Blue Collar Bioinformatics

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Posts Tagged ‘validation

Validating multiple cancer variant callers and prioritization in tumor-only samples

with 5 comments

Overview

The post discusses work validating multiple cancer variant callers in bcbio-nextgen using a synthetic reference call set from the ICGC-TCGA DREAM challenge. We’ve previously validated germline variant calling methods, but cancer calling is additionally challenging. Tumor samples have mixed cellularity due to contaminating normal sample, and consist of multiple sub-clones with different somatic variations. Low-frequency sub-clonal variations can be critical to understand disease progression but are more difficult to detect with high sensitivity and precision.

Publicly available whole genome truth sets like the NA12878 Genome in a Bottle reference materials don’t currently exist for cancer calling, but other groups have been working to prepare standards for use in evaluating callers. The DREAM challenge provides a set of synthetic datasets that include cellularity and multiple sub-clones. There is also on ongoing DREAM contest with real, non-simulated data. In addition, the ICGC has done extensive work on assessing the challenges in cancer variant calling and produced a detailed comparison of multiple variant calling pipelines. Finally, Bina recently released a simulation framework called varsim that they used to compare multiple callers as part of their cancer benchmarking. I’m excited about all this community benchmarking and hope this work can contribute to the goal of having fully consented real patient reference materials, leading to a good understanding of variant caller tradeoffs.

In this work, we evaluated cancer tumor/normal variant calling with synthetic dataset 3 from the DREAM challenge, using multiple approaches to detect SNPs, indels and structural variants. A majority voting ensemble approach combines inputs from multiple callers into a final callset with good sensitivity and precision. We also provide a prioritization method to enrich for somatic mutations in tumor samples without matched normals.

Cancer variant calling in bcbio is due to contributions and support from multiple members of the community:

  • Miika Ahdesmaki and Justin Johnson at AstraZeneca collaborated with our group on integrating and evaluating multiple variant callers. Their financial support helped fund our time on this comparison, and their scientific contributions improved SNP and indel calling.
  • Luca Beltrame integrated a number of cancer variant callers into bcbio and wrote the initial framework for somatic calling.
  • Lorena Pantano integrated variant callers and performed benchmarking work. Members of our group, the Harvard Chan school Bioinformatics core, regularly contribute to bcbio development and validation work.
  • The Wolfson Wohl Cancer Research Centre supported our work on validation of somatic callers.
  • James Cuff, Paul Edmon and the team at Harvard FAS research computing provided compute infrastructure and support that enabled the large number of benchmarking runs it takes to get good quality calling.

I’m continually grateful to the community for all the contributions and support. The MuTect commit history for bcbio is a great example of multiple distributed collaborators working towards the shared goal of integrating and validating a caller.

Variant caller validation

We used a large collection of open source variant callers to detect SNPs, Indels and structural variants:

bcbio runs these callers and uses simple ensemble methods to combine small variants (SNPs, indels) and structural variants into final combined callsets. The new small variant ensemble method uses a simplified majority rule classifier that picks variants to pass based on being present in a configurable number of samples. This performs well and is faster than the previous implementation that made use of both this approach and a subsequent support vector machine step.

Using the 100x whole genome tumor/normal pair from DREAM synthetic dataset 3 we evaluated each of the callers for sensitivity and precision on small variants (SNPs and indels). This synthetic dataset contains 100% tumor cellularity with 3 subclones at allele frequencies of 50%, 33% and 20%.


Cancer evaluation for SNPs and indels

In addition to the whole genome results, the validation album includes results from running against the same dataset limited to exome regions. This has identical patterns of sensitivity and precision. It runs quicker, so is useful for evaluating changes to filtering or program parameters.

We also looked at structural variant calls for larger deletions, duplications and inversions. Here is the precision and sensitivity for duplications across multiple size classes:


Cancer evaluation for structural variants -- duplications.

The full album of validation results includes the comparisons for deletion and inversion events. These comparisons measure the contributions of individual callers within an ensemble approach that attempts to maximize sensitivity and specificity for the combined callset. Keep in mind that each of the individual results make use of other caller information in filtering. Our goal is to create the best possible combined calls, rather than a platform for unbiased comparison of structural variant callers. We’re also actively working on improving sensitivity and specificity for individual callers and expect the numbers to continue to evolve. For example, Zev Kronenberg added WHAM’s ability to identify different classes of structural changes, and we’re still in the process of improving the classifier.

