This section provides useful concepts for getting started digging into the code and contributing new functionality. We welcome contributors and hope these notes help make it easier to get started.

bcbio dev installation

When developing, you’d like to avoid breaking your production bcbio instance. Use the installer script to create a separate bcbio instance without downloading any data. Before installing the second bcbio instance, investigate your PATH and PYTHONPATH variables and clean them from referenceing the production bcbio python. It is better to avoid mixing bcbio instances in the PATH. Also watch ~/.conda/environments.txt, ~/.condarc config files, CONDA_EXE, CONDA_PYTHON_EXE environment variables : having a reference to the production package cache: /n/app/bcbio/dev/anaconda/pkgs leads to bcbio/tools/bin not being populated in some installs.

To install in ${HOME}/local/share/bcbio (your location might be different, make sure you have ~30GB of disk quota there):

python ${HOME}/local/share/bcbio --tooldir=${HOME}/local --nodata --isolate

Make soft links to the data from your production bcbio instance (your installation path could be different from /n/app/bcbio):

ln -s /n/app/bcbio/biodata/genomes/ ${HOME}/local/share/genomes
ln -s /n/app/bcbio/biodata/galaxy/tool-data ${HOME}/local/share/bcbio/galaxy/tool-data

Create .bcbio_devel_profile to clear PATH from the production bcbio executables and reference the development version:

# use everything you need except of production bcbio
export PATH=/usr/local/bin:/usr/bin:/usr/local/sbin:/usr/sbin:
export PATH=${HOME}/local/share/bcbio/anaconda/bin:${HOME}/local/bin:$PATH
export CONDA_EXE=${HOME}/local/share/bcbio/anaconda/bin/conda
export CONDA_PYTHON_EXE=${HOME}/local/share/bcbio/anaconda/bin/python

Or directly call the testing bcbio: ${HOME}/local/share/bcbio/anaconda/bin/

Injecting bcbio code into bcbio installation

To install from your bcbio-nextgen source tree for testing do:

# make sure you are using the development bcbio instance
which bcbio_python
# local git folder
cd ~/code/bcbio-nextgen
bcbio_python install

One tricky part that we don’t yet know how to work around is that pip and standard install have different ideas about how to write Python eggs. install will create an isolated python egg directory like bcbio_nextgen-1.1.5-py3.6.egg, while pip creates an egg pointing to a top level bcbio directory. Where this gets tricky is that the top level bcbio directory takes precedence. The best way to work around this problem is to manually remove the current pip installed bcbio-nextgen code (rm -rf /path/to/anaconda/lib/python3.6/site-packages/bcbio*) before managing it manually with bcbio_python install. We’d welcome tips about ways to force consistent installation across methods.


The test suite exercises the scripts driving the analysis, so are a good starting point to ensure correct installation. Tests use the pytest framework. The tests are available in the bcbio source code:

git clone

There is a small wrapper script that finds the pytest and other dependencies pre-installed with bcbio you can use to run tests:

cd tests

You can use this to run specific test targets:

./ cancer
./ rnaseq
./ devel
./ docker

Optionally, you can run pytest directly from the bcbio install to tweak more options. It will be in /path/to/bcbio/anaconda/bin/pytest. Pass -s to pytest to see the stdout log, and -v to make pytest output more verbose. The -x flag will stop the test at the first failure and --lf will run only the tests that failed the last go-around. Sometimes it is useful to drop into the debugger on failure, wihch you can do by setting -s --pdb. The tests are marked with labels which you can use to run a specific subset of the tests using the -m argument:

pytest -m rnaseq

To run unit tests:

pytest tests/unit

To run integration pipeline tests:

pytest tests/integration

To run tests which use bcbio_vm:

pytest tests/bcbio_vm

To see the test coverage, add the --cov=bcbio argument to pytest.

By default the test suite will use your installed system configuration for running tests, substituting the test genome information instead of using full genomes. If you need a specific testing environment, copy tests/data/automated/post_process-sample.yaml to tests/data/automated/post_process.yaml to provide a test-only configuration.

