bcbio-nextgen 0.6.3

Best-practice pipelines for fully automated high throughput sequencing analysis

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 data processing component of sequencing analysis, providing researchers with more time to focus on the downstream biology.

Quick start

1. 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 referencing the installed software and data.

2. Edit a sample configuration file to describe your samples.

3. Run analysis, distributed across 8 local cores:

   bcbio_nextgen.py bcbio_system.yaml bcbio_sample.yaml -n 8

Documentation

See the full documentation at ReadTheDocs.

Pipelines

Variant calling

bcbio-nextgen implements configurable best-practice pipelines for SNP and small indel calling:

• Sequence alignment:

  • bowtie2

  • bwa

  • mosaik

  • novoalign

• Base Quality Recalibration

• Realignment around indels

• Variant calling:

  • GATK Unified Genotyper (supports both GATK-lite in GATK 2.3 and commercial restricted version in GATK 2.4+)

  • GATK Haplotype caller (part of the commercially restricted GATK 2.4+)

  • FreeBayes

  • samtools mpileup

  • cortex_var

• Quality filtering, using both GATK’s Variant Quality Score Recalibrator or hard filtering.

• Annotation of variant effects, using snpEff

It follows approaches from:

• GATK best practice guidelines for variant calling

• Marth Lab’s gkno pipelines

Features

Distributed

The pipeline runs on single multicore machines, in compute clusters managed by LSF or SGE using IPython parallel, or on the Amazon cloud. This tutorial describes running the pipeline on Amazon with CloudBioLinux and CloudMan.

Galaxy integration

The scripts can be tightly integrated with the Galaxy 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 and a detailed description of the full system.