Code

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.

Development goals

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.

Overview

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

  • pipeline – Top level functionality that drives the analysis pipeline. main.py 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.

Development infrastructure

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, then click New Pull Request from your fork when you’d like to submit your changes for integration in bcbio.

For developing and testing changes locally, you can install directly into a bcbio-nextgen installation. The automated bcbio-nextgen installer creates an isolated Python environment using Anaconda. This will be a subdirectory of your installation root, like /usr/local/share/bcbio-nextgen/anaconda. The installer also includes a bcbio_python executable target which is the python in this isolated anaconda directory. You generally will want to make changes to your local copy of the bcbio-nextgen code and then install these into the code directory. To install from your bcbio-nextgen source tree for testing do:

bcbio_python setup.py install

One tricky part that we don’t yet know how to work around is that pip and standard setup.py install have different ideas about how to write Python eggs. setup.py install will create an isolated python egg directory like bcbio_nextgen-0.7.5a-py2.7.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/python2.7/site-packages/bcbio*) before managing it manually with bcbio_python setup.py install. We’d welcome tips about ways to force consistent installation across methods.

If you want to test with bcbio_nextgen code in a separate environment from your work directory, we recommend using the installer to install only the bcbio code into a separate directory:

python bcbio_nextgen_install.py /path/to/testbcbio --nodata --isolate

Then add this directory to your PATH before your bcbio installation with the tools: export PATH=/path/to/testbcbio/anaconda/bin:$PATH, or directly calling the testing bcbio /path/to/testbcbio/anaconda/bin/bcbio_nextgen.py.

Building the documentation locally

If you have added or modified this documentation, to build it locally and see how it looks like you can do so by running:

cd docs
make html

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

If you want to use the same theme that Read The Docs uses, you can do so by installing sphinx_rtd_theme via pip. You will also need to add this in the docs/conf.py file to use the theme only locally:

html_theme = 'default'
on_rtd = os.environ.get('READTHEDOCS', False)
if not on_rtd:  # only import and set the theme if we're building docs locally
    import sphinx_rtd_theme
    html_theme = 'sphinx_rtd_theme'
    html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]

Adding tools

Aligner

Write new aligners within their own submodule inside the ngsalign directory. bwa.py 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/alignment.py. 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 freebayes.py 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. Having multiple configurations allows customization of sample specific variant calls using parameters supplied to Sample information.
  • 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/shared.py 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/genotype.py. 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/genomes.py). CloudBioLinux supports common genome providers like UCSC and Ensembl directly.

Add the organism to the supported installs within bcbio:

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
bcbio_nextgen.py 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/prepare_dbsnp.py) and RNA-seq (utils/prepare_tx_gff.py) resources for some genomes. For instance, to prepare RNA-seq transcripts for mm9:

bcbio_python prepare_tx_gff.py --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.

Standard function arguments

names

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'}

data

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.