The environment variable BCBIO_TEST_DIR can put the tests in a different directory if the full path to the new directory is specified. For example:

export BCBIO_TEST_DIR=$(pwd)/output

will put the output in your current working directory/output.

The test directory can be kept around after running by passing the --keep-test-dir flag.

Repeat a failed test:

export BCBIO_TEST_DIR=/path/to/test; \
pytest -s -x --keep-test-dir tests/integration/

New release checklist

  • [ ] pull from master to make sure you are up to date

  • [ ] inject the latest code to bcbio dev instance

  • [ ] run integration tests: pytest -s -x tests/integration/ - 24 tests, breaks after the first failed test

  • [ ] run unit tests: pytest -s -x tests/unit

  • [ ] create a branch release_xyz_prep

  • [ ] update version in and docs/

  • [ ] move all finished items from the pinned issue and add release date to, start new (in progress) section

  • [ ] commit and push changes to bcbio

  • [ ] draft new release, copy and paste changes from to the changelog

  • [ ] wait for bioconda-recipes to pick up the new release

  • [ ] review and approve bioconda recipe once it passes the tests

  • [ ] merge recipe by commenting @bioconda-bot please merge

  • [ ] wait until new version is available on bioconda

  • [ ] update requirements-conda.txt

  • [ ] update requirements.txt

  • [ ] push changes to bcbio

  • [ ] create a synchronized release of cloudbiolinux

  • [ ] run installation test (--no-data)

  • [ ] run T/N end2end test

not working for now

  • [ ] update BCBIO_VERSION in bcbio_docker

  • [ ] update BCBIO_REVISION in bcbio_docker

  • [ ] push changes to bcbio_docker

  • [ ] make sure the image builds successfully


bcbio-nextgen provides best-practice pipelines for automated analysis of high throughput sequencing data with the goal of being:

  • Quantifiable: Doing good science requires being able to accurately assess the quality of results and re-verify approaches as new algorithms and software become available.

  • Analyzable: Results feed into tools to make it easy to query and visualize the results.

  • Scalable: Handle large datasets and sample populations on distributed heterogeneous compute environments.

  • Reproducible: Track configuration, versions, provenance and command lines to enable debugging, extension and reproducibility of results.

  • Community developed: The development process is fully open and sustained by contributors from multiple institutions. By working together on a shared framework, we can overcome the challenges associated with maintaining complex pipelines in a rapidly changing area of research.

  • Accessible: Bioinformaticians, biologists and the general public should be able to run these tools on inputs ranging from research materials to clinical samples to personal genomes.

During development we seek to maximize functionality and usefulness, while avoiding complexity. Since these goals are sometimes in conflict, it’s useful to understand the design approaches:

  • Support high level configurability but avoid exposing all program options. Since pipelines support a wide variety of tools, each with a large number of options, we try to define configuration variables at high level based on biological intent and then translate these into best-practice options for each tool. The goal is to avoid having an overwhelming number of input configuration options.

  • Provide best-practice pipelines that make recommended decisions for processing. Coupled with goal of minimizing configuration parameters, this requires trust and discussion around algorithm choices. An example is bwa alignment, which uses bwa aln for reads shorter than 75bp and bwa mem for longer reads, based on recommendations from Heng Li. Our general goal is to encourage discussion and development of best-practices to make it easy to do the right thing.

  • Support extensive debugging output. In complex distributed systems, programs fail in unexpected ways even during production runs. We try to maximize logging to help identify and diagnose these type of unexpected problems.

  • Avoid making mistakes. This results in being conservative about decisions like deleting file intermediates. Coupled with extensive logging, we trade off disk usage for making it maximally easy to restart and debug problems. If you’d like to delete work or log directories automatically, we recommend doing this as part of your batch scripts wrapping bcbio-nextgen.

  • Strive for a clean, readable code base. We strive to make the code a secondary source of information after hand written docs. Practically, this means maximizing information content in source files while using in-line documentation to clarify as needed.

  • Focus on a functional coding style with minimal use of global mutable objects. This approach works well with distributed code and isolates debugging to individual functions rather than globally mutable state.

