Best-practice pipelines for fully automated high throughput sequencing analysis
A python toolkit providing best-practice pipelines for fully automated high throughput sequencing analysis. You write a high level configuration file specifying your inputs and analysis parameters. This input drives a parallel run that handles distributed execution, idempotent processing restarts and safe transactional steps. The goal is to provide a shared community resource that handles the data processing component of sequencing analysis, providing researchers with more time to focus on the downstream biology.
- Community developed: We welcome contributors with the goal of overcoming the biological, algorithmic and computational challenges that face individual developers working on complex pipelines in quickly changing research areas. See our users page for examples of bcbio-nextgen deployments, and the developer documentation for tips on contributing.
- Installation: A single installer script prepares all third party software, data libraries and system configuration files.
- Automated validation: Compare variant calls against common reference materials or sample specific SNP arrays to ensure call correctness. Incorporation of multiple approaches for alignment, preparation and variant calling enable unbiased comparisons of algorithms.
- Distributed: Focus on parallel analysis and scaling to handle large population studies and whole genome analysis. Runs on single multicore computers, in compute clusters using IPython parallel, or on the Amazon cloud. See the parallel documentation for full details.
- Multiple analysis algorithms: bcbio-nextgen provides configurable variant calling and RNA-seq pipelines.
Install bcbio-nextgen with all tool dependencies and data files:
wget https://raw.github.com/chapmanb/bcbio-nextgen/master/scripts/bcbio_nextgen_install.py python bcbio_nextgen_install.py /usr/local/share/bcbio --tooldir=/usr/local \ --genomes GRCh37 --aligners bwa --aligners bowtie2
producing an editable system configuration file referencing the installed software, data and system information.
Automatically create a processing description of sample FASTQ and BAM files from your project, and a CSV file of sample metadata:
bcbio_nextgen.py -w template freebayes-variant project1.csv sample1.bam sample2_1.fq sample2_2.fq
Run analysis, distributed across 8 local cores:
cd project1/work bcbio_nextgen.py ../config/project1.yaml -n 8
- Miika Ahdesmaki, AstraZeneca
- Luca Beltrame, IRCCS “Mario Negri” Institute for Pharmacological Research, Milan, Italy
- Alla Bushoy, AstraZeneca
- Guillermo Carrasco, Science for Life Laboratory, Stockholm
- Brad Chapman, Harvard School of Public Health
- Peter Cock, The James Hutton Institute
- Mario Giovacchini, Science for Life Laboratory, Stockholm
- Rory Kirchner, Harvard School of Public Health
- Jakub Nowacki, AstraZeneca
- Brent Pedersen, University of Colorado Denver
- James Porter, The University of Chicago
- Valentine Svensson, Science for Life Laboratory, Stockholm
- Paul Tang, UCSF
- Roman Valls, Science for Life Laboratory, Stockholm
- Kevin Ying, Garvan Institute of Medical Research, Sydney, Australia
- Jeff Hammerbacher, Icahn School of Medicine at Mount Sinai
The code is freely available under the MIT license.