Fresh installation (HPC cluster, server, AMI instance, Linux only)

1. Install bcbio package and tools script installs:

  • bcbio-nextgen python package;

  • python library dependencies;

  • third party analysis tools:

python3 [bcbio_path] --tooldir=[bcbio_tools_path] --nodata

You have to specify where to install bcbio in your filesystem and where to install tools, for example:

python3 /bcbio --tooldir=/bcbio/tools --nodata

or inside your home directory (make sure it has enough disk quota, 1.2.8 installation with no data takes ~37G - 44G depending on the filesystem):

python3 /home/user/bcbio --tooldir=/home/user/bcbio/tools --nodata

Installation takes 2h or more (depending on the throughput of your storage system and Internet connection). Recommended HPC job parameters for the installation process: 1 CPU core, 20 GB RAM (conda solves could take a lot of RAM).

By default, uses conda package manager (2h installation time without data as of bcbio1.2.8/2021-04-17), --mamba option switches to mamba package manager. The performance of conda vs mamba differs from version to version and the geographical location of the installation. Two examples: mid-2020 - conda installation took > 20h, sometimes w/o success, mamba installation - 30min, 2021/04 - conda installation ~ 2h, mamba installation is freezing, finished successfully after restart(s).

Check if installation works:

which --version

2. Install data

Bcbio needs reference files, indices, and databases. It is possible to install bcbio package and data at once, but we recommend to split these steps, because:

  • (i) some datatargets (dbNSFP, gnomad, snpEff) may take from tens of hours to several days to finish, they could break in the middle due to unstable connections, i.e. it is better to tackle them one by one;

  • (ii) you can re-use your data installation between bcbio instances. Data does not change much even between years, so you can just create symlinks /old_bcbio/genomes -> /new_bcbio/genomes, /old_bcbio/galaxy/tool-data -> /new_bcbio/galaxy/tool-data.

  • (iii) it is easier to debug conda/mamba issues (code installation) and cloudbiolinux issues (recipes) separately. upgrade -u skip --genomes hg38 --aligners bwa

This command installs hg38 human reference genome and bwa aligner index - the bare minimum required to run germline or somatic variant calling pipelines.

Installation notes

  • bcbio should install cleanly on Linux systems. For Mac OSX, we suggest trying bcbio-vm which runs bcbio on Cloud 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.

  • Don’t run the installer with sudo or as the root user. Do not use directories with : in the name, it is not POSIX compliant and will cause installation failures.

  • To use custom mirrors for conda-forge and bioconda channels used during bcbio installation, set appropriate channel alias in your .condarc configuration file.

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

  • Optional tool specific 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. You can override the Java 8 installed with bcbio by setting BCBIO_JAVA_HOME=/path/to/your/javadir if you have the Java you want in /path/to/your/javadir/bin/java.

    • 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 Pisces tumor-only variant callers requires the Microsoft .NET runtime.

  • 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.

Installation parameters

Run upgrade --help

to see all supported installation options: upgrade --help
usage: upgrade [-h] [--cores CORES] [--tooldir TOOLDIR]
                                [-u {stable,development,system,deps,skip}]
                                [--toolconf TOOLCONF] [--revision REVISION]
                                [--toolplus TOOLPLUS]
                                [--datatarget {variation,rnaseq,smallrna,gemini,vep,dbnsfp,dbscsnv,battenberg,kraken,ericscript,gnomad}]
                                [--genomes {GRCh37,hg19,hg38,hg38-noalt,mm10,mm9,rn6,rn5,canFam3,dm3,galGal4,phix,pseudomonas_aeruginosa_ucbpp_pa14,sacCer3,TAIR10,WBcel235,xenTro3,GRCz10,GRCz11,Sscrofa11.1,BDGP6}]
                                [--aligners {bwa,rtg,hisat2,bbmap,bowtie,bowtie2,minimap2,novoalign,twobit,bismark,snap,star,seq}]
                                [--data] [--cwl] [--isolate]
                                [--distribution {ubuntu,debian,centos,scientificlinux,macosx}]

