We provide an automated script that installs third party analysis tools, required genome data and python library dependencies for running human variant and RNA-seq analysis, bundled into an isolated directory or virtual environment:

python /usr/local/share/bcbio --tooldir=/usr/local \
  --genomes GRCh37 --aligners bwa --aligners bowtie2

bcbio should install cleanly on Linux systems. For Mac OSX, we suggest trying bcbio-vm which runs bcbio on Amazon Web Services or isolates all the third party tools inside a Docker container. bcbio-vm is still a work in progress but not all of the dependencies bcbio uses install cleanly on OSX.

With the command line above, indexes and associated data files go in /usr/local/share/bcbio-nextgen and tools are in /usr/local. If you don’t have write permissions to install into the /usr/local directories you can install in a user directory like ~/local or use sudo chmod to give your standard user permissions. Please don’t run the installer with sudo or as the root user.

The installation is highly customizable, and you can install additional software and data later using upgrade. Run python with no arguments to see options for configuring the installation process. Some useful arguments are:

  • --isolate Avoid updating the user’s ~/.bashrc if installing in a non-standard PATH. This facilitates creation of isolated modules without disrupting the user’s environmental setup. Manually edit your ~/.bashrc to allow bcbio runs with:

    export PATH=/path_to_bcbio/bin:$PATH
  • --nodata Do not install genome data.

The machine will need to have some basic requirements for installing and running bcbio:

Optional requirements:

  • Java 1.7, needed when running GATK < 3.6 or MuTect. This must be available in your path so typing java -version resolves a 1.7 version. bcbio distributes Java 8 as part of the anaconda installation for recent versions of GATK and MuTect2.
  • An OpenGL library, like Mesa (On Ubuntu/deb systems: libglu1-mesa, On RedHat/rpm systems: mesa-libGLU-devel). This is only required for cancer heterogeneity analysis with BubbleTree.

The bcbio-nextgen Dockerfile contains the packages needed to install on bare Ubuntu systems.

The automated installer creates a fully integrated environment that allows simultaneous updates of the framework, third party tools and biological data. This offers the advantage over manual installation of being able to manage and evolve a consistent analysis environment as algorithms continue to evolve and improve. Installing this way is as isolated and self-contained as possible without virtual machines or lightweight system containers like Docker. The Upgrade section has additional documentation on including additional genome data, and the section on Extra software describes how to add commercially restricted software like GATK and MuTect. Following installation, you should edit the pre-created system configuration file in /usr/local/share/bcbio-nextgen/galaxy/bcbio_system.yaml to match your local system or cluster configuration (see Tuning core and memory usage).


We use the same automated installation process for performing upgrades of tools, software and data in place. Since there are multiple targets and we want to avoid upgrading anything unexpectedly, we have specific arguments for each. Generally, you’d want to upgrade the code, tools and data together with: upgrade -u stable --tools --data

Tune the upgrade with these options:

  • -u Type of upgrade to do for bcbio-nextgen code. stable gets the most recent released version and development retrieves the latest code from GitHub.
  • --datatarget Customized installed data or download additional files not included by default: Customizing data installation
  • --toolplus Specify additional tools to include. See the section on Extra software for more details.
  • --genomes and --aligners options add additional aligner indexes to download and prepare. upgrade -h lists available genomes and aligners. If you want to install multiple genomes or aligners at once, specify --genomes or --aligners multiple times, like this: --genomes GRCh37 --genomes mm10 --aligners bwa --aligners bowtie2
  • Leave out the --tools option if you don’t want to upgrade third party tools. If using --tools, it will use the same directory as specified during installation. If you’re using an older version that has not yet went through a successful upgrade or installation and saved the tool directory, you should manually specify --tooldir for the first upgrade. You can also pass --tooldir to install to a different directory.
  • Leave out the --data option if you don’t want to get any upgrades of associated genome data.

Customizing data installation

bcbio installs associated data files for sequence processing, and you’re able to customize this to installer larger files or change the defaults. Use the --datatarget flag (potentially multiple times) to customize or add new targets.