  • Make sure your changes integrate correctly by running the test suite before submitting a pull request. The pipeline is automatically tested in Travis-CI, and a red label will appear in the pull request if the former causes any issue.

Style guide


  • Delete unnecessary code (do not just comment it out)

  • Refactor existing code to help deliver new functionality

  • Specify exact version numbers for dependencies


  • Follow PEP 8 and PEP 20

  • Limit all lines to a maximum of 99 characters

  • Add docstrings to each module

  • Follow PEP 257 for docstrings:

    • the """ that ends a multiline docstring should be on a line by itself

    • for one-liner docstrings keep the closing """ on the same line

  • Clarify function calls with keyword arguments for readability

  • Use type hints


The most useful modules inside bcbio, ordered by likely interest:

  • pipeline – Top level functionality that drives the analysis pipeline. contains top level definitions of pipelines like variant calling and RNAseq, and is the best place to start understanding the overall organization of the code.

  • ngsalign – Integration with aligners for high-throughput sequencing data. We support individual aligners with their own separate modules.

  • variation – Tools for variant calling. Individual variant calling and processing approaches each have their own submodules.

  • rnaseq – Run RNA-seq pipelines, currently supporting TopHat/Cufflinks.

  • provenance – Track third party software versions, command lines and program flow. Handle writing of debugging details.

  • distributed – Handle distribution of programs across multiple cores, or across multiple machines using IPython.

  • workflow – Provide high level tools to run customized analyses. They tie into specialized analyses or visual front ends to make running bcbio-nextgen easier for specific common tasks.

  • broad – Code to handle calling Broad tools like GATK and Picard, as well as other Java-based programs.


bcbio-nextgen uses GitHub for code development, and we welcome pull requests. GitHub makes it easy to establish custom forks of the code and contribute those back. The Biopython documentation has great information on using git and GitHub for a community developed project. In short, make a fork of the bcbio code by clicking the Fork button in the upper right corner of the GitHub page, commit your changes to this custom fork and keep it up to date with the main bcbio repository as you develop. The GitHub help pages have detailed information on keeping your fork updated with the main GitHub repository (e.g. After commiting changes, click New Pull Request from your fork when you’d like to submit your changes for integration in bcbio.

Installing development tools

conda install --file requirements-dev.txt


To build this documentation locally and see how it looks like you can do so by installing the dependencies:

cd docs
conda install --file requirements-local.txt --file requirements.txt

and running:

make html

The documentation will be built under docs/_build/html, open index.html with your browser to load your local build.

Adding tools


Write new aligners within their own submodule inside the ngsalign directory. is a good example to follow along with. There are two functions to implement, based on which type of alignment you’d like to allow:

  • align_bam – Performs alignment given an input BAM file. Expected to return a sorted BAM output file.

  • align – Performs alignment given FASTQ inputs (gzipped or not). This is generally expected to implement an approach with unix-pipe that minimizes intermediates and disk IO, returning a sorted BAM output file. For back-compatibility this can also return a text based SAM file.

See the names section for more details on arguments.

Other required implementation details include:

  • galaxy_loc_file – Provides the name of the Galaxy loc file used to identify locations of indexes for this aligner. The automated installer sets up these loc files automatically.

  • remap_index_fn – A function that remaps an index from the Galaxy location file into the exact one for this aligner. This is useful for tools which aren’t supported by a Galaxy .loc file but you can locate them relative to another index.

Once implemented, plug the aligner into the pipeline by defining it as a _tool in bcbio/pipeline/ You can then use it as normal by specifying the name of the aligner in the aligner section of your configuration input.

Variant caller

New variant calling approaches live within their own module inside bcbio/variation. The implementation is a good example to follow for providing your own variant caller. Implement a function to run variant calling on multiple BAMs in an input region that takes the following inputs:

  • align_bams – A list of BAM files to call simultaneously.

  • items – List of data dictionaries associated with each of the samples in align_bams. Enables customization of variant calling based on sample configuration inputs. See documentation on the data dictionary for all of the information contained inside each data item.

  • ref_file – Fasta reference genome file.

