Getting started


  1. Create a sample configuration file for your project (substitute the example BAM and fastq names below with the full path to your sample files): -w template gatk-variant project1 sample1.bam sample2_1.fq sample2_2.fq

    This uses a standard template (GATK best practice variant calling) to automate creation of a full configuration for all samples. See Automated sample configuration for more details on running the script, and manually edit the base template or final output file to incorporate project specific configuration. The example pipelines provide a good starting point and the Sample information documentation has full details on available options.

  2. Run analysis, distributed across 8 local cores: bcbio_sample.yaml -n 8
  3. Read the Configuration documentation for full details on adjusting both the sample and system configuration files to match your experiment and computational setup.


There are 3 logging files in the log directory within your working folder:

  • bcbio-nextgen.log High level logging information about the analysis. This provides an overview of major processing steps and useful checkpoints for assessing run times.
  • bcbio-nextgen-debug.log Detailed information about processes including stdout/stderr from third party software and error traces for failures. Look here to identify the status of running pipelines or to debug errors. It labels each line with the hostname of the machine it ran on to ease debugging in distributed cluster environments.
  • bcbio-nextgen-commands.log Full command lines for all third party software tools run.

Example pipelines

We supply example input configuration files for validation and to help in understanding the pipeline.

Whole genome trio (50x)

This input configuration runs whole genome variant calling using bwa, GATK HaplotypeCaller and FreeBayes. It uses a father/mother/child trio from the CEPH NA12878 family: NA12891, NA12892, NA12878. Illumina’s Platinum genomes project has 50X whole genome sequencing of the three members. The analysis compares results against a reference NA12878 callset from NIST’s Genome in a Bottle initiative.

To run the analysis do:

mkdir -p NA12878-trio-eval/config NA12878-trio-eval/input NA12878-trio-eval/work
cd NA12878-trio-eval/config
cd ../input
cd ../work ../config/NA12878-trio-wgs-validate.yaml -n 16

This is a large whole genome analysis and meant to test both pipeline scaling and validation across the entire genome. It can take multiple days to run depending on available cores. It requires 300Gb for the input files and 1.3Tb for the work directory. Smaller examples below exercise the pipeline with less disk and computational requirements.

Whole genome (10x)

An input configuration for running whole gnome variant calling with bwa and GATK, using Illumina’s Platinum genomes project (NA12878-illumina.yaml). See this blog post on whole genome scaling for expected run times and more information about the pipeline. To run the analysis:

  • Create an input directory structure like:

    ├── config
    │   └── NA12878-illumina.yaml
    ├── input
    └── work
  • Retrieve inputs and comparison calls:

    cd input
    gunzip *.vcf.gz *.bed.gz
  • Retrieve configuration input file:

    cd config
  • Run analysis on 16 core machine:

    cd work ../config/NA12878-illumina.yaml -n 16
  • Examine summary of concordance and discordance to comparison calls from the grading-summary.csv file in the work directory.

Exome with validation against reference materials

This example calls variants on NA12878 exomes from EdgeBio’s clinical sequencing pipeline, and compares them against reference materials from NIST’s Genome in a Bottle initiative. This supplies a full regression pipeline to ensure consistency of calling between releases and updates of third party software. The pipeline performs alignment with bwa mem and variant calling with FreeBayes, GATK UnifiedGenotyper and GATK HaplotypeCaller. Finally it integrates all 3 variant calling approaches into a combined ensemble callset.

This is a large full exome example with multiple variant callers, so can take more than 24 hours on machines using multiple cores.

First get the input configuration file, fastq reads, reference materials and analysis regions:

mkdir NA12878-exome-eval/config NA12878-exome-eval/input NA12878-exome-eval/work
cd NA12878-exome-eval/config
cd ../input

Finally run the analysis, distributed on 8 local cores, with:

cd ../work ../config/NA12878-exome-methodcmp.yaml -n 8

The grading-summary.csv contains detailed comparisons of the results to the NIST reference materials, enabling rapid comparisons of methods.

Cancer tumor normal

This example calls variants in a paired cancer sample with tumor/normal sequencing data using raw data from Han et al in PLoS One. This is a work in progress and we welcome contributions. The goal is to use a full evaluation dataset to compare calling methods:

Get the input configuration file:

mkdir config && cd config

Get fastq reads and analysis regions:

cd .. && mkdir input && cd input


cd .. & mkdir work && cd work ../config/cancer-paired.yaml -n 8

RNAseq example

This example aligns and creates count files for use with downstream analyses using the SEQC data from the FDA’s Sequencing Quality Control project. The rnaseq-seqc.yaml is

Get the input configuration files:


Get the FASTQ files from the SEQC project:

mkdir fastq
cd fastq
tar xvf seqc.tar
cd ..

Use the templating system to set up an analysis: -w template rnaseq-seqc.yalm seqc.csv fastq

Go into the work directory and run your analysis:

cd seqc/work ../config/seqc.yaml -n 16

This will run a full scale RNAseq experiment using Tophat2 as the aligner and will take a long time to finish on a single machine. At the end it will output counts, Cufflinks quantitation and a set of QC results about each lane. If you have a cluster you can parallelize it to speed it up considerably.

A nice looking standalone report of the bcbio-nextgen can be generated using bcbio.rnaseq. Check that repository for details.

Test suite

The test suite exercises the scripts driving the analysis, so are a good starting point to ensure correct installation. Tests use the nose test runner pre-installed as part of the pipeline. Grab the latest source code:

$ git clone

To run the standard tests:

$ cd bcbio-nextgen/tests
$ ./

To run specific subsets of the tests:

$ ./ rnaseq
$ ./ speed=2
$ ./ devel

By default the test suite will use your installed system configuration for running tests, substituting the test genome information instead of using full genomes. If you need a specific testing environment, copy tests/data/automated/post_process-sample.yaml to tests/data/automated/post_process.yaml to provide a test-only configuration.