Validating generalized incremental joint variant calling with GATK HaplotypeCaller, FreeBayes, Platypus and samtools
Incremental joint variant calling
Variant calling in large populations is challenging due to the difficulty in providing a consistent set of calls at all possible variable positions. A finalized set of calls from a large population should distinguish reference calls, without a variant, from no calls, positions without enough read support to make a call. Calling algorithms should also be able to make use of information from other samples in the population to improve sensitivity and precision.
There are two issues with trying to provide complete combined call sets. First, it is computationally expensive to call a large number of samples simultaneously. Second, adding any new samples to a callset requires repeating this expensive computation. This N+1 problem highlights the inflexibility around simultaneous pooled calling of populations.
The GATK team’s recent 3.x release has a solution to these issues: Incremental joint variant discovery. The approach calls samples independently but produces a genomic VCF (gVCF) output for each individual that contains probability information for both variants and reference calls at non-variant positions. The genotyping step combines these individual gVCF files, making use of the information from the independent samples to produce a final callset.
We added GATK incremental joint calling to bcbio-nextgen along with a generalized implementation that performs joint calling with other variant callers. Practically, bcbio now supports this approach with four variant callers:
- GATK HaplotypeCaller (3.2-2) – Follows current GATK recommended best practices for calling, with Variant Quality Score Recalibration used on whole genome and large population callsets. This uses individual sample gVCFs as inputs to joint calling.
- FreeBayes (0.9.14-15) – A haplotype-based caller from Erik Garrison in Gabor Marth’s lab.
- Platypus (0.7.9.2) – A recently published haplotype-based variant caller from Andy Rimmer at the Wellcome Trust Centre for Human Genomics.
- samtools (1.0) – The recently released version of samtools and bcftools with a new multiallelic calling method. John Marshall, Petr Danecek, James Bonfield and Martin Pollard at Sanger have continued samtools development from Heng Li’s code base.
The implementation includes integrated validation against the Genome in a Bottle NA12878 reference standard, allowing comparisons between joint calling, multi-sample pooled calling and single sample calling. Sensitivity and precision for joint calling is comparable to pooled calling, suggesting we should optimize design of variant processing to cater towards individual calling and subsequent finalization of calls, rather than pooling. Generalized joint calling enables combining multiple sets of calls under an identical processing framework, which will be important as we seek to integrate large publicly available populations to extract biological signal in complex multi-gene diseases.
There is not a consistent set of terminology around combined variant calling, but to develop one, here is how I’ll use the terms:
- Joint calling – Calling a group of samples together with algorithms that do not need simultaneous access to all population BAM files. GATK’s incremental joint calling uses gVCF intermediates. Our generalized implementation performs recalling using individual BAMs supplemented with a combined VCF file of variants called in all samples.
- Pooled or batch calling – Traditional grouped sample calling, where algorithms make use of read data from all BAM files of a group. This scales to smaller batches of samples.
- Single sample calling – Variant calling with a single sample only, not making use of information from other samples.
- Squaring off or Backfilling – Creating a VCF file from a group of samples that distinguishes reference from no-call at every position called as a variant in one of the samples. With a squared off VCF, we can use the sample matrix to consider call rate at any position. Large populations called in smaller batches will not be able to distinguish reference from no-call at variants unique to each sub-pool, so will need to be re-processed to achieve this.
bcbio-nextgen automates the calling and validation used in this comparison. We aim to make it easy to install, use and extend.
For GATK HaplotypeCaller based joint genotyping, we implement the GATK best practices recommended by the Broad. Individual sample variant calls produce a gVCF output file that contains both variants as well as probability information about reference regions. Next, variants are jointly called using GenotypeGVFs to produce the final population VCF file.
For the other supported callers – FreeBayes, Platypus and samtools – we use a generalized recalling approach, implemented in bcbio.variation.recall. bcbio-nextgen first calls each individual sample as a standard VCF. We then combine these individual sample VCFs into a global summary of all variant positions called across all samples. Finally we recall at each potential variant position, producing a globally squared off joint callset for the sample that we merge into the final joint VCF. This process parallelizes by chromosome region and by sample, allowing efficient use of resources in both clusters and large multiple core machines.
bcbio.variation.recall generalizes to any variant caller that supports recalling with an input set of variants. Knowing the context of potential variants helps inform better calling. This method requires having the individual sample BAM file available to perform recalling. Having the reads present does provide the ability to improve recalling by taking advantage of realigning reads into haplotypes given known variants, an approach we’ll explore more in future work. The implementation is also general and could support gVCF based combining as this becomes available for non-GATK callers.
Generalized joint calling
We evaluated all callers against the NA12878 Genome in a Bottle reference standard using the NA12878/NA12891/NA12892 trio from the CEPH 1463 Pedigree, with 50x whole genome coverage from Illumina’s platinum genomes. The validation provides putative true positives (concordant), false negatives (discordant missing), and false positives (discordant extra) for all callers:
Overall, there is not a large difference in sensitivity and precision for the four methods, giving us four high-quality options for performing joint variant calling on germline samples. The post-calling filters provide similar levels of false positives to enable comparisons of sensitivity. Notably, samtools new calling method is now as good as other approaches, in contrast with previous evaluations, demonstrating the value of continuing to improve open source tools and having updated benchmarks to reflect these improvements.
Improving sensitivity and precision is always an ongoing process and this evaluation identifies some areas to focus on for future work:
- Platypus SNP and indel calling is slightly less sensitive than other approaches. We worked on Platypus calling parameters and post-call filtering to increase sensitivity from the defaults without introducing a large number of false positives, but welcome suggestions for more improvements.
- samtools indel calling needs additional work to reduce false positive indels in joint and pooled calling. There is more detail on this below in the comparison with single sample samtools calling.
Joint versus pooled versus single approaches
We validated the same NA12878 trio with pooled and single sample calling to assess the advantages of joint calling over single sample, and whether joint calling is comparable in quality to calling simultaneously. The full evaluation for pooled calling shows that performance is similar to joint methods:
If you plot joint, pooled and single sample calling next to each other there are some interesting small differences between approaches that identify areas for further improvement. As an example, here are GATK HaplotypeCaller and samtools with the three approaches presented side by side:
GATK HaplotypeCaller sensitivity and precision are close between the three methods, with small trade offs for different methods. For SNPs, pooled calling is most sensitive at the cost of more false positives, and single calling is more precise at the cost of some sensitivity. Joint calling is intermediate between these two extremes. For indels, joint calling is the most sensitive at the cost of more false positives, with pooled calling falling between joint and single sample calling.
For samtools, precision is currently best tuned for single sample calling. Pooled calling provides better sensitivity, but at the cost of a larger number of false positives. The joint calling implementation regains a bit of this sensitivity but still suffers from increased false positives. The authors of samtools tuned variant calling nicely for single samples, but there are opportunities to increase sensitivity when incorporating multiple samples via a joint method.