By default, bcbio will install data files for variation, rnaseq and smallrna but you can sub-select a single one of these if you don’t require other analyses. The available targets are:

  • variation – Data files required for variant calling: SNPs, indels and structural variants. These include files for annotation like dbSNP, associated files for variant filtering, coverage and annotation files.
  • rnaseq – Transcripts and indices for running RNA-seq. The transcript files are also used for annotating and prioritizing structural variants.
  • smallrna – Data files for doing small RNA analysis.
  • gemini – The GEMINI framework associates publicly available metadata with called variants, and provides utilities for query and analysis. This target installs the required GEMINI data files.
  • caddCADD evaluates the potential impact of variations. It is freely available for non-commercial research, but requires licensing for commercial usage. The download is 30Gb and GEMINI will include CADD annotations if present.
  • vep – Data files for the Variant Effects Predictor (VEP). To use VEP as an alternative to the default installed snpEff, set vep in the Variant calling configuration.
  • dbnsfp Like CADD, dbNSFP provides integrated and generalized metrics from multiple sources to help with prioritizing variations for follow up. The files are large: dbNSFP is 10Gb, expanding to 100Gb during preparation. VEP will use dbNSFP for annotation of VCFs if included.
  • dbscsnv dbscSNV includes all potential human SNVs within splicing consensus regions (−3 to +8 at the 5’ splice site and −12 to +2 at the 3’ splice site), i.e. scSNVs, related functional annotations and two ensemble prediction scores for predicting their potential of altering splicing. VEP will use dbscSNV for annotation of VCFs if included.
  • battenberg Data files for Battenberg, which detects subclonality and copy number changes in whole genome cancer samples.
  • kraken Database for Kraken, optionally used for contamination detection.

Extra software

We’re not able to automatically install some useful tools due to licensing restrictions, so we provide a mechanism to manually download and add these to bcbio-nextgen during an upgrade with the --toolplus command line.

GATK and MuTect/MuTect2

Calling variants with GATK’s HaplotypeCaller, MuTect2 or UnifiedGenotyper requires manual installation of the latest GATK release. This is freely available for academic users, but requires a license for commerical use. It is not freely redistributable so requires a manual download from the GATK download site. If you don’t want to use the restricted GATK version, freely available callers like FreeBayes and VarDict provide a better alternative than using older GATK versions. See the FreeBayes and GATK comparison for a full evaluation.

To install the most recent version of GATK, register with the pre-installed gatk bioconda wrapper:

gatk-register /path/to/GenomeAnalysisTK.tar.bz2

If you’re not using the most recent post-3.6 version of GATK, or using a nightly build, you can add --noversioncheck to the command line to skip comparisons to the GATK version.

MuTect2 is distributed with GATK in versions 3.5 and later.

To install older versions of GATK (< 3.6), download and unzip the latest version from the GATK distribution. Then make this jar available to bcbio-nextgen with: upgrade --tools --toolplus gatk=/path/to/gatk/GenomeAnalysisTK.jar

This will copy the jar and update your bcbio_system.yaml and manifest files to reflect the new version.

MuTect also has similar licensing terms and requires a license for commerical use. After downloading the MuTect jar, make it available to bcbio: upgrade --tools --toolplus mutect=/path/to/mutect/mutect-1.1.7.jar

Note that muTect does not provide an easy way to query for the current version, so your input jar needs to include the version in the name.

System requirements

bcbio-nextgen provides a wrapper around external tools and data, so the actual tools used drive the system requirements. For small projects, it should install on workstations or laptops with a couple Gb of memory, and then scale as needed on clusters or multicore machines.

Disk space requirements for the tools, including all system packages are under 4Gb. Biological data requirements will depend on the genomes and aligner indices used, but a suggested install with GRCh37 and bowtie/bwa2 indexes uses approximately 35Gb of storage during preparation and ~25Gb after:

$ du -shc genomes/Hsapiens/GRCh37/*
3.8G  bowtie2
5.1G  bwa
3.0G  rnaseq-2014-05-02
3.0G  seq
340M  snpeff
4.2G  variation
4.4G  vep
23.5G total


Proxy or firewall problems

Some steps retrieve third party tools from GitHub, which can run into issues if you’re behind a proxy or block git ports. To instruct git to use https:// globally instead of git://:

$ git config --global url. git://

GATK or Java Errors

GATK and other software tools used by bcbio currently require Java 1.7. If you have a different version, you’ll see errors like:

Unsupported major.minor version 51.0

To fix this make sure you have Java 1.7 first in your PATH and that JAVA_HOME is either set to point to the same version, or not set. (unset JAVA_HOME).


