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.
Variant detection and grading overview
Developing pipelines for detecting variants from high throughput sequencing data is challenging due to rapidly changing algorithms and relatively low concordance between methods. This post will discuss automated methods providing evaluation of variant calls, enabling detailed diagnosis of discordant differences between multiple calling approaches. This allows us to:
- Characterize strengths and weaknesses of alignment, post-alignment preparation and calling methods.
- Automatically verify pipeline updates and installations to ensure variant calls recover expected variations. This extends the XPrize validation protocol to provide full summary metrics on concordance and discordance of variants.
- Make recommendations on best-practice approaches to use in sequencing studies requiring either exome or whole genome variant calling.
- Identify characteristics of genomic regions more likely to have discordant variants which require additional care when making biological conclusions based on calls, or lack of calls, in these regions.
This evaluation work is part of a larger community effort to better characterize variant calling methods. A key component of these evaluations is a well characterized set of reference variations for the NA12878 human HapMap genome, provided by NIST’s Genome in a Bottle consortium. The diagnostic component of this work supplements emerging tools like GCAT (Genome Comparison and Analytic Testing), which provides a community platform for comparing and discussing calling approaches.
I’ll show a 12 way comparison between 2 different aligners (novoalign and bwa mem), 2 different post-alignment preparation methods (GATK best practices and the Marth lab’s gkno pipeline), and 3 different variant callers (GATK UnifiedGenotyper, GATK HaplotypeCaller, and FreeBayes). This allows comparison of openly available methods (bwa mem, gkno preparation, and FreeBayes) with those that require licensing (novoalign, GATK’s variant callers). I’ll also describe bcbio-nextgen, the fully automated open-source pipeline used for variant calling and evaluation, which allows others to easily bring this methodology into their own work and extend this analysis.
Aligner, post-alignment preparation and variant calling comparison
To compare methods, we called variants on a NA12878 exome dataset from EdgeBio’s clinical pipeline and assessed them against the NIST Genome in a Bottle reference material. Discordant positions where the reference and evaluation variants differ fall into three different categories:
- Extra variants, called in the evaluation data but not in the reference. These are potential false positives.
- Missing variants, found in the NA12878 reference but not in the evaluation data set. These are potential false negatives. The use of high quality reference materials from NIST enables identification of genomic regions we fail to call in.
- Shared variants, called in both the evaluation and reference but differently represented. This could result from allele differences, such as heterozygote versus homozygote calls, or variant identification differences, such as indel start and end coordinates.
To further identify causes of discordance, we subdivide the missing and extra variants using annotations from the GEMINI variation framework:
- Low coverage: positions with limited read coverage (4 to 9 reads).
- Repetitive: regions identified by RepeatMasker.
- Error prone: variants falling in motifs found to induce sequencing errors.
We subdivide and restrict our comparisons to help identify sources of differences between methods indistinguishable when looking at total discordant counts. A critical subdivison is comparing SNPs and indels separately. With lower total counts of indels but higher error rates, each variant type needs independent visualization. Secondly, it’s crucial to distinguish between discordance caused by a lack of coverage, and discordance caused by an actual difference in variant assessment. We evaluate only in callable regions with 4 or more reads. This low minimum cutoff provides a valuable evaluation of low coverage regions, which differ the most between alignment and calling methods.
I’ll use this data to provide recommendations for alignment, post-alignment preparation and variant calling. In addition to these high level summaries, the full dataset and summary plots available below providing a starting place for digging further into the data.
We compared two recently released aligners designed to work with longer reads coming from new sequencing technologies: novoalign (3.00.02) and bwa mem (0.7.3a). bwa mem identified 1389 additional concordant SNPs and 145 indels not seen with novoalign. 1024 of these missing variants are in regions where novoalign does not provide sufficient coverage for calling. Of those, 92% (941) have low coverage with less than 10 reads in the bwa alignments. Algorithmic changes impact low coverage regions more due to the decreased evidence and susceptibility to crossing calling coverage thresholds, so we need extra care and consideration of calls in these regions.
