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:
wget https://raw.github.com/bcbio/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
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. Do not use directories with
: in the name, it is not
POSIX compliant and will cause installation failures.
The installation is highly customizable, and you can install
additional software and data later using
python bcbio_nextgen_install.py with no arguments to see options
for configuring the installation process. Some useful arguments are:
--isolateAvoid updating the user’s
~/.bashrcif 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:
--nodataDo not install genome data.
The machine will need to have some basic requirements for installing and running bcbio:
- Python 2.7, Python 3.x, or Python 2.6 plus the argparse dependency.
- Basic system setup for unpacking files: tar, gzip, unzip, bzip2, xz-utils.
- The git version control system (http://git-scm.com/)
- wget for file retrieval (https://www.gnu.org/software/wget/)
- Java 1.7, needed when running GATK < 3.6 or MuTect. This must be available in
your path so typing
java -versionresolves 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/javadirif you have the java you want in
- 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 for supported bcbio genomes. For genome builds not
included in the defaults, see the documentation on Adding custom genomes.
Following installation, you should edit the pre-created system configuration
/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:
bcbio_nextgen.py upgrade -u stable --tools --data
Tune the upgrade with these options:
-uType of upgrade to do for bcbio-nextgen code.
stablegets the most recent released version and
developmentretrieves the latest code from GitHub.
--datatargetCustomized installed data or download additional files not included by default: Customizing data installation
--toolplusSpecify additional tools to include. See the section on Extra software for more details.
--alignersoptions add additional aligner indexes to download and prepare.
bcbio_nextgen.py upgrade -hlists available genomes and aligners. If you want to install multiple genomes or aligners at once, specify
--alignersmultiple times, like this:
--genomes GRCh37 --genomes mm10 --aligners bwa --aligners bowtie2
- Leave out the
--toolsoption 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
--tooldirfor the first upgrade. You can also pass
--tooldirto install to a different directory.
- Leave out the
--dataoption 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
Customizing data installation¶
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
By default, bcbio will install data files for
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.
gnomad– gnomAD 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.
cadd– CADD 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
vepin the Variant calling configuration.
dbnsfpLike 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.
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. VEP will use dbscSNV for annotation of VCFs if included.
battenbergData files for Battenberg, which detects subclonality and copy number changes in whole genome cancer samples.
krakenDatabase for Kraken, optionally used for contamination detection.
ericscriptDatabase for EricScript, based gene fusion detection. 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="firstname.lastname@example.org" export COSMIC_PASS="cosmic_password" bcbio_python prepare_cosmic.py 83 /path/to/bcbio
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¶
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 commerical use. It is not freely
redistributable so requires a manual download from the GATK download site.
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:
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:
bcbio_nextgen.py 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:
bcbio_nextgen.py 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.
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
https:// globally instead of
$ git config --global url.https://github.com/.insteadOf git://github.com/
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
Import errors with tracebacks containing Python libraries outside of the bcbio
/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:
$ unset PYTHONHOME $ unset PYTHONPATH $ export PYTHONNOUSERSITE=1
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.
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.
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/fabfile.py -H localhost install_biolinux:flavor=ngs_pipeline_minimal
You can also install them manually, adjusting locations in the
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
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 data_fabfile.py -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 | `-- phix.fa.sa |-- novoalign | `-- phix |-- seq | |-- phix.dict | |-- phix.fa | `-- phix.fa.fai `-- ucsc `-- phix.2bit