Import errors with tracebacks containing Python libraries outside of the bcbio distribution (/path/to/bcbio/anaconda) are often due to other conflicting Python installations. bcbio tries to isolate itself as much as possible but external libraries can get included during installation due to the PYTHONHOME or PYTHONPATH environmental variables or local site libraries. These commands will temporary unset those to get bcbio installed, after which it should ignore them automatically:


Finally, having a .pydistutils.cfg file in your home directory can mess with where the libraries get installed. If you have this file in your home directory, temporarily renaming it to something else may fix your installation issue.

Manual process

The manual process does not allow the in-place updates and management of third party tools that the automated installer makes possible. It’s a more error-prone and labor intensive process. If you find you can’t use the installer we’d love to hear why to make it more amenable to your system. If you’d like to develop against a bcbio installation, see the documentation on setting up a Development infrastructure.

Tool Requirements

The code drives a number of next-generation sequencing analysis tools that you need to install on any machines involved in the processing. The CloudBioLinux toolkit provides automated scripts to help with installation for both software and associated data files:

fab -f cloudbiolinux/ -H localhost install_biolinux:flavor=ngs_pipeline_minimal

You can also install them manually, adjusting locations in the resources section of your bcbio_system.yaml configuration file as needed. The CloudBioLinux infrastructure provides a full list of third party software installed with bcbio-nextgen in `packages-conda.yaml`_, which lists all third party tools installed through Bioconda

Data requirements

In addition to existing bioinformatics software the pipeline requires associated data files for reference genomes, including pre-built indexes for aligners. The CloudBioLinux toolkit again provides an automated way to download and prepare these reference genomes:

fab -f -H localhost -c your_fabricrc.txt install_data_s3:your_biodata.yaml

The biodata.yaml file contains information about what genomes to download. The fabricrc.txt describes where to install the genomes by adjusting the data_files variable. This creates a tree structure that includes a set of Galaxy-style location files to describe locations of indexes:

├── galaxy
│   ├── tool-data
│   │   ├── alignseq.loc
│   │   ├── bowtie_indices.loc
│   │   ├── bwa_index.loc
│   │   ├── sam_fa_indices.loc
│   │   └── twobit.loc
│   └── tool_data_table_conf.xml
├── genomes
│   ├── Hsapiens
│   │   ├── GRCh37
│   │   └── hg19
│   └── phiX174
│       └── phix
└── liftOver

Individual genome directories contain indexes for aligners in individual sub-directories prefixed by the aligner name. This structured scheme helps manage aligners that don’t have native Galaxy .loc files. The automated installer will download and set this up automatically:

`-- phix
    |-- bowtie
    |   |-- phix.1.ebwt
    |   |-- phix.2.ebwt
    |   |-- phix.3.ebwt
    |   |-- phix.4.ebwt
    |   |-- phix.rev.1.ebwt
    |   `-- phix.rev.2.ebwt
    |-- bowtie2
    |   |-- phix.1.bt2
    |   |-- phix.2.bt2
    |   |-- phix.3.bt2
    |   |-- phix.4.bt2
    |   |-- phix.rev.1.bt2
    |   `-- phix.rev.2.bt2
    |-- bwa
    |   |-- phix.fa.amb
    |   |-- phix.fa.ann
    |   |-- phix.fa.bwt
    |   |-- phix.fa.pac
    |   |-- phix.fa.rbwt
    |   |-- phix.fa.rpac
    |   |-- phix.fa.rsa
    |   `--
    |-- novoalign
    |   `-- phix
    |-- seq
    |   |-- phix.dict
    |   |-- phix.fa
    |   `-- phix.fa.fai
    `-- ucsc
        `-- phix.2bit