Generally, we don’t expect the same advantages for pooled or joint calling in a trio as we’d see in a larger population. However, even for this small evaluation population we can see the improvements available by considering additional variant information from other samples. For Platypus we unexpectedly had better calls from joint calling compared to pooled calling, but expect these differences to harmonize over time as the tools continue to improve.
Overall, this comparison identifies areas where we can hope to improve generalized joint calling. We plan to provide specific suggestions and feedback to samtools, Platypus and other tool authors as part of a continuous validation and feedback process.
Reproducing and extending the analysis
All variant callers and calling methods validated here are available for running in bcbio-nextgen. bcbio automatically installs the generalized joint calling implementation, and it is also available as a java executable at bcbio.variation.recall. All tools are freely available, open source and community developed and we welcome your feedback and contributions.
The documentation contains full instructions for running the joint analysis. This is an extended version of previous work on validation of trio calling and uses the same input dataset with a bcbio configuration that includes single, pooled and joint calling:
mkdir -p NA12878-trio-eval/config NA12878-trio-eval/input NA12878-trio-eval/work-joint cd NA12878-trio-eval/config cd ../input wget https://raw.github.com/chapmanb/bcbio-nextgen/master/config/examples/NA12878-trio-wgs-validate-getdata.sh bash NA12878-trio-wgs-validate-getdata.sh wget https://raw.github.com/chapmanb/bcbio-nextgen/master/config/examples/NA12878-trio-wgs-joint.yaml cd ../work_joint bcbio_nextgen.py ../config/NA12878-trio-wgs-joint.yaml -n 16
Having a general joint calling implementation with good sensitivity and precision is a starting point for more research and development. To build off this work we plan to:
- Provide better ensemble calling methods that scale to large multi-sample calling projects.
- Work with FreeBayes, Platypus and samtools tool authors to provide support for gVCF style files to avoid the need to have BAM files present during joint calling, and to improve sensitivity and precision during recalling-based joint approaches.
- Combine variant calls with local reassembly to improve sensitivity and precision. Erik Garrison’s glia provides streaming local realignment given a set of potential variants. Jared Simpson used the SGA assembler to combine FreeBayes calls with de-novo assembly. Ideally we could identify difficult regions of the genome based on alignment information and focus more computationally expensive assembly approaches there.
We plan to continue working with the open source scientific community to integrate, extend and improve these tools and are happy for any feedback and suggestions.
Structural variant detection goals
This post describes community based work integrating structural variant calling and validation into bcbio-nextgen. I’ve previously written about approaches for validating single nucleotide changes (SNPs) and small insertions/deletions (Indels), but it has always been unfortunate to not have reliable ways to detect larger structural variations: deletions, duplications, inversions, translocations and other disruptive events. Detecting these events with short read sequencing is difficult, and our goal in bcbio is to create a global summary of predicted structural variants from multiple callers alongside measures of sensitivity and precision.
The latest release of bcbio automates structural variant calling with three callers:
- LUMPY – a probabilistic structural variant caller incorporating both split read and read pair discordance, developed by Ryan Layer in Aaron Quinlan and Ira Hall’s labs.
- DELLY – an integrated paired-end and split-end structural variant caller developed by Tobias Rausch.
- cn.mops – a read count based copy number variation (CNV) caller developed by Günter Klambauer.
bcbio integrates structural variation predictions from all approaches into a high level BED file. This is a first pass way to identify potentially disruptive large scale events. Here are example regions: a duplication called by all 3 callers, a deletion called by 2 callers, and a complex region with both deletions and duplications.
9 139526855 139527537 DUP_delly,DUP_lumpy,cnv3_cn_mops 10 99034861 99037400 DEL_delly,cnv0_cn_mops,cnv1_cn_mops 12 8575814 8596742 BND_lumpy,DEL_delly,DEL_lumpy,DUP_delly,DUP_lumpy,cnv1_cn_mops,cnv3_cn_mops
This output associates larger structural events with regions of interest in a high level way, while allowing us to quickly determine the individual tool support for each event. Using this, we are no longer blind to potential structural changes and can use the summary to target in-depth investigation with the more detailed metrics available from each caller and a read viewer like PyBamView. The results can also help inform prioritization of SNP and Indel calls since structural rearrangements often associate with false positives. Longer term we hope this structural variant summary and comparison work will be useful for community validation efforts like the Global Alliance for Genomics and Health benchmarking group, the Genome in a Bottle consortium and the ICGC-TCGA DREAM Mutation Calling challenge.
Below I’ll describe a full evaluation of the sensitivity and precision of this combined approach using an NA12878 trio, as well as describe how to run and extend this work using bcbio-nextgen.
This blog post is the culmination of a lot of work and support from the open source bioinformatics community. David Jenkins worked with our our group for the summer and headed up evaluation of structural variation results. We received wonderful support from Colby Chang, Ryan Layer, Ira Hall and Aaron Quinlan on both LUMPY and structural variation validation in general. They freely shared scripts and datasets for evaluation, which gave us the materials to make these comparisons. Günter Klambauer gave us great practical advice on using cn.mops. Tobias Rausch helped immensely with tips for speeding up DELLY on whole genomes, and Ashok Ragavendran from Mike Talkowski’s lab generously discussed tricks for scaling DELLY runs. Harvard Research Computing provided critical support and compute for this analysis as part of a collaboration with Intel.
To validate the output of this combined structural variant calling approach we used a set of over 4000 validated deletions made available by Ryan Layer as part of the LUMPY manuscript. These are deletion calls in NA12878 with secondary support evidence from Moleculo and PacBio datasets. We further subset these regions by removing calls in low complexity regions identified in Heng Li’s variant calling artifacts paper. An alternative set of problematic regions are the potential misassembly regions identified by Colby Chang and Ira Hall during LUMPY and speedseq development (Edit: The original post mistakenly mentioned these overlap significantly with low complexity regions, but that is only due to overlap in obvious problem areas like the N gap regions. We’ll need additional work to incorporate both regions into future evaluations. Thanks to Colby for catching this error.). These are a useful proxy for regions we’re currently not able to reliably call structural variants in.
We ran all structural variant callers using the NA12878/NA12891/NA12892 trio from the CEPH 1463 Pedigree as an input dataset. This consists of 50x whole genome reads from Illumina’s platinum genomes project, and is the same dataset used in our previous analyses of population based SNP and small indel calling.
Our goal is to define boundaries on sensitivity – the percentage of validated calls we detect – and precision – how many of the total calls overlap with validated regions. We required a simple overlap of the called regions with validated regions to consider a called variant as validated, and stratified results by event size to quantify detection metrics at different size ranges.
The comparison highlights the value of providing a combined call set. I’d caution against using this as a comparison between methods. Accurate structural variation calling depends on multiple sources of evidence and we still have work to do in improving our ability to filter for maximal sensitivity and specificity. The ensemble method in the figure below displays results of our final calls, made from collapsing structural variant calls from all three input callers:
Across all size classes, we detect approximately half of the structural variants and expect that about half of the called events are false positives. Smaller structural variants of less than 1kb are the most difficult to detect with these methods. Larger events from 1kb to 25kb have better sensitivity and precision. As the size of the events increase precision decreases, so larger called events tend to have more false positives.
