Create a sample configuration file for your project (substitute the example BAM and fastq names below with the full path to your sample files):
bcbio_nextgen.py -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.
Run analysis, distributed across 8 local cores:
bcbio_nextgen.py bcbio_sample.yaml -n 8
Read the Configuration documentation for full details on adjusting both the sample and system configuration files to match your experiment and computational setup.
bcbio encourages a project structure like:
my-project/ ├── config ├── final └── work
with the input configuration in the
config directory, the outputs of the
pipeline in the
final directory, and the actual processing done in the
work directory. Run the
bcbio_nextgen.py script from inside the
directory to keep all intermediates there. The
final directory, relative to
the parent directory of the
work directory, is the default location
specified in the example configuration files and gets created during
final directory has all of the finished outputs and you can
work intermediates to cleanup disk space after confirming the
results. All of these locations are configurable and this project structure is
only a recommendation.
There are 3 logging files in the
log directory within your working folder:
bcbio-nextgen.logHigh level logging information about the analysis. This provides an overview of major processing steps and useful checkpoints for assessing run times.
bcbio-nextgen-debug.logDetailed 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.logFull command lines for all third party software tools run.
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 wget https://raw.github.com/chapmanb/bcbio-nextgen/master/config/examples/NA12878-trio-wgs-validate.yaml cd ../input wget https://raw.github.com/chapmanb/bcbio-nextgen/master/config/examples/NA12878-trio-wgs-validate-getdata.sh bash NA12878-trio-wgs-validate-getdata.sh cd ../work bcbio_nextgen.py ../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.
We also have a more extensive evaluation that includes 2 additional variant callers, Platypus and samtools, and 3 different methods of calling variants: single sample, pooled, and incremental joint calling. This uses the same input data as above but a different input configuration file:
mkdir -p NA12878-trio-eval/work_joint cd NA12878-trio-eval/config wget https://raw.github.com/chapmanb/bcbio-nextgen/master/config/examples/NA12878-trio-wgs-joint.yaml cd ../work_joint bcbio_nextgen.py ../config/NA12878-trio-wgs-joint.yaml -n 16
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 -p NA12878-exome-eval/config NA12878-exome-eval/input NA12878-exome-eval/work cd NA12878-exome-eval/config wget https://raw.github.com/chapmanb/bcbio-nextgen/master/config/examples/NA12878-exome-methodcmp.yaml cd ../input wget https://raw.github.com/chapmanb/bcbio-nextgen/master/config/examples/NA12878-exome-methodcmp-getdata.sh bash NA12878-exome-methodcmp-getdata.sh
Finally run the analysis, distributed on 8 local cores, with:
cd ../work bcbio_nextgen.py ../config/NA12878-exome-methodcmp.yaml -n 8
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 using multiple approaches in a paired tumor/normal cancer sample from the ICGC-TCGA DREAM challenge. It uses synthetic dataset 3 which has multiple subclones, enabling detection of lower frequency variants. Since the dataset is freely available and has a truth set, this allows us to do a full evaluation of variant callers.
To get the data:
mkdir -p cancer-dream-syn3/config cancer-dream-syn3/input cancer-dream-syn3/work cd cancer-dream-syn3/config wget https://raw.githubusercontent.com/chapmanb/bcbio-nextgen/master/config/examples/cancer-dream-syn3.yaml cd ../input wget https://raw.githubusercontent.com/chapmanb/bcbio-nextgen/master/config/examples/cancer-dream-syn3-getdata.sh bash cancer-dream-syn3-getdata.sh
cd ../work bcbio_nextgen.py ../config/cancer-dream-syn3.yaml -n 8
The configuration and data file has downloads for exome only and whole genome analyses. It enables exome by default, but you can use the larger whole genome evaluation by uncommenting the relevant parts of the configuration and retrieval script.
Structural variant calling – whole genome NA12878 (50x)¶
This example runs structural variant calling with multiple callers (Lumpy, Manta and CNVkit), providing a combined output summary file and validation metrics against NA12878 deletions. It uses the same NA12878 input as the whole genome trio example.
To run the analysis do:
mkdir -p NA12878-sv-eval cd NA12878-sv-eval wget https://raw.github.com/chapmanb/bcbio-nextgen/master/config/examples/NA12878-sv-getdata.sh bash NA12878-sv-getdata.sh cd work bcbio_nextgen.py ../config/NA12878-sv.yaml -n 16
This is large whole genome analysis and the timing and disk space requirements for the NA12878 trio analysis above apply here as well.
This example aligns and creates count files for use with downstream analyses using a subset of the SEQC data from the FDA’s Sequencing Quality Control project.
Get the setup script and run it, this will download six samples from the SEQC project, three from the HBRR panel and three from the UHRR panel. This will require about 100GB of disk space for these input files. It will also set up a configuration file for the run, using the templating system:
wget https://raw.github.com/chapmanb/bcbio-nextgen/master/config/examples/rnaseq-seqc-getdata.sh bash rnaseq-seqc-getdata.sh
Now go into the work directory and run the analysis:
cd seqc/work bcbio_nextgen.py ../config/seqc.yaml -n 8
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.
Human genome build 38¶
Validate variant calling on human genome build 38, using two different builds (with and without alternative alleles) and three different validation datasets (Genome in a Bottle prepared with two methods and Illumina platinum genomes). To run:
mkdir -p NA12878-hg38-val cd NA12878-hg38-val wget https://raw.github.com/chapmanb/bcbio-nextgen/master/config/examples/NA12878-hg38-validate-getdata.sh bash NA12878-hg38-validate-getdata.sh cd work bcbio_nextgen.py ../config/NA12878-hg38-validate.yaml -n 16
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 wget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR091/ERR091571/ERR091571_1.fastq.gz wget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR091/ERR091571/ERR091571_2.fastq.gz
Retrieve configuration input file:
cd config wget https://raw.github.com/chapmanb/bcbio-nextgen/master/config/examples/NA12878-illumina.yaml
Run analysis on 16 core machine:
cd work bcbio_nextgen.py ../config/NA12878-illumina.yaml -n 16
Examine summary of concordance and discordance to comparison calls from the
grading-summary.csvfile in the work directory.
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 https://github.com/chapmanb/bcbio-nextgen.git
To run the standard tests:
$ cd bcbio-nextgen/tests $ ./run_tests.sh
To run specific subsets of the tests:
$ ./run_tests.sh rnaseq $ ./run_tests.sh speed=2 $ ./run_tests.sh devel $ ./run_tests.sh docker $ ./run_tests.sh devel_ipython
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.yaml to provide a test-only