Improvements in filtering

Our evaluation comparisons show best effort attempts to provide good quality calls for every caller. The final results often come from multiple rounds of improving sensitivity and precision by adjusting program parameters or downstream filtering. The goal of tightly integrating bcbio with validation is that the community can work on defining a set of parameters and filters that work best in multiple cases, and then use these directly within the same framework for processing real data.

In presenting the final results only, it may not be clear that plugging a specific tool into a custom bash script will not always produce the same results we see here. As an example, here are the improvements in FreeBayes sensitivity and precision from our initial implementation, presented over the exome regions of synthetic dataset 3:


FreeBayes caller improvements

The original implementation used a vcfsamplediff based approach to filtering, as recommended on the FreeBayes mailing list. The current, improved, version uses a custom filter based on genotype likelihoods, based on the approach in the speeseq pipeline.

In general, you can find all of the integration work for individual callers in the bcbio source code, broken down by caller. For instance, here is the integration work on MuTect. The goal is to make all of the configuration settings and filters fully transparent so users can understand how they work when using bcbio, or transplant them into their own custom pipelines.

Tumor-only prioritization

The above validations were all done on cancer calling with tumor and normal pairs. The filters to separate pre-existing germline mutations from cancer specific somatic mutations rely on the presence of a normal sample. In some cases, we don’t have matched normal samples to do this filtering. Two common examples are FFPE samples and tumor cell lines. For these samples, we’d like to be able to prioritize likely tumor specific variations for followup using publicly available resources.

We implemented a prioritization strategy from tumor-only samples in bcbio that takes advantage of publicly available resources like COSMIC, ClinVar, 1000 genomes, ESP and ExAC. It uses GEMINI to annotate the initial tumor-only VCF calls with external annotations, then extracts these to prioritize variants with high or medium predicted impact, not present in 1000 genomes or ExAC at more than 1% in any subpopulation, or identified as pathenogenic in COSMIC or ClinVar.

Validating this prioritization strategy requires real tumor samples with known mutations. Our synthetic datasets are not useful here, since the variants do not necessarily model standard biological variability. You could spike in biologically relevant mutations, as done in the VarSim cancer simulated data, but this will bias towards our prioritization approach since both would use the same set of necessarily imperfect known variants and population level mutations.

We took the approach of using published tumor data with validated mutations. Andreas Sjödin identified a Hepatoblastoma exome sequencing paper with publicly available sample data and 23 validated cancer related variations across 5 samples. This is a baseline to help determine how stringent to be in removing potential germline variants.

The prioritization enriches variants of interest by 35-50x without losing sensitivity to confirmed variants:

sample caller confirmed enrichment additional filtered
HB2T freebayes 6 / 7 44x 1288 56046
HB2T mutect 6 / 7 48x 1014 47755
HB2T vardict 6 / 7 36x 1464 52090
HB3T freebayes 4 / 4 46x 1218 54997
HB3T mutect 4 / 4 49x 961 46894
HB3T vardict 4 / 4 35x 1511 51404
HB6T freebayes 4 / 4 43x 1314 56240
HB6T mutect 4 / 4 51x 946 47747
HB6T vardict 3 / 4 35x 1497 51625
HB8T freebayes 6 / 6 42x 1364 57121
HB8T mutect 6 / 6 47x 1053 48639
HB8T vardict 6 / 6 35x 1542 52642
HB9T freebayes 2 / 2 41x 1420 57582
HB9T mutect 2 / 2 44x 1142 49858
HB9T vardict 2 / 2 36x 1488 53098

We consistently missed one confirmed mutation in the HB2T sample. This variant, reported as a somatic mutation in an uncharacterized open reading frame (C2orf57), may actually be a germline mutation in the study sub-population. The variant is present at a 10% frequency in the East Asian population but only 2% in the overall population, based on data from both the ExAC and 1000 genomes projects. Although the ethnicity of the original samples is not reported, the study authors are all from China. This helps demonstrate the effectiveness of large population frequencies, stratified by population, in prioritizing and evaluating variant calls.