Our standard workflow uses novoalign based on its stringency in resolving large insertions and deletions. These results suggest equally good results using bwa mem, along with improved processing times. One caveat to these results is that some of the available Illumina call data that feeds into NIST’s reference genomes comes from a bwa alignment, so some differences may reflect a bias towards bwa alignment heuristics. Using non-simulated reference data sets has the advantage of capturing real biological and process errors, but requires iterative improvement of the reference materials to avoid this type of potential algorithmic bias.
Post-alignment preparation and quality score recalibration
We compared two methods of quality recalibration:
- GATK’s best practices (2.4-9): This involves de-duplication with Picard MarkDuplicates, GATK base quality score recalibration and GATK realignment around indels.
- The Marth Lab’s gkno realignment pipeline: This performs de-duplication with samtools rmdup and realignment around indels using ogap. All commands in this pipeline work on streaming input, avoiding disk IO penalties by using unix pipes. This piped approach improves scaling on large numbers of whole genome samples. Notably, our implementation of the pipeline does not use a base quality score recalibration step.
GATK best practice pipelines offer an advantage over the gkno-only pipeline primarily because of improvements in SNP calling from base quality recalibration. Specifically we lose ~1% (824 / 77158) of called variations. These fall into the discordant missing “other” category, so we cannot explain them by metrics such as coverage or genome difficulty. The simplest explanation is that initial poor quality calculations in those regions result in callers missing those variants. Base quality recalibration helps recover them. These results match Brendan O’Fallon’s recent analysis of base quality score recalibration.
This places a practical number on the lost variants when avoiding recalibration either due to scaling or GATK licensing concerns. Some other options for recalibration include Novoalign’s Quality Recalibration and University of Michigan’s BamUtil recab, although we’ve not yet tested either in depth as potential supplements to improve calling in non-GATK pipelines.
For this comparison, we used three general purpose callers that handle SNPs and small indels, all of which have updated versions since our last comparison:
- FreeBayes (0.9.9 296a0fa): A haplotype-based Bayesian caller from the Marth Lab, with filtering on quality score and read depth.
- GATK UnifiedGenotyper (2.4-9): GATK’s widely used Bayesian caller, using filtering recommendations for exome experiments from GATK’s best practices.
- GATK HaplotypeCaller (2.4-9): GATK’s more recently developed haplotype caller which provides local assembly around variant regions, using filtering recommendations for exomes from GATK’s best practices.
Adjusting variant calling methods has the biggest impact on the final set of calls. Called SNPs differ by 4577 between the three compared approaches, in comparison with aligner and post-alignment preparation changes which resulted in a maximum difference of 1389 calls. This suggests that experimenting with variant calling approaches currently provides the most leverage to improve calls.
A majority of the SNP concordance differences between the three calling methods are in low coverage regions with between 4 and 9 reads. GATK UnifiedGenotyper performs the best in detecting SNPs in these low coverage regions. FreeBayes and GATK HaplotypeCaller both call more conservatively in these regions, generating more potential false negatives. FreeBayes had the fewest heterozygote/homozygote discrimination differences of the three callers.
For indels, FreeBayes and HaplotypeCaller both provide improved sensitivity compared to UnifiedGenotyper, with HaplotypeCaller identifying the most, especially in low coverage regions. In contrast to the SNP calling results, FreeBayes has more calls that match the expected indel but differ in whether a call is a heterozygote or homozygote.
No one caller outperformed the others on all subsets of the data. GATK UnifiedGenotyper performs best on SNPs but is less sensitive in resolving indels. GATK HaplotypeCaller identifies the most indels, but is more conservative than the other callers on SNPs. FreeBayes provides intermediate sensitivity and specificity between the two for both SNPs and indels. A combined UnifiedGenotyper and HaplotypeCaller pipeline for SNPs and indels, respectively, would provide the best overall calling metrics based on this set of comparisons.
Low coverage regions are the key area of difference between callers. Coupled with the alignment results and investigation of variant changes resulting from quality score binning, this suggests we should be more critical in assessing both calls and coverage in these regions. Assessing coverage and potential false negatives is especially critical since we lack good tools to summarize and prioritize genomic regions that are potentially missed during sequencing. This also emphasizes the role of population-based calling to help resolve low coverage regions, since callers can use evidence from multiple samples to better estimate the likelihoods of low coverage calls.
