Best-practice pipelines for fully automated high throughput sequencing analysis
Project description
## bcbio-nextgen
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 pipeline that handles distributed execution, idempotent processing restarts and safe transactional steps. The goal is to provide a shared community resource that handles the front end data processing component of sequencing analysis, allowing us to focus on the downstream biology.
## Quick start
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 install_directory data_directory
producing a [system configuration file][q2] referencing the installed software and data.
Edit a [sample configuration file][q1] to describe your samples.
Run analysis, distributed across 8 local cores:
bcbio_nextgen.py bcbio_system.yaml bcbio_sample.yaml -n 8
[q1]: https://github.com/chapmanb/bcbio-nextgen/blob/master/config/bcbio_sample.yaml [q2]: https://github.com/chapmanb/bcbio-nextgen/blob/master/config/bcbio_system.yaml
## Documentation
See the [full documentation at ReadTheDocs][d1].
[d1]: https://bcbio-nextgen.readthedocs.org
## Pipelines
### Variant calling
The pipeline implements the [GATK best practice][v1] guidelines for variant calling, which includes:
Alignment
Base Quality Recalibration
Realignment around indels
- Variant calling. The pipeline supports:
[GATK Unified Genotyper][v2] (part of GATK-lite in GATK 2.x)
[GATK Haplotype caller][v3] (part of the full, non-open source GATK 2.x)
[FreeBayes][v4]
[samtools mpileup][v5]
[cortex_var][v6]
Quality filtering, using both [GATK’s Variant Quality Score Recalibrator][v7] and hard filtering.
Annotation of effects, using [snpEff][v8]
[v1]: http://gatkforums.broadinstitute.org/discussion/1186/best-practice-variant-detection-with-the-gatk-v4-for-release-2-0 [v2]: http://www.broadinstitute.org/gatk/gatkdocs/org_broadinstitute_sting_gatk_walkers_genotyper_UnifiedGenotyper.html [v3]: http://www.broadinstitute.org/gatk/gatkdocs/org_broadinstitute_sting_gatk_walkers_haplotypecaller_HaplotypeCaller.html [v4]: https://github.com/ekg/freebayes [v5]: http://samtools.sourceforge.net/mpileup.shtml [v6]: http://cortexassembler.sourceforge.net/index_cortex_var.html [v7]: http://www.broadinstitute.org/gatk/gatkdocs/org_broadinstitute_sting_gatk_walkers_variantrecalibration_VariantRecalibrator.html [v8]: http://snpeff.sourceforge.net/
## Features
### Distributed
The pipeline runs on single multicore machines, in compute clusters managed by LSF or SGE using [IPython parallel][o8], or on the Amazon cloud. [This tutorial][o5] describes running the pipeline on Amazon with [CloudBioLinux][o6] and [CloudMan][o7].
### Galaxy integration
The scripts can be tightly integrated with the [Galaxy][o1] web-based analysis tool. Tracking of samples occurs via a web based LIMS system, and processed results are uploading into Galaxy Data Libraries for researcher access and additional analysis. See the [installation instructions for the front end][o2] and a [detailed description of the full system][o3].
[o1]: http://galaxy.psu.edu/ [o2]: https://bitbucket.org/galaxy/galaxy-central/wiki/LIMS/nglims [o3]: http://bcbio.wordpress.com/2011/01/11/next-generation-sequencing-information-management-and-analysis-system-for-galaxy/ [o5]: http://bcbio.wordpress.com/2011/08/19/distributed-exome-analysis-pipeline-with-cloudbiolinux-and-cloudman/ [o6]: http://cloudbiolinux.org [o7]: http://wiki.g2.bx.psu.edu/Admin/Cloud [o8]: http://ipython.org/ipython-doc/dev/index.html
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