Beyond the values for sensitivity and precision, the biggest takeaway is that combining multiple callers helps detect additional variants we’d miss with any individual caller. Count based callers like cn.mops enable improved sensitivity on large deletions but don’t resolve small deletions at 50x depth using our current settings, although tuning can help detect these smaller sized events as well. Similarly, lumpy and delly capture different sets of variants across all of the size classes.
The comparison also emphasizes the potential for improving both individual caller filtering and ensemble structural variant preparation. The ensemble method uses bedtools to create a merged superset of all individually called regions. This is the simplest possible approach to combine calls. Similarly, individual caller filters are intentionally simple as well. cn.mops calling performs no additional filtering beyond the defaults, and could use adjustment to detect and filter smaller events. Our DELLY filter requires 4 supporting reads or both split and paired read evidence. Our LUMPY filter require at least 4 supporting reads to retain an event. We welcome discussion of the costs and tradeoffs of these approaches. For instance, requiring split and paired evidence for DELLY increases precision at the cost of sensitivity. These filters are a useful starting point and resolution, but we hope to continue to refine and improve them over time.
bcbio-nextgen handles installation and automation of the programs used in this comparison. The documentation contains instructions to download the data and run the NA12878 trio calling and validation. This input configuration file should be easily adjusted to run on your data of interest.
The current implementation has reasonable run times for whole genome structural variant calling. We use samblaster to perform duplicate marking alongside identification of discordant and split read pairs. The aligned reads from bwa stream directly into samblaster, adding minimal processing time to the run. For LUMPY calling, the pre-prepared split and discordant reads feed directly into speedseq, which nicely automates the process of running LUMPY. For DELLY, we subsample correct pairs in the input BAM to 50 million reads and combine with the pre-extracted problematic pairs to improve runtimes for whole genome inputs.
We processed three concurrently called 50x whole genome samples from FASTQ reads to validated structural variants in approximately 3 days using 32 cores. Following the preparation work described above, LUMPY calling took 6 hours, DELLY takes 24 hours parallelized on 32 cores and cn.mops took 16 hours parallelized by chromosome on 16 cores. This is a single data point for current capabilities, and is an area where we hope to continue to improve scalability and parallelization.
The implementation and validation are fully integrated into the community developed bcbio-nextgen project and we hope to expand this work to incorporate additional structural variant callers like Pindel and CNVkit, as well as improving filtering and ensemble calling. We also want to expand structural variant validation to include tumor/normal cancer samples and targeted sequencing. We welcome contributions and suggestions on current and future directions in structural variant calling.
Whole genome trio variant calling evaluation: low complexity regions, GATK VQSR and high depth filters
Whole genome trio validation
I’ve written previously about the approaches we use to validate the bcbio-nextgen variant calling framework, specifically evaluating aligners and variant calling methods and assessing the impact of BAM post-alignment preparation methods. We’re continually looking to improve both the pipeline and validation methods and two recent papers helped advance best-practices for evaluating and filtering variant calls:
- Michael Linderman and colleagues describe approaches for validating clinical exome and whole genome sequencing results. One key result I took from the paper was the difference in assessment between exome and whole genome callsets. Coverage differences due to capture characterize discordant exome variants, while complex genome regions drive whole genome discordants. Reading this paper pushed us to evaluate whole genome population based variant calling, which is now feasible due to improvements in bcbio-nextgen scalability.
- Heng Li identified variant calling artifacts and proposed filtering approaches to remove them, as well as characterizing caller error rates. We’ll investigate two of the filters he proposed: removing variants in low complexity regions, and filtering high depth variants.
We use the NA12878/NA12891/NA12892 trio from the CEPH 1463 Pedigree as an input dataset, consisting of 50x whole genome reads from Illumina’s platinum genomes. This enables both whole genome comparisons, as well as pooled family calling that replicates best-practice for calling within populations. We aligned reads using bwa-mem and performed streaming de-duplication detection with samblaster. Combined with no recalibration or realignment based on our previous assessment, this enabled fully streamed preparation of BAM files from input fastq reads. We called variants using two realigning callers: FreeBayes (v0.9.14-7) and GATK HaplotypeCaller (3.1-1-g07a4bf8) and evaluated calls using the Genome in a Bottle reference callset for NA12878 (v0.2-NIST-RTG). The bcbio-nextgen documentation has full instructions for reproducing the analysis.
This work provides three practical improvements for variant calling and validation:
- Low complexity regions contribute 45% of the indels in whole genome evaluations, and are highly variable between callers. This replicates Heng’s results and Michael’s assessment of common errors in whole genome samples, and indicates we need to specifically identify and assess the 2% of the genome labeled as low complexity. Practically, we’ll exclude them from further evaluations to avoid non-representative bias, and suggest removing or flagging them when producing whole genome variant calls.
- We add a filter for removing false positives from FreeBayes calls in high depth, low quality regions. This removes variants in high depth regions that are likely due to copy number or other larger structural events, and replicates Heng’s filtering results.
- We improved settings for GATK variant quality recalibration (VQSR). The default VQSR settings are conservative for SNPs and need adjustment to be compatible with the sensitivity available through FreeBayes or GATK using hard filters.
Low complexity regions
Low complexity regions (LCRs) consist of locally repetitive sections of the genome. Heng’s paper identified these using mdust and provides a BED file of LCRs covering 2% of the genome. Repeats in these regions can lead to artifacts in sequencing and variant calling. Heng’s paper provides examples of areas where a full de-novo assembly correctly resolves the underlying structure, while local reassembly variant callers do not.
To assess the impact of low complexity regions on variant calling, we compared calls from FreeBayes and GATK HaplotypeCaller to the Genome in a Bottle reference callset with and without low complexity regions included. The graph below shows concordant non-reference variant calls alongside discordant calls in three categories: missing discordants are potential false negatives, extras are potential false positives, and shared are variants that overlap between the evaluation and reference callsets but differ due to zygosity (heterozygote versus homozygote) or indel representation.
- For SNPs, removing low complexity regions removes approximately ~2% of the total calls for both FreeBayes and GATK. This corresponds to the 2% of the genome subtracted by removing LCRs.
- For indels, removing LCRs removes 45% of the calls due to the over-representation of indels in repeat regions. Additionally, this results in approximately equal GATK and FreeBayes concordant indels after LCR removal. Since the Genome in a Bottle reference callset uses GATK HaplotypeCaller to resolve discrepant calls, this change in concordance is likely due to bias towards GATK’s approaches for indel resolution in complex regions.
- The default GATK VQSR calls for SNPs are not as sensitive, relative to FreeBayes calls. I’ll describe additional work to improve this below.
Practically, we’ll now exclude low complexity regions in variant comparisons to avoid potential bias and more accurately represent calls in the remaining non-LCR genome. We’ll additionally flag low complexity indels in non-evaluation callsets as likely to require additional followup. Longer term, we need to incorporate callers specifically designed for repeats like lobSTR to more accurately characterize these regions.