Automated calling and grading pipeline
Method comparisons become dated quickly due to the continuous improvement in aligners and variant callers. While these recommendations are useful now, in 6 months there will be new releases with improved approaches. This rapid development cycle creates challenges for biologists hoping to derive meaning from variant results: do you stay locked on software versions whose trade offs you understand, or do you attempt to stay current and handle re-verifying results with every new release?
Our goal is to provide a community developed pipeline and comparison framework that ameliorates this continuous struggle to re-verify. The analysis done here is fully automated as part of the bcbio-nextgen analysis framework. This framework code provides full exposure and revision tracking of all parameters used in analyses. For example, the ngsalign module contains the command lines used for bwa mem and novoalign, as well as all other tools.
To install the pipeline, third-party software and required data files:
wget https://raw.github.com/chapmanb/bcbio-nextgen/master/scripts/bcbio_nextgen_install.py python bcbio_nextgen_install.py /usr/local /usr/local/share/bcbio-nextgen
To re-run this analysis, retrieve the input data files and configuration as described in the bcbio-nextgen example documentation with:
$ mkdir config && cd config $ wget https://raw.github.com/chapmanb/bcbio-nextgen/master/config/\ examples/NA12878-exome-methodcmp.yaml $ cd .. && mkdir input && cd input $ wget https://dm.genomespace.org/datamanager/file/Home/EdgeBio/\ CLIA_Examples/NA12878-NGv3-LAB1360-A/NA12878-NGv3-LAB1360-A_1.fastq.gz $ wget https://dm.genomespace.org/datamanager/file/Home/EdgeBio/\ CLIA_Examples/NA12878-NGv3-LAB1360-A/NA12878-NGv3-LAB1360-A_2.fastq.gz $ wget https://s3.amazonaws.com/bcbio_nextgen/NA12878-nist-v2_13-NGv3-pass.vcf.gz $ wget https://s3.amazonaws.com/bcbio_nextgen/NA12878-nist-v2_13-NGv3-regions.bed.gz $ gunzip NA12878-nist-*.gz $ wget https://s3.amazonaws.com/bcbio_nextgen/NGv3.bed.gz $ gunzip NGv3.bed.gz
Then run the analysis, distributed on 8 local cores, with:
$ mkdir work && cd work $ bcbio_nextgen.py bcbio_system.yaml ../input ../config/NA12878-exome-methodcmp.yaml -n 8
The bcbio-nextgen documentation describes how to parallelize processing over multiple machines using cluster schedulers (LSF, SGE, Torque).
The pipeline and comparison framework are open-source and configurable for multiple aligners, preparation methods and callers. We invite anyone interested in this work to provide feedback and contributions.
Full data sets
We extracted the conclusions for alignment, post-alignment preparation and variant calling from analysis of the full dataset. The visualizations for the full data are not as pretty but we make them available for anyone interested in digging deeper:
- Summary CSV of comparisons split by methods and concordance/discordance types, easily importable into R or pandas for further analysis.
- Code for preparing and plotting results
- Full comparisons of all 12 methods, stratified by concordance and discordance: SNPs and indels
- Boxplots of differences between alignment methods: SNPs and indels
- Boxplots of differences between post-alignment preparation methods: SNPs and indels
- Boxplots of differences between variant calling methods: SNPs and indels
The comparison variant calls are also useful for pinpointing algorithmic differences between methods. Some useful subsets of variants:
- Concordant variants called by bwa and not novoalign, where novoalign did not have sufficient coverage in the region. These are calls where either novoalign fails to map some reads, or bwa maps too aggressively: VCF of bwa calls with low or no coverage in novoalign.
- Discordant variants called consistently by multiple calling methods. These are potential errors in the reference material, or consistently problematic calling regions for multiple algorithms. Of the 9004 shared discordants, the majority are potential false negatives not seen in the evaluation calls (7152; 79%). Another large portion is heterozygote/homozygote differences, which make up 1627 calls (18%). 6652 (74%) of the differences have low coverage in the exome evaluation, again reflecting the difficulties in calling in these regions. The VCF of discordants found in 2 or more callers contains these calls, with a ‘GradeCat’ INFO tag specifying the discordance category.
We encourage reanalysis and welcome suggestions for improving the presentation and conclusions in this post.