optional arguments:
  -h, --help            show this help message and exit
  --cores CORES         Number of cores to use if local indexing is necessary.
  --tooldir TOOLDIR     Directory to install 3rd party software tools. Leave
                        unspecified for no tools
  --tools               Boolean argument specifying upgrade of tools. Uses
                        previously saved install directory
  -u {stable,development,system,deps,skip}, --upgrade {stable,development,system,deps,skip}
                        Code version to upgrade
  --toolconf TOOLCONF   YAML configuration file of tools to install
  --revision REVISION   Specify a git commit hash or tag to install
  --toolplus TOOLPLUS   Specify additional tool categories to install
  --datatarget {variation,rnaseq,smallrna,gemini,vep,dbnsfp,dbscsnv,battenberg,kraken,ericscript,gnomad}
                        Data to install. Allows customization or install of
                        extra data.
  --genomes {GRCh37,hg19,hg38,hg38-noalt,mm10,mm9,rn6,rn5,canFam3,dm3,galGal4,phix,pseudomonas_aeruginosa_ucbpp_pa14,sacCer3,TAIR10,WBcel235,xenTro3,GRCz10,GRCz11,Sscrofa11.1,BDGP6}
                        Genomes to download
  --aligners {bwa,rtg,hisat2,bbmap,bowtie,bowtie2,minimap2,novoalign,twobit,bismark,snap,star,seq}
                        Aligner indexes to download
  --data                Upgrade data dependencies
  --cwl                 Install code and data for running CWL workflows
  --isolate             Created an isolated installation without PATH updates
  --distribution {ubuntu,debian,centos,scientificlinux,macosx}
                        Operating system distribution

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/anaconda/bin:/path_to_bcbio/tools/bin:$PATH
  • --nodata Do not install genome data.


bcbio 1.2.9 has major changes in the conda environments. Please consider installing bcbio1.2.9 code/tools from scratch rather than upgrading from 1.2.8. You can re-use the data installation from bcbio<=1.2.8. snpeff databases has to be re-installed with the below command

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:

  • --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 gone 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.

  • Some aligners such as STAR don’t have pre-built indices due to the large file sizes of these. You set the number of cores to use for indexing with --cores 8.

  • For example, recommended HPC job parameters for upgrade -u skip --data --datatarget rnaseq --genomes GRCh37 are: 2 CPU cores, 2GB memory, and 2 hours run time.

Customizing data installation

bcbio supports the following genome references, 12 of them have additional data downloads. If you need a reference which is absent in the list, you may install it as a custom genome.

bcbio installs associated data files for sequence processing, and you’re able to customize this to install 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, including ExAC.

  • gnomadgnomAD is a large scale collection of genome variants, expanding on ExAC to include whole genome and more exome inputs. This is a large 25Gb download, available for human genome builds GRCh37, hg19 and hg38.

  • 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.

  • dbscsnvdbscSNV 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.

  • 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.

  • ericscript – Database for EricScript, based gene fusion detection. Supports hg38, hg19 and GRCh37.

  • TOPMedTOPMed Allele frequencies for whole genome variants from heart, lung, blood and sleep disorders. Supports hg38, hg19 and GRCh37.

For somatic analyses, bcbio includes COSMIC v68 for hg19 and GRCh37 only. Due to license restrictions, we cannot include updated versions of this dataset and hg38 support with the installer. To prepare these datasets yourself you can use a utility script shipped with cloudbiolinux that downloads, sorts and merges the VCFs, then copies into your bcbio installation:

export COSMIC_USER=""
export COSMIC_PASS="your_cosmic_password"
bcbio_python 89 /path/to/bcbio

/path/to/bcbio/ here is the directory one up from the genomes directory. The script removes variants marked as SNP in COSMIC, i.e. leaving only somatic variants. From version a minor portion of variants gets re-classified, for example: 3,779 variants were SNPs in cosmic-90 and became mutations in cosmic-92, 656 variants were mutations in cosmic-90 and became SNPs in cosmic-92.

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 option.

GATK and MuTect/MuTect2

bcbio includes an installation of GATK4, which is freely available for all uses. This is the default runner for HaplotypeCaller or MuTect2. If you want to use an older version of GATK, it requires manual installation. This is freely available for academic users, but requires a license for commercial use. It is not freely redistributable, so requires a manual download from the GATK download site, direct link. You also need to include tools_off: [gatk4] in your configuration for runs: see changing bcbio defaults.

To install GATK3, register with the pre-installed gatk bioconda wrapper:

gatk3-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 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 commercial 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 requirement for the tools, including all system packages is about 22GB (or more, depending on the type of the file system). 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

Most software tools used by bcbio require Java 1.8. bcbio distributes an OpenJDK Java build and uses it so you don’t need to install anything. Older versions of GATK (< 3.6) and MuTect require a locally installed Java 1.7. If you have version incompatibilities, you’ll see errors like:

Unsupported major.minor version 51.0

Fixing this requires either installing Java 1.7 for old GATK and MuTect or avoiding pointing to an incorrect java (unset JAVA_HOME). You can also tweak the java used by bcbio, described in the Automated installation section.


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 environment.

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

Maintain many bcbio installations

It is often asked how to reproduce older bcbio analyses when every update changes a lot in tools and in bcbio code. One of the solutions is the use of modules in HPC environemnt: You can have a bcbio/version module for every bcbio snapshot you need. They would consume <50G each, and a single large genomes folder could be symlinked to all of them. Data in genomes changes in a much slower pace compared to bcbio code and tools.