  • assoc_files – Useful associated files for variant calling. This includes the DbSNP VCF file. It’s a named tuple mapping to files specified in the configuration. bcbio/pipeline/ has the available inputs.

  • region – A tuple of (chromosome, start, end) specifying the region to call in.

  • out_file– The output file to write to. This should contain calls for all input samples in the supplied region.

Once implemented, add the variant caller into the pipeline by updating caller_fns in the variantcall_sample function in bcbio/variation/ You can use it by specifying it in the variantcaller parameter of your sample configuration.

Adding new organisms

While bcbio-nextgen and supporting tools receive the most testing and development on human or human-like diploid organisms, the algorithms are generic and we strive to support the wide diversity of organisms used in your research. We welcome contributors interested in setting up and maintaining support for their particular research organism, and this section defines the steps in integrating a new genome. We also welcome suggestions and implementations that improve this process.

Setup CloudBioLinux to automatically download and prepare the genome:

  • Add the genome database key and organism name to list of supported organisms in the CloudBioLinux configuration (config/biodata.yaml).

  • Add download details to specify where to get the fasta genome files (cloudbio/biodata/ CloudBioLinux supports common genome providers like UCSC and Ensembl directly.

Add the organism to the supported installs within bcbio (in two places):

Test installation of genomes by pointing to your local cloudbiolinux edits during a data installation:

mkdir -p tmpbcbio-install
ln -s ~/bio/cloudbiolinux tmpbcbio-install upgrade --data --genomes DBKEY

Add configuration information to bcbio-nextgen by creating a config/genomes/DBKEY-resources.yaml file. Copy an existing minimal template like canFam3 and edit with pointers to snpEff and other genome resources. The VEP database directory has Ensembl names. SnpEff has a command to list available databases:

snpEff databases

Finally, send pull requests for CloudBioLinux and bcbio-nextgen and we’ll happily integrate the new genome.

This will provide basic integration with bcbio and allow running a minimal pipeline with alignment and quality control. We also have utility scripts in CloudBioLinux to help with preparing dbSNP (utils/ and RNA-seq (utils/ resources for some genomes. For instance, to prepare RNA-seq transcripts for mm9:

bcbio_python --genome-dir /path/to/bcbio/genomes Mmusculus mm9

We are still working on ways to best include these as part of the standard build and install since they either require additional tools to run locally, or require preparing copies in S3 buckets.

Enabling new MultiQC modules

MultiQC modules can be turned on in bcbio/qc/ bcbio collects the files to be used rather than searching through the work directory to support CWL workflows. Quality control files can be added by using the datadict.update_summary_qc function which adds the files in the appropriate place in the data dict. For example, here is how to add the quality control reports from bismark methylation calling:

    data = dd.update_summary_qc(data, "bismark", base=biasm_file)
    data = dd.update_summary_qc(data, "bismark", base=data["bam_report"])
    data = dd.update_summary_qc(data, "bismark", base=splitting_report)

Files that can be added for each tool in MultiQC can be found in the MultiQC module documentation

Standard function arguments


This dictionary provides lane and other BAM run group naming information used to correctly build BAM files. We use the rg attribute as the ID within a BAM file:

{'lane': '7_100326_FC6107FAAXX',
 'pl': 'illumina',
 'pu': '7_100326_FC6107FAAXX',
 'rg': '7',
 'sample': 'Test1'}


The data dictionary is a large dictionary representing processing, configuration and files associated with a sample. The standard work flow is to pass this dictionary between functions, updating with associated files from the additional processing. Populating this dictionary only with standard types allows serialization to JSON for distributed processing.

The dictionary is dynamic throughout the workflow depending on the step, but some of the most useful key/values available throughout are:

  • config – Input configuration variables about how to process in the algorithm section and locations of programs in the resources section.

  • dirs – Useful directories for building output files or retrieving inputs.

  • metadata – Top level metadata associated with a sample, specified in the initial configuration.

  • genome_resources – Naming aliases and associated files associated with the current genome build. Retrieved from organism specific configuration files (buildname-resources.yaml) this specifies the location of supplemental organism specific files like support files for variation and RNA-seq analysis.