On April 7th and 8th, a group of biologists and programmers gathered at the Broad Institute to work on improving interoperability of open-source bioinformatics tools. Organized by the Open Bioinformatics Foundation and GenomeSpace team, this was part of the lead up to the Bioinformatics Open Source Conference (BOSC) in July in Berlin. The event is part of an ongoing series of coding sessions (Codefests or Hackathons) organized by the open bioinformatics community, which give programmers who typically work together remotely a chance to code and discuss in the same place for two days. These have been successful in both producing new code and in building connections which help sustain development of these community projects.
Goals and outcomes
One major challenge in analyzing biological data is interfacing multiple bioinformatics tools. Tools often work independently, and where general architectures like plugins or API exist they are often project specific. This results in isolated islands of data exchange, but transferring data or resources between tools requires work that is often rate-limiting or insurmountable.
Our goal at the hackathon was to provide simple APIs and implementations that help facilitate transfers between multiple islands of functionality. GenomeSpace does this by providing a central hub and API to push and pull from tools. We wanted to generalize this to support multiple tools, and build client implementations that demonstrate this in practice. The long term goal is to encourage tool developers to provide server side APIs compatible with the more general library, making extension of the connector toolkit easier. For developers, the client API would allow them to easily transfer files between multiple tools without needing to learn and implement the specific transfer APIs of each tool.
We called this high level client library Genome Connector (gcon, for short) and took a practical approach by implementing client libraries that provide a common interface to multiple tools: GenomeSpace, Galaxy, BaseSpace, 23andMe and general key-value stores through jClouds. To identify a reasonable amount of work for two days, we focused on file transfer: authentication, finding files, getting and putting files to remote analysis platforms. In addition we defined some critical components for doing biological work:
- File metadata: We need to be able to store arbitrary key/value on objects to assign essential biological information necessary to interpret it, like organisms and genome build. In addition, metadata allows provenance and tracking of files by enabling annotation of files with history and processing steps.
- Filesets: Large biological files have secondary files with indexes, allowing indexed retrieval of data (for example: read bam and bai, variant vcf and idx, tabix gz and tbi). To avoid expensive reindexing, we want to group and transfer these together.
We also identified other useful extensions that would help improve interoperability and facilitate building connected tools, like providing Publish/subscribe hooks to avoid having to poll servers for updates, and smarter approaches to sending data to avoid duplication and unnecessary transfer of data.
The output of our discussion and coding are common Genome Connector implementations in multiple languages. GitHub repositories are available for in-progress Java, Python and Clojure implementations. These wrap multiple diverse tools and expose them through a common top level API, allowing developers to push and pull data from multiple tools.
I’m immensely grateful to the incredible participants who generously donated their time and expertise to help with these projects. For anyone interested we also have detailed documentation on discussions during the hackathon.
Bioinformatics Open Source Conference
If you’re a bioinformatics programmers interested in open source coding and helping answer biological questions by improving usability and connectivity of tools, you’re welcome to join the OpenBio and BOSC communities. We’ve created a biological interoperability mailing list for additional discussion. The next BOSC conference is July 19th and 20th in Berlin, Germany as part of the ISMB conference. There will also be another two day Codefest proceeding BOSC on July 17th and 18th. Abstracts for talks at BOSC are due this Friday, April 12th. Looking forward to seeing everyone at future BOSC and coding events.
BAM file size reduction and quality score binning
We have a large upcoming whole genome sequencing project with Illumina, and they approached us about delivering BAM files with reduced resolution base quality scores. They have a white paper describing the approach, which involves binning scores to reduce resolution. This reduces the number of scores describing the quality of a base from 40 down to 8.
The advantage of this approach is a significant reduction in file size. BAM files use BGZF compression, and the underlying gzip DEFLATE algorithm compresses based on shared text regions. Reducing the number of quality values increases shared blocks and improves compression. This reduces BAM file sizes by 25-35%: an exome BAM file reduced from 5.7Gb to 3.7Gb after quality binning.
The potential downside is that the reduction in quality resolution may impact alignment and variant calling approaches that rely on base quality scores. To assess this, I implemented quality score binning as part of the bcbio-nextgen analysis pipeline using the CRAM toolkit and ran alignment, recalibration, realignment and variant calling on:
- The original unbinned 40-resolution base quality BAM from an NA12878 exome.
