10x Genomics
Chromium Genome & Exome

Long Ranger2.0, printed on 11/23/2024

Phasing and SV Calling of Exomes

Long Ranger's Exome Mode analyzes sequencing data from a Chromium-prepared, targeted library. This involves the following steps:

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

2. Run longranger run on each exome sample that was demultiplexed by longranger demux.

For the following example, assume that the Illumina BCL output is in a folder named /sequencing/140101_D00123_0111_AHAWT7ADXX.

Run longranger demux

First, follow the instructions on running longranger demux to generate FASTQ files. For example, if the flowcell serial number was HAWT7ADXX, then longranger demux will output FASTQ files in HAWT7ADXX/outs/fastq_path.

Run Long Ranger in Exome Mode with longranger run

To run Long Ranger in exome mode, use the longranger run command with a .bed file as the --targets argument. Here is a complete example:

After determining these input arguments, run longranger run:

$ cd /home/jdoe/runs
$ longranger run --id=sample345 \
                 --sex=female \
                 --fastqs=/home/jdoe/runs/HAWT7ADXX/outs/fastq_path \
                 --reference=/opt/refdata-hg19-2.0.0 \
                 --targets=/home/jdoe/runs/agilent_exome.bed \
                 --indices=SI-GA-A1

Following a set of preflight checks to validate input arguments, Long Ranger pipeline stages will begin to run:

longranger run 2.0.1
Copyright (c) 2016 10x Genomics, Inc.  All rights reserved.
-----------------------------------------------------------------------------
Martian Runtime - 2.0.1
 
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 run 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 run.

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 run 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_index.bam.bai
- VCF phased: /home/jdoe/runs/sample345/outs/phased_variants.vcf.gz
- VCF index: /home/jdoe/runs/sample345/outs/phased_variants_index.vcf.gz.tbi
- SV calls: /home/jdoe/runs/sample345/outs/sv_calls.bedpe
- SV candidates: /home/jdoe/runs/sample345/outs/sv_candidates.bedpe
- SV phasing: /home/jdoe/runs/sample345/outs/sv_phasing.tsv
- 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 run 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.