Single cell RNA-seq analysis¶
Setting up bcbio single cell RNA-seq analysis tutorial outlines the steps needed to run bcbio in that use case.
Cancer tumor-normal variant calling¶
This is a teaching orientated example of using bcbio from the Cold Spring Harbor Laboratory’s Advanced Sequencing Technology and Applications course. This uses cancer tumor normal data from the ICGC-TCGA DREAM synthetic 3 challenge, subset to exomes on chromosome 6 to reduce runtimes. It demonstrates:
- Running a cancer tumor/normal workflow through bcbio.
- Analysis with human genome build 38.
- SNP and indel detection, with 3 variant callers and an ensemble method.
- Structural variant calling, with 2 callers.
- Prioritization of structural variants for cancer associated genes in CIViC.
- HLA typing.
- Validation of both small and structural variants against truth sets.
Loading pre-run analysis¶
To save downloading the genome data and running the analysis, we have a pre-prepared AMI with the data and analysis run. Use the AWS Console to launch the pre-built AMI – search Community AMIs for ami-5e84fe34. Any small instance type is fine for exploring the configuration, run directory and output files. Make sure you associate a public IP and a security group that allows remote ssh.
Once launched, ssh into the remote machine with
ssh -i your-keypair email@example.com to explore the inputs and outputs. The default PATH contains bcbio and third party programs in
/usr/local/bin, with the biological data installed in
/usr/local/share/bcbio. The run is in a
Input configuration file¶
To run bcbio, you prepare a small configuration file describing your analysis. You can prepare it manually or use an automated configuration method. The example has a pre-written configuration file with tumor/normal data located in the
config directory and this section walks through the settings.
You define the type of analysis (variant calling) along with the input files and genome build:
analysis: variant2 files: [../input/cancer-syn3-chr6-tumor-1.fq.gz, ../input/cancer-syn3-chr6-tumor-2.fq.gz] genome_build: hg38
Sample description and assignment as a tumor sample, called together with a matched normal:
description: syn3-tumor metadata: batch: syn3 phenotype: tumor sex: female
Next it defines parameters for running the analysis. First we pick our aligner (bwa mem):
algorithm: aligner: bwa
Post-alignment, we mark duplicates but do not perform recalibration and realignment:
mark_duplicates: true recalibrate: false realign: false
We call variants in exome regions on chromosome 6 using a BED file input, call variants as low as 2% in the tumor sample, and use 3 variant callers along with an ensemble method that combines results for any found in 2 out of 3:
variant_regions: ../input/NGv3-chr6-hg38.bed min_allele_fraction: 2 variantcaller: [vardict, freebayes, varscan] ensemble: numpass: 2
For structural variant calling, we use two callers and prioritize variants to those found in the CIViC database:
svcaller: [lumpy, manta] svprioritize: cancer/civic
Call HLA types with OptiType:
Finally, we validate both the small variants and structural variants. These use pre-installed validation sets that come with bcbio. We limit validation regions to avoid low complexity regions, which cause bias in validating indels:
exclude_regions: [lcr] validate: dream-syn3-crossmap/truth_small_variants.vcf.gz validate_regions: dream-syn3-crossmap/truth_regions.bed svvalidate: DEL: dream-syn3-crossmap/truth_DEL.bed DUP: dream-syn3-crossmap/truth_DUP.bed INV: dream-syn3-crossmap/truth_INV.bed
Output files are in
~/run/cancer-syn3-chr6/final, extracted from the full work directory in
The directories with sample information are in
syn3-tumor/. Aligned BAMs include a
-ready.bam file with all of the original reads (including split and discordants) and separate files with only the split (
-sr.bam) and discordant (
syn3-tumor-ready.bam syn3-tumor-ready.bam.bai syn3-tumor-sr.bam syn3-tumor-sr.bam.bai syn3-tumor-disc.bam syn3-tumor-disc.bam.bai
SNP and indel calls for 3 callers, plus combined ensemble calls:
syn3-tumor-ensemble.vcf.gz syn3-tumor-ensemble.vcf.gz.tbi syn3-tumor-freebayes.vcf.gz syn3-tumor-freebayes.vcf.gz.tbi syn3-tumor-varscan.vcf.gz syn3-tumor-varscan.vcf.gz.tbi syn3-tumor-vardict.vcf.gz syn3-tumor-vardict.vcf.gz.tbi
Structural variant calls for 2 callers, plus a simplified list of structural variants in cancer genes of interest:
syn3-tumor-sv-prioritize.tsv syn3-tumor-lumpy.vcf.gz syn3-tumor-lumpy.vcf.gz.tbi syn3-tumor-manta.vcf.gz syn3-tumor-manta.vcf.gz.tbi
HLA typing results:
Validation results from comparisons against truth set, including plots:
syn3-tumor-sv-validate.csv syn3-tumor-sv-validate-DEL.png syn3-tumor-sv-validate-df.csv syn3-tumor-sv-validate-DUP.png syn3-tumor-sv-validate-INV.png syn3-tumor-validate.png
The top level directory for the project,
2015-11-18_syn3-cshl/ has files relevant to the entire run. There is a consolidated quality control report:
Povenance information, with log files of all commands run and program versions used:
bcbio-nextgen.log bcbio-nextgen-commands.log programs.txt data_versions.csv
A top level summary of metrics for alignment, variant calling and coverage that is useful downstream:
Preparing and Running¶
The steps to prepare an AMI from a bare machine and run the analysis. These are pre-done on the teaching AMI to save time:
Use the AWS Console to launch a Ubuntu Server 14.04 (ami-d05e75b8). Start an m4.4xlarge instance with a 100GB SSD. Make sure you associate a public IP and can ssh in externally.
SSH to your instance:
ssh -i ~/.ec2/your-key.pem ubuntu@public-ip
Install bcbio with hg38 data:
sudo apt-get update sudo apt-get install -y build-essential zlib1g-dev wget curl python-setuptools git \ openjdk-7-jdk openjdk-7-jre ruby libncurses5-dev libcurl4-openssl-dev \ libbz2-dev unzip pigz bsdmainutils wget https://raw.githubusercontent.com/bcbio/bcbio-nextgen/master/scripts/bcbio_nextgen_install.py python bcbio_nextgen_install.py /usr/local/share/bcbio --tooldir /usr/local \ --genomes hg38 --aligners bwa --sudo --isolate -u development
Install the analysis data:
mkdir -p run cd run wget https://raw.githubusercontent.com/bcbio/bcbio-nextgen/master/config/teaching/cancer-syn3-chr6-prep.sh bash cancer-syn3-chr6-prep.sh
Run the analysis:
cd cancer-syn3-chr6/work bcbio_nextgen.py ../config/cancer-syn3-chr6.yaml -n 16