- The BAM binned into 8-resolution base qualities before alignment.
- The BAM binned into 8-resolution base qualities before alignment and binned again following base quality score recalibration.
A comparison of alignment and variant calls from the three approaches indicates that binning has nearly no impact on alignment and a small impact on variant calls, primarily in low depth regions.
We aligned 100bp paired end reads with Novoalign, a quality aware aligner. Comparison of mapped reads showed nearly no impact on total mapped reads. The plot below shows a generic delta of changes in mapped reads across the 22 autosomes alongside the increase in unmapped pairs. Out of 73 million total reads, the changes account for ~0.003% of the total reads. There also did not appear to be any worrisome patterns of loss for specific chromosomes. Overall, there is a minimal impact of quality score binning on the ability to align the reads.
Variant call differences
We called variants using the GATK Unified Genotyper following the best practice recommendations for exomes and then compared calls from original and binned quality scores. Both approaches for binning — pre-binning, and pre-binning plus post-quality recalibration binning — showed similar levels of concordance to non-binned quality scores: 99.81 and 99.78, respectively. Since the additional binning after recalibration provides a smaller prepared BAM file for storage and has a similar impact to pre-binning only, we used this for additional analysis of discordant variants.
The table below shows the discordant differences between the 40 quality score resolution and binned, 8 quality score resolution BAMs. 40 quality discordant variants are those called with full quality score resolution but not called, or called differently, after binning to 8 quality score resolution. Conversely, the 8-quality discordants are those called uniquely after quality binning:
|Overall genotype concordance||99.78|
|40-quality discordant: total||821|
|40-quality discordant: SNPs||759|
|40-quality discordant: indels||62|
|8-quality discordant: total||1289|
|8-quality discordant: SNPs||1240|
|8-quality discordant: indels||49|
We investigated the discordant variants further since 1.5% of the total variant calls change as a result of binning, Of the 1851 unique discordant variants, approximately half (928) fall into reproducible variants identified by looking at ensemble combinations of replicates. Of these potentially problematic discordant variants more than half are in low coverage regions with less than 10 reads:
The major influence of quality score binning is resolution of variants in low coverage regions. This manifests as differences in heterozygote and homozygote calling, indel representation and filtering differences related to quality and mappability. To assess the potential impact, we looked at the loss in callable bases on a 30x whole genome sequence when moving from a minimum of 5 reads to a minimum of 10, using GATK’s CallableLoci tool. Regions with read coverage of 5 to 9 make up 4.7 million genome positions, 0.17% of the total callable bases.
|5 read minimum||10 read minimum|
|No coverage/ poor mapping||71,272,008||71,272,008|
In conclusion, quality score binning provides a useful reduction in input file sizes with minimal impact on alignment. For variant calling, use additional caution in low coverage regions with less than 10 supporting reads. Given the rapid increases in read throughput that are driving the need for file size reduction, quality score binning is a worthwhile tradeoff for high-coverage recalling work.
A key goal of the Archon Genomics X Prize infrastructure is development of a set of highly accurate reference genome variants. I’ve described our work preparing these reference genomes, and specifically defined the challenges behind merging genomic variant calls from multiple technologies and calling methods. Comparing calls from two different calling methods, for example GATK and samtools mpileup, produces a large number of differing variants which need reconciliation. Taking the overlapping subset from multiple callers is too conservative and will miss real variations, while including all calls is too liberal and introduces false positives.
Here I’ll describe a fully automated approach for preparing an accurate set of combined variant calls. Ensemble machine learning methods are a powerful way to incorporate inputs from multiple models. We use a heuristic and support vector machine (SVM) algorithm to consolidate variants, producing a final set of calls with better sensitivity and specificity than current best practice methods. The approach is open source, fully automated and generalizable to both human diploid sequencing as well as X Prize haploid reference fosmids.