High depth, low quality, filter for FreeBayes
The second filter proposed in Heng’s paper was removal of high depth variants. This was a useful change in mindset for me as I’ve primarily thought about removing low quality, low depth variants. However, high depth regions can indicate potential copy number variations or hidden duplicates which result in spurious calls.
Comparing true and false positive FreeBayes calls with a pooled multi-sample call quality of less than 500 identifies a large grouping of false positive heterozygous variants at a combined depth, across the trio, of 200:
The cutoff proposed by Heng was to calculate the average depth of called variants and set the cutoff as the average depth plus a multiplier of 3 or 4 times the square root of average depth. This dataset was an average depth of 169 for the trio, corresponding to a cutoff of 208 if we use the 3 multiplier, which compares nicely with a manual cutoff you’d set looking at the above graphs. Applying a cutoff of QUAL < 500 and DP > 208 produces a reduction in false positives with little impact on sensitivity:
A nice bonus of this filter is that it makes intuitive sense: variants with high depth and low quality indicate there is something problematic, and depth manages to partially compensate for the underlying issue. Inspired by GATK’s QualByDepth annotation and default filter of QD < 2.0, we incorporated a generalized version of this into bcbio-nextgen’s FreeBayes filter: QUAL < (depth-cutoff * 2.0) and DP > depth-cutoff.
GATK variant quality score recalibration (VQSR)
The other area where we needed to improve was using GATK Variant Quality Score Recalibration. The default parameters provide a set of calls that are overly conservative relative to the FreeBayes calls. VQSR provides the ability to tune the filtering so we experimented with multiple configurations to achieve approximately equal sensitivity relative to FreeBayes for both SNPs and Indels. The comparisons use the Genome in a Bottle reference callset for evaluation, and include VQSR default settings, multiple tranche levels and GATK’s suggested hard filters:
While the sensitivity/specificity tradeoff depends on the research question, in trying to set a generally useful default we’d like to be less conservative than the GATK VQSR default. We learned these tips and tricks for tuning VQSR filtering:
- The default setting for VQSR is not a tranche level (like 99.0), but rather a LOD score of 0. In this experiment, that corresponded to a tranche of ~99.0 for SNPs and ~98.0 for indels. The best-practice example documentation uses command line parameter that specify a consistent tranche of 99.0 for both SNPs and indels, so depending on which you follow as a default you’ll get different sensitivities.
- To increase sensitivity, increase the tranche level. My expectations were that decreasing the tranche level would include more variants, but that actually applies additional filters. My suggestion for understanding tranche levels is that they specify the percentage of variants you want to capture; a tranche of 99.9% captures 99.9% of the true cases in the training set, while 99.0% captures less.
- We found tranche settings of 99.97% for SNPs and 98.0% for indels correspond to roughly the sensitivity/specificity that you achieve with FreeBayes. These are the new default settings in bcbio-nextgen.
- Using hard filtering of variants based on GATK recommendations performs well and is also a good default choice. For SNPs, the hard filter defaults are less conservative and more in line with FreeBayes results than VQSR defaults. VQSR has improved specificity at the same sensitivity and has the advantage of being configurable, but will require an extra tuning step.
Overall VQSR provides good filtering and the ability to tune sensitivity but requires validation work to select tranche cutoffs that are as sensitive as hard filter defaults, since default values tend to be overly conservative for SNP calling. In the absence of the ability or desire to tune VQSR tranche levels, the GATK hard filters provide a nice default without much of a loss in precision.
Data availability and future work
Thanks to continued community work on improving variant calling evaluations, this post demonstrates practical improvements in bcbio-nextgen variant calling. We welcome interested contributors to re-run and expand on the analysis, with full instructions in the bcbio-nextgen example pipeline documentation. Some of the output files from the analysis may also be useful:
- VCF files for FreeBayes true positive and false positive heterozygote calls, used here to improve filtering via assessment of high depth regions. Heterozygotes make up the majority of false positive calls so take the most work to correctly filter and detect.
- Shared false positives from FreeBayes and GATK HaplotypeCaller. These are potential missing variants in the Genome in a Bottle reference. Alternatively, they may represent persistent errors found in multiple callers.
We plan to continue to explore variant calling improvements in bcbio-nextgen. Our next steps are to use the trio population framework to compared pooled population calling versus the incremental joint discovery approach introduced in GATK 3. We’d also like to compare with single sample calling followed by subsequent squaring off/backfilling to assess the value of concurrent population calling. We welcome suggestions and thoughts on this work and future directions.
bcbio-nextgen is a community developed, best-practice pipeline for genomic data processing, performing automated variant calling and RNA-seq analyses from high throughput sequencing data. It has an automated installation script that sets up the code and third party tools used during analysis, and we’ve been working on improving the process to make getting started with bcbio-nextgen easier. The current approach of installing tools in a separate semi-isolated directory is non-optimal for a couple of reasons:
- A separate directory does not give full isolation from system programs and libraries. It’s possible to disrupt processing by unintentionally including other command line programs into your PATH. Additionally it is not easy to recreate a snapshot of the current environment for reproducibility without manual re-installation of specific versions of software.
- The automated installation script needs to deal with the peculiarities of heterogeneous cluster environments. Different system characteristics can be tricky to anticipate and automate, and lead to more tickets devoted to install problems than we’d like. The goal is to do more science and spend less time dealing with installation woes.
Docker lightweight linux containers help solve both of these issues. By isolating tools and software involved in processing, installation is as easy as downloading a pre-built image containing the software. By containerizing the running process, software does not interfere with other installed programs. Docker containers provide the isolation and deployment advantages of Virtual Machines without the associated overhead. Additionally they allow easy export of the full software environment used to run an analysis, improving our ability to reproduce results.
This post describes bcbio-nextgen-vm, a wrapper around bcbio-nextgen that runs analyses using pre-created Docker containers. The implementation is feature compatible with bcbio-nextgen but provides improved installation, isolation and reproducibility. I’ll also discuss future work to further improve provenance and traceability of analysis runs with the Arvados platform, and a fun chance to work on reproducibility and provenance at an Arvados hackathon on Tuesday March 11th.
We reused the existing bcbio-nextgen installation scripts to create easily distributed Docker images with pipeline code and external tools. In fact, the bcbio-nextgen Dockerfile replicates current best practice recommendations for setting up the pipeline on a local system. CloudBioLinux drives installation of the software, using packaging work from existing communities such as Bio-Linux, DebianMed and homebrew-science. The advantage over the previous installation approach is that this Docker installation takes place in a defined environment and we distribute the pre-built images, avoiding the need to configure and build software on individual systems.
The pre-built Docker image contains a full manifest of installed software, from the system libraries to custom scientific packages. Coupled with the ability to export and save Docker images, this creates a reproducible run environment. Special thanks for the manifest implementation are due to the DebianMed community and Tony Travis. I had time to finish the manifest implementation while at the DebianMed Hackathon in Aberdeen. This critical component helped enable external version queries for Docker isolated software.
