10x Genomics
Chromium Genome & Exome

Long Ranger2.1, printed on 12/26/2024

Targeted Phasing and SV Calling

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:

1. Run longranger mkfastq on the Illumina BCL output folder to generate FASTQ files.

2. 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.

Target Files

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 deletions in targeted regions. It is important to supply the --cnvfilter argument to prevent false-positive calls in regions where baits perform poorly. We have created a cnvfilter file tailored to the SureSelect Human All Exon V6 r2 BED file. Download: Agilent Exome V6 r2 CNV Filter BED. For other bait sets, it is recommended to create a BED file to masking poorly performing baits and problematic regions to avoid false positive calls.

Run longranger mkfastq

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.

Run longranger targeted

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.

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 2.1.6
Copyright (c) 2016 10x Genomics, Inc.  All rights reserved.
-----------------------------------------------------------------------------
Martian Runtime - 2.2.2
 
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.

Output Files

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:

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.