The major challenge with tumor-only prioritization approaches is that you can’t expect to accurately filter private germline mutations that you won’t find in genome-wide catalogs. With a sample set using a small number of validated variants it’s hard to estimate the proportion of ‘additional’ variants in the table above that are germline false positives versus the proportion that are additional tumor-only mutations not specifically evaluated in the study. We plan to continue to refine filtering with additional validated datasets to help improve this discrimination.

Practically, bcbio automatically runs prioritization with all tumor-only analyses. It filters variants by adding a “LowPriority“ filter to the output VCF so users can readily identify variants flagged during the prioritization.

Future work

This is a baseline for assessing SNP, indel and structural variant calls in cancer analyses. It also prioritizes impact variants in cases where we lack normal matched normals. We plan to continue to improve cancer variant calling in bcbio and some areas of future focus include:

  • Informing variant calling using estimates of tumor purity and sub-clonal frequency. bcbio integrates CNVkit, a copy number caller, which exports read count segmentation data. Tools like THetA2, phyloWGS, PyLOH and sciClone use these inputs to estimate normal contamination and sub-clonal frequencies.
  • Focusing on difficult to call genomic regions and provide additional mechanisms to better resolve and improve caller accuracy in these regions. Using small variant calls to define problematic genome areas with large structural rearrangements can help prioritize and target the use of more computationally expensive methods for copy number assessment, structural variant calling and de-novo assembly.
  • Evaluating calling and tumor-only prioritization on Horizon reference standards. They contain a larger set of validated mutations at a variety of frequencies.

As always, we welcome suggestions, contributions and feedback.

Written by Brad Chapman

March 5, 2015 at 6:13 am

Benchmarking variation and RNA-seq analyses on Amazon Web Services with Docker

with 12 comments

Overview

We developed a freely available, easy to run implementation of bcbio-nextgen on Amazon Web Services (AWS) using Docker. bcbio is a community developed tool providing validated and scalable variant calling and RNA-seq analysis. The AWS implementation automates all of the steps of building a cluster, attaching high performance shared filesystems, and running an analysis. This makes bcbio readily available to the research community without the need to install and configure a local installation.

The entire installation bootstraps from standard Linux AMIs, enabling adjustment of the tools, genome data and installation without needing to prepare custom AMIs. The implementation uses Elasticluster to provision and configure the cluster. We automate the process with the boto Python interface to AWS and Ansible scripts. bcbio-vm isolates code and tools inside a Docker container allowing runs on any remote machine with a download of the Docker image and access to the shared filesystem. Analyses run directly from S3 buckets, with automatic streaming download of input data and upload of final processed data.

We provide timing benchmarks for running a full variant calling analysis using bcbio on AWS. The benchmark dataset was a cancer tumor/normal evaluation, from the ICGC-TCGA DREAM challenge, with 100x coverage in exome regions. We compared the results of running this dataset on 2 different networked filesystems: Lustre and NFS. We also show benchmarks for an RNA-seq dataset using inputs from the Sequencing Quality Control (SEQC) project.

We developed bcbio on AWS and ran these timing benchmarks thanks to work with great partners. A collaboration with Biogen and Rudy Tanzi’s group at MGH funded the development of bcbio on AWS. A second collaboration with Intel Health and Life Sciences and AstraZenenca funded the somatic variant calling benchmarking work. We’re thankful for all the relationships that make this work possible:

  • John Morrissey automated the process of starting a bcbio cluster on AWS and attaching a Lustre filesystem. He also automated the approach to generating graphs of resource usage from collectl stats and provided critical front line testing and improvements to all the components of the bcbio AWS interaction.
  • Kristina Kermanshahche and Robert Read at Intel provided great support helping us get the Lustre ICEL CloudFormation templates running.
  • Ronen Artzi, Michael Heimlich, and Justin Johnson at AstraZenenca setup Lustre, Gluster and NFS benchmarks using a bcbio StarCluster instance. This initial validation was essential for convincing us of the value of moving to a shared filesystem on AWS.
  • Jason Tetrault, Karl Gutwin and Hank Wu at Biogen provided valuable feedback, suggestions and resources for developing bcbio on AWS.
  • Glen Otero parsed the collectl data and provided graphs, which gave us a detailed look into the potential causes of bottlenecks we found in the timings.
  • James Cuff, Paul Edmon and the team at Harvard FAS research computing built and administered the Regal Lustre setup used for local testing.
  • John Kern and other members of the bcbio community tested, debugged and helped identify issues with the implementation. Community feedback and contributions are essential to bcbio development.