Tying all these parts together, the bcbio-nextgen-vm wrapper drives processing of individual run components using isolated Docker containers. The Python wrapper script uses the existing work in bcbio-nextgen for defining workflows, and it runs on distributed cluster systems using the IPython parallel framework. Using Conda and Binstar to handle installation of Python dependencies results in a streamlined installation procedure for all the wrapper software.
The diagram below shows the parts of bcbio-nextgen handled within each of the components of the system. bcbio-nextgen-vm drives the workflow and parallel runs, interacting with a cluster scheduler, and lives outside of Docker on a central server. The wrapper code manages the work of starting Docker containers and mounting external filesystems to local mounts within the Docker container. On each processing node, execution happens within isolated Docker containers with external biological software and bcbio-nextgen processing-specific code.
The initial v0.1.0 release of bcbio-nextgen-vm contains full support for all bcbio-nextgen functionality using isolated Docker containers. It runs on clusters using IPython parallel and on single machines using multiple cores, and has minimal external requirements beyond Docker. See the full installation instructions, and bcbio-nextgen-vm run instructions to get started with processing your samples. It uses the same infrastructure and input files as bcbio-nextgen, so the bcbio-nextgen documentation contains much more detail on defining the biological pipelines to run.
With the new isolated framework, you can install bcbio-nextgen on a system with only Docker installed. Conda handles installation of the Python dependencies, ideally inside of an isolated minimal Anaconda Python environment, and is the only non-Docker-contained infrastructure required. The install script will also download and prepare biological data required for processing, including genomes, index files and annotations.
We’re hoping to migrate bcbio-nextgen to this Docker enabled framework over time and welcome feedback on installation or usage challenges that still exist. Reporting problems on the GitHub issue tracker would be a major help as we continue to develop and improve the wrapper framework.
One area of particular interest is installation and security on cluster systems. While patiently waiting for the ability to run Docker as a non-root user, we recommend installing bcbio-nextgen-vm to run with the docker group id on execution. The internal scripts within the bcbio-nextgen Docker container run all commands as the calling user to mitigate security issues.
Provenance and further work
Adding Docker isolated containers provides the pipeline with improved reproducibility. Maintaining the full state of all the tools and software only requires exporting and gzipping the Docker image and storing it alongside the final processed result. The 1Gb stored image can be later reconstituted and rerun to reproduce earlier results, or shared with collaborators to ensure identical processing pipelines across multiple locations. Saving the initial input data plus the Docker image provides the ability to re-run an analysis at any point in the future.
With this framework in place, the next step for improving reproducibility is enabling full provenance to trace processing steps. bcbio-nextgen currently has extensive log files of command lines and program output, but in parallel environments it requires work to deconvolute these to establish the full set of steps leading up to production of files of interest.
Arvados is an promising open source framework designed to help handle provenance and run tracking. Curoverse provides commercial support and development for the Arvados platform and recently closed a round of financing as they continue to expand and develop the framework.
If you’re interested in reproducibility and provenance, and live in the Boston area, Curoverse is hosting an Arvados hackathon next Tuesday evening, March 11th at their offices. I’ll be there learning about ways to integrate bcbio-nextgen with the work they’re doing and would be happy to talk with anyone about the Docker work or reproducible pipelines in general.
Updated comparison of variant detection methods: Ensemble, FreeBayes and minimal BAM preparation pipelines
Variant evaluation overview
I previously discussed our approach for evaluating variant detection methods using a highly confident set of reference calls provided by NIST’s Genome in a Bottle consortium for the NA12878 human HapMap genome, In this post, I’ll update those conclusions based on recent improvements in GATK and FreeBayes.
The comparisons use bcbio-nextgen, an automated open-source pipeline for variant calling and evaluation that identifies concordant and discordant variants with the XPrize validation protocol. By having an automated validation workflow attached to a regularly updated, community developed, variant calling pipeline, we can actively track progress of variant callers and provide updates as algorithms improve.
Since the initial post, There have been two new GATK releases of UnifiedGenotyper and HaplotypeCaller, as well as multiple improvements to FreeBayes. Additionally we’ve enchanced our ensemble calling method, which combines inputs from multiple callers into a single final set of calls, to better handle comparisons with inputs from three callers.
The goal of this post is to re-evaluate these variant detection approaches and provide an updated set of recommendations:
- FreeBayes detects more concordant SNPs and indels compared to GATK approaches, including GATK’s HaplotypeCaller method.
- Post-alignment BAM processing steps like base quality recalibration and realignment have little impact on the quality of variant calls with variant callers that perform local realignment, including FreeBayes and GATK HaplotypeCaller.
- The Ensemble calling method provides the best variant detection by combining inputs from GATK UnifiedGenotyper, HaplotypeCaller and FreeBayes.
Avoiding the post-alignment BAM recalibration and realignment steps allows us to save significant time and pipeline complexity. Combined with the improvements in FreeBayes, this enables a variant calling pipeline that can be freely used for academic, clinical and commercial work with equal quality variant calls compared to current GATK best-practice approaches.
Calling and evaluation methods
We called variants on a NA12878 exome dataset from EdgeBio’s clinical pipeline and assessed them against the NIST’s Genome in a Bottle reference material. Full instructions for replicating the analysis and installing the pipeline are available from the bcbio-nextgen documentation site. Following alignment with bwa-mem (0.7.5a), we post-processed the BAM files with two methods:
- GATK’s best practices (2.7-2): This involves de-duplication with Picard MarkDuplicates, GATK base quality score recalibration and GATK realignment around indels.
- Minimal post-processing, with de-duplication using samtools rmdup and no realignment or recalibration.
We then called variants with three general purpose callers:
- FreeBayes (v0.9.9.2-18): A haplotype-based Bayesian caller from the Marth Lab. We filter calls with a hard filter based on depth, quality and strand bias.
- GATK UnifiedGenotyper (2.7-2): GATK’s widely used Bayesian caller. Since this is single sample exome data, we filter calls using GATK’s recommended hard filters, instead of Variant Quality Score Recalibration (VQSR).
- GATK HaplotypeCaller (2.7-2): GATK’s more recently developed haplotype caller which provides local assembly around variant regions. We also filtered these calls using recommended hard filters.
Finally, we evaluated the calls from each combination of variant caller and BAM post-alignment preparation method using the bcbio.variation framework. This provides a summary identifying concordant and discordant variants, separating SNPs and indels since they have different error profiles. Additionally it classifies discordant variants. where the reference material and evaluation variants differ, into three categories:
- Extra variants, called in the evaluation data but not in the reference. These are potential false positives or missing calls from the reference materials.
- Missing variants, found in the NA12878 reference but not in the evaluation data set. These are potential false negatives.
- Shared variants, called in both the evaluation and reference but differently represented. This results from allele differences, such as heterozygote versus homozygote calls, or variant identification differences, such as indel start and end coordinates.