It also contains information the genome build, sample name and reference genome file throughout. Here’s an example of these inputs:

{'config': {'algorithm': {'aligner': 'bwa',
                          'callable_regions': 'analysis_blocks.bed',
                          'coverage_depth': 'low',
                          'coverage_interval': 'regional',
                          'mark_duplicates': 'samtools',
                          'nomap_split_size': 50,
                          'nomap_split_targets': 20,
                          'num_cores': 1,
                          'platform': 'illumina',
                          'quality_format': 'Standard',
                          'realign': 'gkno',
                          'recalibrate': 'gatk',
                          'save_diskspace': True,
                          'upload_fastq': False,
                          'validate': '../reference_material/7_100326_FC6107FAAXX-grade.vcf',
                          'variant_regions': '../data/automated/variant_regions-bam.bed',
                          'variantcaller': 'freebayes'},
            'resources': {'bcbio_variation': {'dir': '/usr/share/java/bcbio_variation'},
                          'bowtie': {'cores': None},
                          'bwa': {'cores': 4},
                          'cortex': {'dir': '~/install/CORTEX_release_v1.0.5.14'},
                          'cram': {'dir': '/usr/share/java/cram'},
                          'gatk': {'cores': 2,
                                   'dir': '/usr/share/java/gatk',
                                   'jvm_opts': ['-Xms750m', '-Xmx2000m'],
                                   'version': '2.4-9-g532efad'},
                          'gemini': {'cores': 4},
                          'novoalign': {'cores': 4,
                                        'memory': '4G',
                                        'options': ['-o', 'FullNW']},
                          'picard': {'cores': 1,
                                     'dir': '/usr/share/java/picard'},
                          'snpEff': {'dir': '/usr/share/java/snpeff',
                                     'jvm_opts': ['-Xms750m', '-Xmx3g']},
                          'stampy': {'dir': '~/install/stampy-1.0.18'},
                          'tophat': {'cores': None},
                          'varscan': {'dir': '/usr/share/java/varscan'},
                          'vcftools': {'dir': '~/install/vcftools_0.1.9'}}},
'genome_resources': {'aliases': {'ensembl': 'human',
                                  'human': True,
                                  'snpeff': 'hg19'},
                      'rnaseq': {'transcripts': '/path/to/rnaseq/ref-transcripts.gtf',
                                 'transcripts_mask': '/path/to/rnaseq/ref-transcripts-mask.gtf'},
                      'variation': {'dbsnp': '/path/to/variation/dbsnp_132.vcf',
                                    'train_1000g_omni': '/path/to/variation/1000G_omni2.5.vcf',
                                    'train_hapmap': '/path/to/hg19/variation/hapmap_3.3.vcf',
                                    'train_indels': '/path/to/variation/Mills_Devine_2hit.indels.vcf'},
                      'version': 1},
 'dirs': {'fastq': 'input fastq directory',
              'galaxy': 'directory with galaxy loc and other files',
              'work': 'base work directory'},
 'metadata': {'batch': 'TestBatch1'},
 'genome_build': 'hg19',
 'name': ('', 'Test1'),
 'sam_ref': '/path/to/hg19.fa'}

Processing also injects other useful key/value pairs. Here’s an example of additional information supplied during a variant calling workflow:

{'prep_recal': 'Test1/7_100326_FC6107FAAXX-sort.grp',
 'summary': {'metrics': [('Reference organism', 'hg19', ''),
                         ('Total', '39,172', '76bp paired'),
                         ('Aligned', '39,161', '(100.0\\%)'),
                         ('Pairs aligned', '39,150', '(99.9\\%)'),
                         ('Pair duplicates', '0', '(0.0\\%)'),
                         ('Insert size', '152.2', '+/- 31.4')],
             'pdf': '7_100326_FC6107FAAXX-sort-prep-summary.pdf',
             'project': 'project-summary.yaml'},
 'validate': {'concordant': 'Test1-ref-eval-concordance.vcf',
              'discordant': 'Test1-eval-ref-discordance-annotate.vcf',
              'grading': 'validate-grading.yaml',
              'summary': 'validate-summary.csv'},
 'variants': [{'population': {'db': 'gemini/TestBatch1-freebayes.db',
                              'vcf': None},
               'validate': None,
               'variantcaller': 'freebayes',
               'vrn_file': '7_100326_FC6107FAAXX-sort-variants-gatkann-filter-effects.vcf'}],
 'vrn_file': '7_100326_FC6107FAAXX-sort-variants-gatkann-filter-effects.vcf',
 'work_bam': '7_100326_FC6107FAAXX-sort-prep.bam'}