Architecture

The implementation provides both a practical way to run large scale variant calling and RNA-seq analysis, as well as a flexible backend architecture suitable for production quality runs. This writeup might feel a bit like a black triangle moment since I also wrote about running bcbio on AWS three years ago. That implementation was a demonstration for small scale usage rather than a production ready system. We now have a setup we can support and run on large scale projects thanks to numerous changes in the backend architecture:

  • Amazon, and cloud based providers in general, now provide high end filesystems and networking. Our AWS runs are fast because they use SSD backend storage, fast networking connectivity and high end processors that would be difficult to invest in for a local cluster. Renting these is economically feasible now that we have an approach to provision resources, run the analysis, and tear everything down. The dichotomy between local cluster hardware and cloud hardware will continue to expand with upcoming improvements in compute (Haswell processors) and storage (16Tb EBS SSD volumes).
  • Isolating all of the software and code inside Docker containers enables rapid deployment of fixes and improvements. From an open source support perspective, Amazon provides a consistent cluster environment we have full control over, limiting the space of potential system specific issues. From a researcher’s perspective, this will allow use of bcbio without needing to spend time installing and testing locally.
  • The setup runs from standard Linux base images using Ansible scripts and Elasticluster. This means we no longer need to build and update AMIs for changes in the architecture or code. This simplifies testing and pushing fixes, letting us spend less time on support and more on development. Since we avoid having a pre-built AMI, the process of building and running bcbio on AWS is fully auditable for both security and scientific accuracy. Finally, it provides a path to support bcbio on container specific management services like Amazon’s EC2 container service.
  • All long term data storage happens in Amazon’s S3 object store, including both analysis specific data as well as general reference genome data. Downloading reference data for an analysis on demand removes the requirement to maintain large shared EBS volumes. On the analysis side, you maintain only the input files and high value output files in S3, removing the intermediates upon completion of the analysis. Removing the need to manage storage of EBS volumes also provides a cost savings ($0.03/Gb/month for S3 versus $0.10+/Gb/month for EBS) and allows the option of archiving in Glacier for long term storage.

All of these architectural changes provide a setup that is easier to maintain and scale over time. Our goal moving ahead is to provide a researcher friendly interface to setting up and running analyses. We hope to achieve that through the in-development Common Workflow Language from Galaxy, Arvados, Seven Bridges, Taverna and the open bioinformatics community.

Variant calling – benchmarking AWS versus local

We benchmarked somatic variant calling in two environments: on the elasticluster Docker AWS implementation and on local Harvard FAS machines.

  • AWS processing was twice as fast as a local run. The gains occur in disk IO intensive steps like alignment post-processing. AWS offers the opportunity to rent SSD backed storage and obtain a 10GigE connected cluster without contention for network resources. Our local test machines have an in-production Lustre filesystem attached to a large highly utilized cluster provided by Harvard FAS research computing.
  • At this scale Lustre and NFS have similar throughput, with Lustre outperforming NFS during IO intensive steps like alignment, post-processing and large BAM file merging. From previous benchmarking work we’ll need to process additional samples in parallel to fully stress the shared filesystem and differentiate Lustre versus NFS performance. However, the resource plots at this scale show potential future bottlenecks during alignment, post-processing and other IO intensive steps. Generally, having Lustre scaled across 4 LUNs per object storage server (OSS) enables better distribution of disk and network resources.

AWS runs use two c3.8xlarge instances clustered in a single placement group, providing 32 cores and 60Gb of memory per machine. Our local run was comparable with two compute machines, each with 32 cores and 128Gb of memory, connected to a Lustre filesystem. The benchmark is a cancer tumor/normal evaluation consisting of alignment, recalibration, realignment and variant detection with four different callers. The input is a tumor/normal pair from the the ICGC-TCGA DREAM challenge with 100x coverage in exome regions. Here are the times, in hours, for each benchmark:

  AWS (Lustre) AWS (NFS) Local (Lustre)
Total 4:42 5:05 10:30
genome data preparation 0:04 0:10  
alignment preparation 0:12 0:15  
alignment 0:29 0:52 0:53
callable regions 0:44 0:44 1:25
alignment post-processing 0:13 0:21 4:36
variant calling 2:35 2:05 2:36
variant post-processing 0:05 0:03 0:22
prepped BAM merging 0:03 0:18 0:06
validation 0:05 0:05 0:09
population database 0:06 0:07 0:09

To provide more insight into the timing differences between these benchmarks, we automated collection and plotting of resource usage on AWS runs.