Variant caller comparison
Using this framework, we compared the 3 variant callers and combined ensemble method:
- FreeBayes outperforms the GATK callers on both SNP and indel calling. The most recent versions of FreeBayes have improved sensitivity and specificity which puts them on par with GATK HaplotypeCaller. One area where FreeBayes performs better is in correctly resolving heterozygote/homozygote calls, reflected in the lower number of discordant shared variants.
- GATK HaplotypeCaller is all around better than the UnifiedGenotyper. In the previous comparison, we found UnifiedGenotyper performed better on SNPs and HaplotypeCaller better on indels, but the recent improvements in GATK 2.7 have resolved the difference in SNP calling. If using a GATK pipeline, UnifiedGenotyper lags behind the realigning callers in resolving indels, and I’d recommend using HaplotypeCaller. This mirrors the GATK team’s current recommendations.
- The ensemble calling approach provides the best overall resolution of both SNPs and indels. The one area where it lags slightly behind is in identification of homozygote/heterozygote calls, especially in indels. This is due to positions where HaplotypeCaller and FreeBayes both call variants but differ on whether it is a heterozygote or homozygote, reflected as higher discordant shared counts.
In addition to calling sensitivity and specificity, an additional factor to consider is the required processing time. Rough benchmarks on family-based calling of whole genome sequencing data indicate that HaplotypeCaller is roughly 7x slower than UnifiedGenotyper and FreeBayes is 2x slower. On multiple 30x whole genome samples, our experience is that calling can range from 10 hours for GATK UnifiedGenotyper to 70 hours for HaplotypeCallers. Ensemble calling requires running all three callers plus combining into a final call set, and for family-based whole genome samples can add another 100 hours of processing time. These estimates fluctuate greatly depending on the compute infrastructure and presence of longer difficult genomic regions with deeper coverage, but give some estimates of timing considerations.
Post-alignment BAM preparation comparison
Given the improved accuracy of local realignment haplotype-based callers like FreeBayes and HaplotypeCaller, we explored the accuracy cost of removing the post-alignment BAM processing steps. The recommended GATK best-practice is to follow up alignment with identification of duplicate reads, followed by base quality score recalibration and realignment around indels. Based on whole genome benchmarking work, these steps can take as long as the initial alignment and scale poorly due to the high IO costs of manipulating large BAM files. For multiple 30x whole genome samples running on 16 cores per sample, this can account for 12 to 16 hours of processing time.
To compare the quality impact of avoiding recalibration and realignment, we performed the identical alignment and variant calling steps as above, but did minimal post-alignment BAM preparation. Following alignment, the only step performed was deduplication using samtools rmdup. Unlike Picard MarkDuplicates, samtools rmdup handles piped streaming input to avoid IO penalties. This is at the cost of not handling some edge cases. Longer term, we’d like to explore biobambam’s markduplicates2, which implements a more efficient streaming version of the Picard MarkDuplicates algorithm.
Suprisingly, skipping base recalibration and indel realignment had almost no impact on the quality of resulting variant calls:
While GATK UnifiedGenotyper suffers during indel calling without recalibration and realignment, both HaplotypeCaller and FreeBayes perform as good or better without these steps. This allows us to save on processing time and complexity without sacrificing call quality when using a haplotype aware realigning caller.
Caveats and conclusions
Taken together, the improvements in FreeBayes and ability to avoid post-alignment BAM processing allow use of a commercially unrestricted GATK-free pipeline with equal quality to current GATK best practices. Adding in GATK’s two callers plus our ensemble combining method provides the most accurate overall calls, at the cost of additional processing time.
It’s also important to consider potential drawbacks of this analysis as we continue to design future evaluations. The comparison is in exome regions for single sample variant calling. In future work it would be helpful to have population or family based inputs. We’d also like to prepare test datasets that focus specifically on evaluating the quality of calls in more difficult repetitive regions within the whole genome. Using populations or whole genomes would also allow use of GATK’s Variant Quality Score Recalibration as part of the pipeline, which could provide improved filtering compared to the hard-filtering approach used here.
Another consideration is that the reference callset prepared by the Genome in a Bottle consortium makes extensive use of GATK tools during preparation. Evaluation of the reference materials with FreeBayes and other callers can help reduce potential GATK-specific biases when continuing to develop reliable reference materials.
All of these pipelines are freely available, open-source, community developed projects and we welcome feedback and contributors. By integrating validation into a scalable analysis pipeline, we hope to build a community interested in widely accessible calling pipelines coupled with well-evaluated reference datasets and methods.
Summary from Bioinformatics Open Science Codefest 2013: Tools, infrastructure, standards and visualization
The 2013 Bioinformatics Open Source Conference (BOSC) starts tomorrow in Berlin, Germany. It’s a yearly conference devoted to community-based software development projects supporting biological research. Members of the Open Bioinformatics Foundation discuss implementations and approaches to better provide interoperable and reusable software, libraries and pipelines.
For the past five years, a two day Codefest and hackathon preceded the conference. This gives programmers time to work face-to-face, sharing approaches and discovering connections between projects. This year, the the Department of Biology, Humboldt-Universität zu Berlin kindly hosted Codefest 2013. Thanks to the organizers and attendees, we finished projects ranging from tool development, infrastructure integration, standards development and visualization. There are photos of the Codefest in progress and a detailed writeup of projects.
Below we summarize the accomplishments from the two days. We welcome feedback on the topics covered and hope that by sharing our work we can encourage more programmers to become part of the open science bioinformatics community. Actively working to build well-tested, community-developed, interoperable tools is how we solve increasingly difficult research questions ranging from human health to plant breeding to microbial community function. The progress made in two days illuminates the effectiveness of open collaborative science.
BioRuby and BaseSpace – Develop SDK and apps for Illumina BaseSpace
Toshiaki Katayama, Raoul Bonnal, Eri Kibukawa, Joachim Baran, Dan MacLean, Fernando Izquierdo-Carrasco, Spencer Bliven
During the Codefest, we tested and documented our port of the BaseSpace Python SDK to Ruby. Ruby/Biogem developers can now easily utilize next-generation sequencing code within the Illumina’s BaseSpace framework. For non-Ruby programers, we found that it can be a burden to create new Web app from scratch on top of your NGS program. So we started new project to provide a Web-app scaffold for BaseSpace. We have already implemented the basic portion but will need some more time before releasing the BioBaseSpace application. The BaseSpace Ruby SDK was officially released: for more information, see Joachim’s blog post, the official announcement from the BioRuby team and the annoucement from Illumina.
Barrnap – Bacterial ribosomal RNA predictor
Torsten Seemann, Tim Booth
For the last 8 years RNAmmer has been the standard tool for predicting ribosomal RNA features in genomes, because it is reasonably fast, accurate, and works on bacteria and eukaryotes. Its drawbacks are that it relies on small, older databases; requires an older conflicting version of HMMER; and has restrictive licence terms. To resolve these issues we have implemented a new rRNA predictor which uses the new “nmmer” tool from HMMER 3.1 for searching DNA profiles against DNA sequence. We used the Silva and GreenGenes seed alignments for the 5S, 23S and 16S genes to build the profile models from. Barrnap is a small Perl script which takes FASTA as input, and outputs the rRNA feature predictions in GFF3 format. It will be packaged in Bio-Linux and replace RNAmmer in the Prokka bacterial annotation system.