We use a pair of replicates from EdgeBio’s clinical exome sequencing pipeline to prepare ensemble variant calls in the widely studied HapMap NA12878 genome. Compared to variants from a single calling method, the ensemble method produced more concordant variants when comparing the replicates, with fewer discordants. The finalized ensemble calls also provide a useful method to compare strengths and weaknesses of individual calling methods. The implementation is freely available and I’ll discuss how to get it running on your data so you can use, critique and extend the methods. This work is a collaboration between Harvard School of Public Health, EdgeBio and NIST.
Comparison materials and algorithm
A difficult aspect of evaluating variant calling methods is establishing a reference set of calls. For X Prize we use three established methods, each of which comes with tradeoffs. Metrics like transition/transversion ratios or dbSNP overlap provide a global picture of calling but are not fine grained enough to distinguish improvements over best practices. Sanger validation restricts you to a manageable subset of calls. Comparisons against public resources like 1000 genomes bias results towards technologies and callers used in preparing those variant callsets.
Here we employ a fourth method by comparing replicates from EdgeBio’s clinical exome sequencing pipeline. These are NA12878 samples independently prepared using Nimblegen’s version 3.0 kit and sequenced on an Illumina HiSeq. By comparing the replicates in regions with 4 or more reads in both samples, we identify the ability of variant calling algorithms to call identical variations with differing coverage and error profiles.
- GATK UnifiedGenotyper – Bayesian approach to call SNPs and indels, treating each position independently.
- GATK HaplotypeCaller – Performs local de-novo assembly to call SNPs and indels on individual haplotypes.
- FreeBayes – Bayesian calling approach that handles simultaneous SNPs and indel calling via assessment of regional haplotypes.
- samtools mpileup – Uses an approach similar to GATK’s UnifiedGenotyper for SNP and indel calling.
- VarScan – Calls variants using a heuristic/statistic approach eliminating common sources of bias.
We took a combined heuristic and machine learning approach to consolidate these five sets of variant calls into a final ensemble callset. The first step is to prepare the union of all variant calls from the input callers, identifying calling methods that support each variant. Secondly, we annotate each variant with metrics including strand bias, allele balance, regional sequence entropy, position of calls within reads, regional base quality and overall genotype likelihoods. We then filter this prepared set of all possible variants to produce a final set of trusted calls.
The first filtering step is to heuristically identify trusted variants based on the number of callers supporting them. This configurable parameter allow you to make an initial conservative cutoff for including variants in the final calls: I trust variants supported by N or more callers.
For the remaining calls that fall below the trusted support cutoff, we distinguish true and false positives using a support vector machine (SVM). The annotated metrics described above are the input parameters and we prepare true and false positives for the classifier using a multi-step process:
- Use variants found in all callers as the true positive set, and variants found in a single caller as false positives. Use these training variants to identify an initial set of below-cutoff variants to include and exclude.
- With this initial set of below-cutoff true/false variants, re-train multiple classifiers stratified based on variant characteristics: variant type (indels vs SNPs), zygosity (heterozygous vs homozygous) and regional sequence complexity.
- Use these final classifiers to identify included and excluded variants falling below the trusted calling support cutoff.
The final set of calls includes the trusted variants and those that pass the SVM filtering. An input configuration file for variant preparation and assessment allows adjustment of the trusted threshold as well as defining which metrics to use for building the SVM classifiers.
Ensemble calling improvements
We assess calling sensitivity and specificity by comparing concordant and discordant variant calls between the replicates. To provide a consistent method to measure both SNP and indel correctness, we use the positive predictive value as the percentage of concordant calls between duplicates (concordant variants / (concordant variants + discordant variants)). This is different than the overall concordance rate, which also includes non-variant regions where both replicates do not call a variation. As a result these percentages will be lower if you expect the 99% values that result when including reference calls. The advantage of this metric is that it’s easily interpreted as the percentage of concordant called variants. It also allows individual comparisons of SNPs and indels, since the overall number of indels are low compared to the total bases considered. GATK’s VariantEval documentation has a nice discussion of alternative metrics to genotype concordance.