Variant calling – resource usage plots

bcbio retrieves collectl usage statistics from the server and prepares graphs of CPU, memory, disk and network usage. These plots allow in-depth insight into limiting factors during individual steps in the workflow.

We’ll highlight some interesting comparisons between NFS and Lustre during the variant calling benchmarking. During this benchmark, the two critical resources were CPU usage and disk IO on the shared filesystems. We also measure memory usage but that was not a limiting factor with these analyses. In addition to the comparisons highlighted below, we have the full set of resource usage graphs available for each run:

CPU

These plots compare CPU usage during processing steps for Lustre and NFS. The largest differences between the two runs are in the alignment, alignment post-processing and variant calling steps:

NFS

Lustre


CPU resource usage for Lustre during variant calling

For alignment and alignment post-processing the Lustre runs show more stable CPU usage. NFS specifically spends more time in the CPU wait state (red line) during IO intensive steps. On larger scale projects this may become a limiting factor in processing throughput. The variant calling step was slower on Lustre than NFS, with inconsistent CPU usage. We’ll have to investigate this slowdown further, since no other metrics point to an obvious bottleneck.

Shared filesystem network usage and IO

These plots compare network usage during processing for Lustre and NFS. We use this as a consistent proxy for the performance of the shared filesystem and disk IO (the NFS plots do have directly measured disk IO for comparison purposes).

NFS

Lustre


Network resource usage Lustre

The biggest difference in the IO intensive steps is that Lustre network usage is smoother compared to the spiky NFS input/output, due to spreading out read/writes over multiple disks. Including more processes with additional read/writes will help determine how these differences translate to scaling on larger numbers of simultaneous samples.

RNA-seq benchmarking

We also ran an RNA-seq analysis using 4 samples from the Sequencing Quality Control (SEQC) project. Each sample has 15 million 100bp paired reads. bcbio handled trimming, alignment with STAR, and quantitation with DEXSeq and Cufflinks. We ran on a single AWS c3.8xlarge machines with 32 cores, 60Gb of memory, and attached SSD storage.

RNA-seq optimization in bcbio is at an earlier stage than variant calling. We’ve done work to speed up trimming and aligning, but haven’t yet optimized the expression and count steps. The analysis runs quickly in 6 1/2 hours, but there is still room for further optimization, and this is a nice example of how we can use benchmarking plots to identify targets for additional work:

Total 6:25
genome data preparation 0:32
adapter trimming 0:32
alignment 0:24
estimate expression 3:41
quality control 1:16

The RNA-seq collectl plots show the cause of the slower steps during expression estimation and quality control. Here is CPU usage over the run:


RNA-seq CPU usage

The low CPU usage during the first 2 hours of expression estimation corresponds to DEXSeq running serially over the 4 samples. In contrast with Cufflinks, which parallelizes over all 32 cores, DEXSeq runs in a single core. We could run these steps in parallel by using multiprocessing to launch the jobs, split by sample. Similarly, the QC steps could benefit from parallel processing. Alternatively, we’re looking at validating other approaches for doing quantification like eXpress. These are the type of benchmarking and validation steps that are continually ongoing in the development of bcbio pipelines.

Reproducing the analysis

The process to launch the cluster and an NFS or optional Lustre shared filesystem is fully automated and documented. It sets up permissions, VPCs, clusters and shared filesystems from a basic AWS account, so requires minimal manual work. bcbio_vm.py has commands to:

  • Add an IAM user, a VPC and create the Elasticluster config.
  • Launch a cluster and bootstrap with the latest bcbio code and data.
  • Create and mount a Lustre filesystem attached to the cluster.
  • Terminate the cluster and Lustre stack upon completion.

The processing handles download of input data from S3 and upload back to S3 on finalization. We store data encrypted on S3 and manage access using IAM instance profiles. The examples below show how to run both a somatic variant calling evaluation and an RNA-seq evaluation.