BioJVM – Coordinating and integrating BioJava and ScaBio
Spencer Bliven, Andreas Prlic, Markus Gumbel
Both Java and Scala run on the Java Virtual Machine. As such, it makes sense to coordinate and document the various Bio* projects which run on the JVM and therefor can interoperate to some degree. We are able to successfully reference BioJava functions from Scala code and ScaBio functions from Java code. The ease of this process means that users can easily use both libraries from whichever language is more suited for their biological problem.
Peter Cock, Konstantin Tretyakov, Bin Zhang
The Biopython team worked on training new users at Codefest and exploring integration of Biopython with other Python molecular visualization toolkits like PyMol. Infrastructure development involved testing and debugging on multiple systems, including identifying and fixing Windows and PyPy problems. We also identified areas where we can make it easier to contribute to Biopython: specifically easing the process to report and fix bugs by moving to integrated GitHub issue tracking and working to support Biopython-associated projects with easy installation tools.
I spent several hours revisiting previous work on the Galaxy package for Bio-Linux and made significant progress towards it being something that can go into Debian-proper. Results will be committed to Deb-Med public SVN and patches will be forwarded to the Galaxy dev mailing list.
Standards and Visualization
Ontology and provenance representation
Herve Menager, Bertrand Neron, Jackie Quinn, Stian Soiland-Reyes, Matus Kalas, Steffen Moller
The goal of this group was to investigate and implement solutions to use ontologies to help people find and use the programs and data they need for their work, and to help automate the integration of tools or data resources into workflows or workbenches. We also wanted to identify useful provenance metadata, to store in a rigorous way the conditions and configuration of analysis steps run by users. This improves transparency, reproducibility, and reliability of the scientific results.
We worked toward inclusion of the EDAM onotology as part of the Mobyle system’s built-in type and classification mechanisms. We created a user case by identify workflows in Mobyle and mapped the descriptions unto EDAM classification to allow mapping between the types. We also investigated the possibilities opened by projects such as PROV to standardize the provenance information stored by systems such as Mobyle. We added a prototype functionality to the development version of Mobyle that dynamically generates this provenance information in a JSON-based format.
Integrate DGE-Vis & Dalliance, JS animation scheduler
David Powell, Thomas Down, Skyler Brungardt, Alex Kalderimis
Infrastructure management via CloudBioLinux (CBL)
Enis Afgan, John Chilton, Brad Chapman
- Galaxy: We integrated custom installation procedures present in CBL with the Galaxy-tools versioned installation methodology.
- Documentation: Due to the increased interest by individuals to use and contribute to CBL, we invested effort into creating purpose-driven documentation for CBL. This should help people use the endproduct of CBL, customize CBL their needs, as well as learn about the internals of CBL with the aim of contributing. We will finish and make the documentation available on ReadTheDocs over the coming months.
- Build frameworks: We developed a simpler automated method to invoke the CBL build framework to help remove complex error prone steps.
- Web tooling: In spirit of making CBL more accessible and easier to use, we’ve decided to tackle development of a lightweight webapp that helps with customizing and generating CBL configuration files.
Improve ipython cluster support and runtime metrics
Valentine Svensson, Guillermo Carrasco, Roman Valls, Per Unneberg
We worked to extend the Ipython parallel cluster framework to support additional schedulers, specifically implementing SLURM support to supplement existing SGE, LSF, Torque and Condor schedulers. We plan to extend this to allow generalized use of the DRMAA connector, ultimately port such generalization into ipython so that python scientific computations can be executed efficiently across different clusters implementation. Both Roman and Guillermo blogged detailed documentation of the work in progress.
We also worked to build a tool that helps provide run time estimations for bioinformatcs jobs (e.g. “how long should aligning 40 million reads against hg19 with BWA take if I use 8 cores?”). We plan to collaborate on longer term development of this with the Genome Comparison of Analytic Testing team.
GATK-based reusable pipeline based around Rubra/Ruffus
Clare Sloggett, Bernie Pope
We worked on code cleanup, documentation and test data for a reusable pipeline to handle variant calling and annotation, using Rubra built on the Ruffus framework. It handles BWA alignment, GATK alignment cleaning and variant calling and ENSEMBL annotation. To make these pipelines easier to run, we worked on integrating them into the GVF flavor in CloudBioLinux.
Scaling for whole genome sequencing
Moving from exome to whole genome sequencing introduces a myriad of scaling and informatics challenges. In addition to the biological component of correctly identifying biological variation, it’s equally important to be able to handle the informatics complexities that come with scaling up to whole genomes.
At Harvard School of Public Health, we are processing an increasing number of whole genome samples and the goal of this post is to share experiences scaling the bcbio-nextgen pipeline to handle the associated increase in file sizes and computational requirements. We’ll provide an overview of the pipeline architecture in bcbio-nextgen and detail the four areas we found most useful to overcome processing bottlenecks:
- Support heterogeneous cluster creation to maximize resource usage.
- Increase parallelism by developing flexible methods to split and process by genomic regions.
- Avoid file IO and prefer streaming piped processing pipelines.
- Explore distributed file systems to better handle file IO.
This overview isn’t meant as a prescription, but rather as a description of experiences so far. The work is a collaboration between the HSPH Bioinformatics Core, the research computing team at Harvard FAS and Dell Research. We welcome suggestions and thoughts from others working on these problems.
The bcbio-nextgen pipeline runs in parallel on single multicore machines or distributed on job scheduler managed clusters like LSF, SGE, and TORQUE. The IPython parallel framework manages the set up of parallel engines and handling communication between them. These abstractions allow the same pipeline to scale from a single processor to hundreds of node on a cluster.
The high level diagram of the analysis pipeline shows the major steps in the process. For whole genome samples we start with large 100Gb+ files of reads in FASTQ or BAM format and perform alignment, post-alignment processing, variant calling and variant post processing. These steps involve numerous externally developed software tools with different processing and memory requirements.
A major change in the pipeline was supporting creation of heterogeneous processing environments targeted for specific programs. This moves away from our previous architecture, which attempted to flatten processing and utilize single cores throughout. Due to algorithm restrictions, some software requires the entire set of reads for analysis. For instance, GATK’s base quality recalibrator uses the entire set of aligned reads to accurately calculate inputs for read recalibration. Other software operates more efficiently on entire files: the alignment step scales better by running using multiple cores on a single machine, since the IO penalty for splitting the input file is so severe.
To support this, bcbio-nextgen creates an appropriate type of cluster environment for each step:
- Multicore: Allocates groups of same machine processors, allowing analysis of individual samples with multiple cores. For example, this enables running bwa alignment with 16 cores on multiprocessor machines.
