeted:
$ cd /home/jdoe/runs $ longranger targeted --id=sample345 \ --reference=/opt/refdata-hg19-2.1.0 \ --fastqs=/home/jdoe/runs/HAWT7ADXX/outs/fastq_path \ --targets=/home/jdoe/runs/agilent_exome.bed \ --cnvfilter=/home/jdoe/runs/agilent_v6r2_cnvfilter.bed
Following a set of preflight checks to validate input arguments, Long Ranger pipeline stages will begin to run:
longranger targeted 2.2.2 Copyright (c) 2016 10x Genomics, Inc. All rights reserved. ----------------------------------------------------------------------------- Martian Runtime - 2.3.2 Running preflight checks (please wait)... 2016-05-01 12:00:00 [runtime] (ready) ID.sample345.PHASER_SVCALLER_CS.PHASER_SVCALLER._ALIGNER.SETUP_CHUNKS 2016-05-01 12:00:00 [runtime] (run:local) ID.sample345.PHASER_SVCALLER_CS.PHASER_SVCALLER._SNPINDEL_PHASER.SORT_GROUND_TRUTH 2016-05-01 12:00:00 [runtime] (run:local) ID.sample345.PHASER_SVCALLER_CS.PHASER_SVCALLER._SNPINDEL_PHASER.SORT_GROUND_TRUTH.fork0.chnk0.main ...
By default, longranger targeted will use all of the cores available on your
system to execute pipeline stages. You can specify a different number of cores
to use with the --localcores
option; for example, --localcores=16
will limit Long Ranger to using up to sixteen cores at once. Similarly,
--localmem
will restrict the amount of memory (in GB) used by
longranger targeted.
The pipeline will create a new folder named with the sample ID you specified (e.g. /home/jdoe/runs/sample345
) for its output. If this folder already exists, Long Ranger will assume it is an existing pipestance and attempt to resume running it.
A successful longranger targeted execution should conclude with a message similar to this:
2016-05-02 15:46:41 [runtime] (run:local) ID.sample345.PHASER_SVCALLER_CS.PHASER_SVCALLER.LOUPE_PREPROCESS.fork0.join 2016-05-02 15:46:44 [runtime] (join_complete) ID.sample345.PHASER_SVCALLER_CS.PHASER_SVCALLER.LOUPE_PREPROCESS 2016-05-02 15:46:55 [runtime] VDR killed 4738 files, 223GB. Outputs: - Run summary: /home/jdoe/runs/sample345/outs/summary.csv - BAM barcoded: /home/jdoe/runs/sample345/outs/phased_possorted_bam.bam - BAM index: /home/jdoe/runs/sample345/outs/phased_possorted_bam.bam.bai - VCF phased: /home/jdoe/runs/sample345/outs/phased_variants.vcf.gz - VCF index: /home/jdoe/runs/sample345/outs/phased_variants.vcf.gz.tbi - Large-scale SV calls: /home/jdoe/runs/sample345/outs/large_sv_calls.bedpe - Large-scale SV candidates: /home/jdoe/runs/sample345/outs/large_sv_candidates.bedpe - Large-scale SVs: /home/jdoe/runs/sample345/outs/large_svs.vcf.gz - Large-scale SVs index: /home/jdoe/runs/sample345/outs/large_svs.vcf.gz.tbi - Mid-scale deletions: /home/jdoe/runs/sample345/outs/dels.vcf.gz - Mid-scale deletions index: /home/jdoe/runs/sample345/outs/dels.vcf.gz.tbi - Loupe file: /home/jdoe/runs/sample345/outs/loupe.loupe Pipestance completed successfully!
The output of the pipeline will be contained in a folder named with the sample ID you specified (e.g. sample345
). The subfolder named outs
will contain the main pipeline output files:
File Name | Description |
---|---|
summary.csv | Run summary metrics in CSV format |
phased_possorted_bam.bam | Aligned reads annotated with barcode information |
phased_possorted_bam.bam.bai | Index for phased_possorted_bam.bam |
phased_variants.vcf.gz | VCF annotated with barcode and phasing information |
phased_variants.vcf.gz.tbi | Index for phased_variants.vcf.gz |
large_sv_calls.bedpe | Confidently called large-scale structural variants (greater than the 97.5th percentile of the molecule size distribution or inter-chromosomal) in BEDPE format |
large_sv_candidates.bedpe | Large-scale structural variant calls and low confidence candidates in BEDPE format |
large_svs.vcf.gz | Large-scale structural variant calls and candidates in VCF format |
large_svs.vcf.gz.tbi | Index for large_svs.vcf.gz |
dels.vcf.gz | Exon deletion calls |
dels.vcf.gz.tbi | Index for dels.vcf.gz |
loupe.loupe | File that can be opened in the Loupe genome browser |
Once longranger targeted has successfully completed, you can browse the resulting .loupe
file in the Loupe genome browser, or refer to the Understanding Output section to explore the data by hand.