Parallelization framework

bcbio-nextgen supports parallel runs on local machines using multiple cores and distributed on a cluster using IPython using a general framework.

The first parallelization step starts up a set of resources for processing. On a cluster this spawns a IPython parallel controller and set of engines for processing. The prun (parallel run) start function is the entry point to spawning the cluster and the main argument is a parallel dictionary which contains arguments to the engine processing command. Here is an example input from an IPython parallel run:

{'cores': 12,
 'type': 'ipython'
 'progs': ['aligner', 'gatk'],
 'ensure_mem': {'star': 30, 'tophat': 8, 'tophat2': 8},
 'module': 'bcbio.distributed',
 'queue': 'batch',
 'scheduler': 'torque',
 'resources': [],
 'retries': 0,
 'tag': '',
 'timeout': 15}

The cores and type arguments must be present, identifying the total cores to use and type of processing, respectively. Following that are arguments to help identify the resources to use. progs specifies the programs used, here the aligner, which bcbio looks up from the input sample file, and gatk. ensure_mem is an optional argument that specifies minimum memory requirements to programs if used in the workflow. The remaining arguments are all specific to IPython to help it spin up engines on the appropriate computing cluster.

A shared component of all processing runs is the identification of used programs from the progs argument. The run creation process looks up required memory and CPU resources for each program from the Resources section of your bcbio_system.yaml file. It combines these resources into required memory and cores using the logic described in the Memory management section of the parallel documentation. Passing these requirements to the cluster creation process ensures the available machines match program requirements.

bcbio-nextgen’s pipeline.main code contains examples of starting and using set of available processing engines. This example starts up machines that use samtools, gatk and cufflinks then runs an RNA-seq expression analysis:

with prun.start(_wprogs(parallel, ["samtools", "gatk", "cufflinks"]),
                samples, config, dirs, "rnaseqcount") as run_parallel:
    samples = rnaseq.estimate_expression(samples, run_parallel)

The pipelines often reuse a single set of machines for multiple distributed functions to avoid the overhead of starting up and tearing down machines and clusters.

The run_parallel function returned from the prun.start function enables running on jobs in the parallel on the created machines. The ipython wrapper code contains examples of implementing this. It is a simple function that takes two arguments, the name of the function to run and a set of multiple arguments to pass to that function:

def run(fn_name, items):

The items arguments need to be strings, lists and dictionaries to allow serialization to JSON format. The internals of the run function take care of running all of the code in parallel and returning the results back to the caller function.

In this setup, the main processing code is fully independent from the parallel method used so running on a single multicore machine or in parallel on a cluster return identical results and require no changes to the logical code defining the pipeline.

During re-runs, we avoid the expense of spinning up processing clusters for completed tasks using simple checkpoint files in the checkpoints_parallel directory. The prun.start wrapper writes these on completion of processing for a group of tasks with the same parallel architecture, and on subsequent runs will go through these on the local machine instead of parallelizing. The processing code supports these quick re-runs by checking for and avoiding re-running of tasks when it finds output files.

Plugging new parallelization approaches into this framework involves writing interface code that handles the two steps. First, create a cluster of ready to run machines given the parallel function with expected core and memory utilization:

  • num_jobs – Total number of machines to start.

  • cores_per_job – Number of cores available on each machine.

  • mem – Expected memory needed for each machine. Divide by cores_per_job to get the memory usage per core on a machine.

Second, implement a run_parallel function that handles using these resources to distribute jobs and return results. The multicore wrapper and ipython wrapper are useful starting points for understanding the current implementations.