As a baseline we used calls from GATK’s UnifiedGenotyper to represent a current best practice approach. GATK calls 117079 SNPs, 86.6% of which are concordant. It also calls 14966 indels, with 64.6% concordant. Here are the full concordant and discordant numbers, broken down by variant type and replicate:
|rep1 discordant: total||9857|
|rep1 discordant: SNPs||7514|
|rep1 discordant: indels||2343|
|rep2 discordant: total||11029|
|rep2 discordant: SNPs||8070|
|rep2 discordant: indels||2959|
Our ensemble method produces improvements in both total concordant variants detected and the ratio of concordant to discordants. For SNPs, the ensemble calls add 5345 additional variants to a total of 122424, with an increase in concordance to 87.4%. For indels the major improvement is in removal of discordants: We identify 14184 indels with 67.2% concordant. Here is the equivalent table for the ensemble method:
|rep1 discordant: total||9555|
|rep1 discordant: SNPs||7581|
|rep1 discordant: indels||1974|
|rep2 discordant: total||10445|
|rep2 discordant: SNPs||7780|
|rep2 discordant: indels||2665|
For scientists who have worked to increase sensitivity and specificity of individual variant callers, it’s exciting to be able to improve both simultaneously. As mentioned above, you can also tune the method to increase specificity or sensitivity by adjusting the support needed for including trusted variants.
The final ensemble callsets from both replicates are available as VCF files from GenomeSpace in the
Comparison of calling methods
Calling the same samples with multiple callers allows direct comparisons between calling methods. The advantage of producing an accurate final set of ensemble calls is that this provides a baseline to evaluate the strengths and weaknesses of different calling methods. The figure below compares concordant, missing variants and additional variants called by each of the 5 methods in comparison with the consolidated ensemble calls:
- GATK UnifiedGenotyper provides the best SNP calling, followed closely by samtools mpileup.
- For indel calling, the GATK HaplotypeCaller produces the most concordant calls followed by UnifiedGenotyper and FreeBayes. UnifiedGenotyper does good as well, but is conservative and has the fewest additional indels. FreeBayes and GATK HaplotypeCaller both provide resolution of individual haplotypes which helps in regions with heterozygous indels or closely spaced SNPs and indels.
- If you want to use a single variant caller, GATK UnifiedGenotyper does the best overall job.
- If you wanted to choose free open-source tools for calling, I would recommend samtools for SNP calling and FreeBayes for indel calling.
Variant calling methods with recommendations for both calling and filtering provide the best out of the box performance. An advantage of GATK and samtools is they provide calling, variant quality metrics, and filtering. On the other side, FreeBayes is a good example of a powerful tool that takes some time to learn to filter optimally. One potential source of bias in producing the individual calls is that I personally have more experience with GATK tools so may have room to improve with the other callers.
Availability and usage
Combining multiple calling approaches improves both sensitivity and specificity of the final set of variants. The downside is the need to run and coordinate calls from all of the different callers. To mitigate this, we developed an automated pipeline that ties together multiple open-source tools using two custom components:
- bcbio-nextgen – A Python framework to run a full sequencing analysis pipeline from input fastq files to consolidated ensemble variant calls. It supports multiple aligners and variant callers, and enables distributed work over multiple cores on a large machine or multiple machines in a cluster environment.
- bcbio.variation – A Clojure toolkit build on top of GATK’s variant API that provides ensemble call preparation as well as more general functionality for normalizing and comparing variants produced by multiple callers.
bcbio-nextgen has a script, built on functionality in the CloudBioLinux project, that automates installation of associated variant callers and data dependencies:
wget https://raw.github.com/chapmanb/bcbio-nextgen/master/scripts/bcbio_nextgen_install.py python bcbio_nextgen_install.py install_directory data_directory
With the dependencies installed, you describe the input files and analysis with a YAML formatted input file. The NA12878 ensemble configuration file used for this analysis provides a useful starting point. Run the analysis, distributed on multiple cores, with:
bcbio_nextgen.py bcbio_system.yaml ensemble_sample.yaml -n 8
The bcbio-nextgen documentation provides additional details about configuration inputs and distributed processing. The framework generally handles the automation and processing involved with high throughput sequencing analysis.
EdgeBio kindly made the NA12878 datasets used in this analysis publicly available:
I welcome feedback on the approach, data or tools and am actively working to extend this and make it easier to use. As re-sequencing becomes increasingly important for human health applications it is critical that we develop open, shared best-practice workflows to handle the data processing. This allows us to focus back on the fun and difficult work of understanding the biology.