- Full usage of single cores: Maximize usage of single cores for processes that scale beyond the number of samples. For example, we run variant calling in parallel across subsets of the genome.
- Per sample single core usage: Some steps do not currently parallelize beyond the number of samples, so require a single core per sample.
IPython parallel provides the distributed framework for creating these processing setups, working on top of existing schedulers like LSF, SGE and TORQUE. It creates processing engines on distributed cores within the cluster, using ZeroMQ to communicate job information between machines.
Cluster schedulers allow this type of control over core usage, but an additional future step is to include memory and disk IO requirements as part of heterogeneous environment creation. Amazon Web Services allows selection of exact memory, disk and compute resources to match the computational step. Eucalyptus and OpenStack bring this control to local hardware and virtual machines.
Parallelism by genomic regions
While the initial alignment and preparation steps require analysis of a full set of reads due to IO and algorithm restrictions, subsequent steps can run with increased parallelism by splitting across genomic regions. Variant detection algorithms do require processing continuous blocks of reads together, allowing local realignment algorithms to correctly characterize closely spaced SNPs and indels. Previously, we’d split analyses by chromosome but this has the downside of tying analysis times to chromosome 1, the largest chromosome.
The pipeline now identifies chromosome blocks without callable reads. These blocks group by either genomic features like repetitive hard to align sequence or analysis requirements like defined target regions. Using the globally shared callable regions across samples, we fraction the genome into more uniform sections for processing. As a result we can work on smaller chunks of reads during time critical parts of the process: applying base recalibration, de-duplication, realignment and variant calling.
A key bottleneck throughout the pipeline is disk usage. Steps requiring reading and writing large BAM or FASTQ files slow down dramatically once they overburden disk IO, distributed filesystem capabilities or ethernet connectivity between storage nodes. A practical solution to this problem is to avoid intermediate files and use unix pipes to stream results between processes.
We reworked our alignment step specifically to eliminate these issues. The previous attempt took a disk centric approach that allowed scaling out to multiple single cores in a cluster. We split an input FASTQ or BAM file into individual chunks of reads, and then aligned each of these chunks independently. Finally, we merged all the individual BAMs together to produce a final BAM file to pass on to the next step in the process. While nicely generalized, it did not scale when running multiple concurrent whole genomes.
The updated pipeline uses multicore support in samtools and aligners like bwa-mem and novoalign to pipe all steps as a stream: preparation of input reads, alignment, conversion to BAM and coordinate sorting of aligned reads. This results in improved scaling at the cost of only being able to increase single sample throughput to the maximum processors on a machine.
More generally, the entire process creates numerous temporary file intermediates that are a cause of scaling issues. Commonly used best-practice toolkits like Picard and GATK primarily require intermediate files. In contrast, tools in the Marth lab’s gkno pipeline handle streaming input and output making it possible to create alignment post-processing pipelines which minimize temporary file creation. As a general rule, supporting streaming algorithms amenable to piping can ameliorate file load issues associated with scaling up variant calling pipelines. This echos the focus on streaming algorithms Titus Brown advocates for dealing with large metagenomic datasets.
Distributed file systems
While all three of CPU, memory and disk speed limit individual steps during processing, the hardest variable to tweak is disk throughput. CPU and memory limitations have understandable solutions: buy faster CPUs and more memory. Improving disk access is not as easily solved, even with monetary resources, as it’s not clear what combination of disk and distributed filesystem will produce the best results for this type of pipeline.
We’ve experimented with NFS, GlusterFS and Lustre for handling disk access associated with high throughput variant calling. Each requires extensive tweaking and none has been unanimously better for all parts of the process. Much credit is due to John Morrissey and the research computing team at Harvard FAS for help performing incredible GlusterFS and network improvements as we worked through scaling issues, and Glen Otero, Will Cottay and Neil Klosterman at Dell for configuring an environment for NFS and Lustre testing. We can summarize what we’ve learned so far in two points:
- A key variable is the network connectivity between storage nodes. We’ve worked with the pipeline on networks ranging from 1 GigE to InfiniBand connectivity, and increased throughput delays scaling slowdowns.
- Different part of the processes stress different distributed file systems in complex ways. NFS provides the best speed compared to single machine processing until you hit scaling issues, then it slows down dramatically. Lustre and GlusterFS result in a reasonable performance hit for less disk intensive processing, but delay the dramatic slowdowns seen with NFS. However, when these systems reach their limits they hit a slowdown wall as bad or worse than NFS. One especially slow process identified on Gluster is SQLite indexing, although we need to do more investigation to identify specific underlying causes of the slowdown.
Other approaches we’re considering include utilizing high speed local temporary disk, reducing writes to long term distributed storage file systems. This introduces another set of challenges: avoiding stressing or filling up local disk when running multiple processes. We’ve also had good reports about using MooseFS but haven’t yet explored setting up and configuring another distributed file system. I’d love to hear experiences and suggestions from anyone with good or bad experiences using distributed file systems for this type of disk intensive high throughput sequencing analysis.
A final challenge associated with improving disk throughput is designing a pipeline that is not overly engineered to a specific system. We’d like to be able to take advantage of systems with large SSD attached temporary disk or wonderfully configured distributed file systems, while maintaining the ability scale on other systems. This is critical for building a community framework that multiple groups can use and contribute to.
Providing detailed timing estimates for large, heterogeneous pipelines is difficult since they will be highly depending on the architecture and input files. Here we’ll present some concrete numbers that provide more insight into the conclusions presented above. These are more useful as a side by side comparison between approaches, rather than hard numbers to predict scaling on your own systems.
In partnership with Dell Solutions Center, we’ve been performing benchmarking of the pipeline on dedicated cluster hardware. The Dell system has 32 16-core machines connected with high speed InfiniBand to distributed NFS and Lustre file systems. We’re incredibly appreciative of Dell’s generosity in configuring, benchmarking and scaling out this system.
As a benchmark, we use 10x coverage whole genome human sequencing data from the Illumina platinum genomes project. Detailed instructions on setting up and running the analysis are available as part of the bcbio-nextgen example pipeline documentation.
Below are wall-clock timing results, in total hours, for scaling from 1 to 30 samples on both Lustre and NFS fileystems:
|primary||1 sample||1 sample||1 sample||30 samples||30 samples|
|bottle||16 cores||96 cores||96 cores||480 cores||480 cores|
Some interesting conclusions:
- Scaling single samples to additional cores (16 to 96) provides a 40% reduction in processing time due to increased parallelism during post-processing and variant calling.
- Lustre provides the best scale out from 1 to 30 samples, with 30 sample concurrent processing taking only 1.5x as along as a single sample.
- NFS provides slightly better performance than Lustre for single sample scaling.
- In contrast, NFS runs into scaling issues at 30 samples, proceeding 5.5 times slower during the IO intensive alignment post-processing step.
This is preliminary work as we continue to optimize code parallelism and work on cluster and distributed file system setup. We welcome feedback and thoughts to improve pipeline throughput and scaling recommendations.