Running the somatic variant calling evaluation

This analysis performs evaluation of variant calling using tumor/normal somatic sample from the DREAM challenge. To run, prepare an S3 bucket to run the analysis from. Copy the configuration file to your own personal bucket and add a GATK jar. You can use the AWS console or any available S3 client to do this. For example, using the AWS command line client:

aws s3 mb s3://YOURBUCKET-syn3-eval/
aws s3 cp s3://bcbio-syn3-eval/cancer-dream-syn3-aws.yaml s3://YOURBUCKET-syn3-eval/
aws s3 cp GenomeAnalysisTK.jar s3://YOURBUCKET-syn3-eval/jars/

Now ssh to the cluster head node, create the work directory and use bcbio_vm to create a batch script that we submit to SLURM. This example uses an attached Lustre filesystem:

bcbio_vm.py elasticluster ssh bcbio
sudo mkdir -p /scratch/cancer-dream-syn3-exome
sudo chown ubuntu !$
cd !$ && mkdir work && cd work
bcbio_vm.py ipythonprep s3://YOURBUCKET-syn3-eval/cancer-dream-syn3-aws.yaml \
                        slurm cloud -n 60
sbatch bcbio_submit.sh

This runs alignment and variant calling with multiple callers (MuTect, FreeBayes, VarDict and VarScan), validates against the DREAM validation dataset truth calls and uploads the results back to S3 in YOURBUCKET-syn3-eval/final.

Running the RNA-seq evaluation

This example runs an RNA-seq analysis using inputs from the Sequencing Quality Control (SEQC) project. Full details on the analysis are available in the bcbio example run documentation. To setup the run, we copy the input configuration from a publicly available S3 bucket into your own personal bucket:

aws s3 mb s3://YOURBUCKET-eval-rna-seqc/
aws s3 cp s3://bcbio-eval-rna-seqc/eval-rna-seqc.yaml s3://YOURBUCKET-eval-rnaseqc/

Now ssh to the cluster head node, create the work directory and use bcbio_vm to run on 32 cores using the attached EBS SSD filesystem:

bcbio_vm.py elasticluster ssh bcbio
mkdir -p ~/run/eval-rna-seqc/work
cd ~/run/eval-rna-seqc/work
bcbio_vm.py run s3://YOURBUCKET-eval-rna-seqc/eval-rna-seqc.yaml -n 32

This will process three replicates from two different SEQC panels, performing adapter trimming, alignment with STAR and produce counts, Cufflinks quantitation and quality control metrics. The results upload back into your initial S3 bucket as YOURBUCKET-eval-rna-seqc/final, and you can shut down the cluster used for processing.

Costs per hour

These are the instance costs, per hour, for running a 2 node 64 core cluster and associated Lustre filesystem on AWS. For NFS runs, the compute costs are identical but there will not be any additional instances for the shared filesystem. Other run costs will include EBS volumes, but these are small ($0.13/Gb/month) compared to the instance costs over these time periods. We use S3 for long term storage rather than the Lustre or NFS filesystems.

  AWS type n each total
compute entry node c3.large 1 $0.11  
compute worker nodes c3.8xlarge 2 $1.68  
        $3.47/hr
ost (object data store) c3.2xlarge 4 $0.42  
mdt (metadata target) c3.4xlarge 1 $0.84  
mgt (management target) c3.xlarge 1 $0.21  
NATDevice m3.medium 1 $0.07  
Lustre licensing   1 $0.48  
        $3.28/hr
        $6.75/hr

Work to do

The bcbio AWS implementation is freely available and documented and we’ll continue to develop and support it. Some of the areas of immediate improvement we hope to tackle in the future include:

We welcome feedback on the implementation, usage and benchmarking results.

Written by Brad Chapman

December 19, 2014 at 10:17 am

Validating generalized incremental joint variant calling with GATK HaplotypeCaller, FreeBayes, Platypus and samtools

with 20 comments

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.

Terminology

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.

Implementation

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:


Incremental joint calling: GATK HaplotypeCaller, FreeBayes, Platypus, samtools

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:


Pooled calling: GATK HaplotypeCaller, FreeBayes, Platypus, samtools

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:


Joint, pooled and single calling: GATK HaplotypeCaller and samtools

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.

Written by Brad Chapman

October 7, 2014 at 8:53 am