Blue Collar Bioinformatics

Note: new posts have moved to Please look there for the latest updates and comments

Archive for the ‘xprize’ Category

An automated ensemble method for combining and evaluating genomic variants from multiple callers

with 10 comments


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.

We aligned reads with novoalign and performed deduplication, base recalibration and realignment using GATK best practices. With these prepared reads, we called variants with five approaches:

  • 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:

concordant: total 111159
concordant: SNPs 101495
concordant: indels 9664
rep1 discordant: total 9857
rep1 discordant: SNPs 7514
rep1 discordant: indels 2343
rep2 discordant: total 11029
rep2 discordant: SNPs 8070
rep2 discordant: indels 2959
het/hom discordant 4181

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:

concordant: total 116608
concordant: SNPs 107063
concordant: indels 9545
rep1 discordant: total 9555
rep1 discordant: SNPs 7581
rep1 discordant: indels 1974
rep2 discordant: total 10445
rep2 discordant: SNPs 7780
rep2 discordant: indels 2665
het/hom discordant 3975

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 xprize/NA12878-exome-v_03 folder:

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:

Concordance/discordance for calling methods

  • 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:

python 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_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.

Written by Brad Chapman

February 6, 2013 at 7:25 am

Genomics X Prize public phase update: variant classification and de novo calling

with 7 comments


Last month I described our work at HSPH and EdgeBio preparing reference genomes for the Archon Genomics X Prize public phase, detailing methods used in the first version of our NA19239 variant calls. We’ve been steadily improving the calling approaches, and released version 0.2 on the X Prize validation website and GenomeSpace. Here I’ll describe the improvements we’ve made over the last month, focusing on two specific areas:

  • De novo calling: Zam Iqbal suggested using his cortex_var de novo variant caller in addition to the current GATK, FreeBayes and samtools callers. With his help, we’ve included these calls in this release, and provide comparisons between de novo and alignment based methods.
  • Improved variant classification: Consolidating variant calls from multiple callers involves making tough choices about when to include or exclude variants. I’ll describe the details of selecting metrics for use in SVM classification and filtering of variants.

Our goal is to iteratively improve our calling and variant preparation to create the best possible set of reference calls. I’d be happy to talk more with anyone working on similar problems or with insight into useful ways to improve our current callsets. We have a Get Satisfaction site for discussion and feedback, and have offered a $2500 prize for helpful comments.

As a reminder, all of the code and data used here is freely available:

  • The variant analysis infrastructure, built on top of GATK, automates genome preparation, normalization and comparison. It provides a full pipeline, driven by simple configuration files, for consolidating multiple variant calls.
  • The combined variant calls, including training data and potential true and false positives, are available from GenomeSpace: Public/chapmanb/xprize/NA19239-v0_2.
  • The individual variant calls for each technology and calling method are also available from GenomeSpace: Public/EdgeBio/PublicData/Release1.

de novo variant calling with cortex_var

de novo variant calling performs reference-free assembly of either local or global genome regions, then subsequently uses these assemblies to call variants relative to a known reference. The advantage is that assemblies can avoid errors associated with mapping to the reference, helping resolve large variations as well as small variations near problem alignments or low complexity regions.

Hybrid approaches that use localized de novo assembly in variant regions help mitigate the extensive computational requirements associated with whole-genome assembly. Complete Genomics variant calling and GATK 2.0’s Haplotype Caller both provide pipelines for hybrid de novo assembly in variant detection. The fermi and SGA assemblers are also used in variant calling, although the paths from assembly to variants are not as automated.

Thanks to Zam’s generous assistance, we used cortex_var for localized de novo assembly and variant calling within individual fosmid boundaries. As a result, CloudBioLinux now contains automated build instructions for cortex_var , handling binary builds for multiple k-mer and color combinations. An automated cortex_var pipeline, part of the bcbio-nextgen toolkit, runs the processing workflow:

  • Start with reads aligned to fosmid regions using novoalign.
  • For each fosmid region, extract all mapped reads along with a local reference genome for variant calling.
  • de novo assemble all reads in the fosmid region and call variants against the local reference genome using cortex_var’s Bubble Caller.
  • Remap regional variant coordinates back to the full genome.
  • Combine all regional calls into the final set of cortex_var calls.

Directly comparing GATK and cortex_var calls shows similar levels of concordance and discordance as the GATK/samtools comparison from the last post:

concordant: total 153787
concordant: SNPs 130913
concordant: indels 22874
GATK discordant: total 20495
GATK discordant: SNPs 6522
GATK discordant: indels 13973
cortex_var discordant: total 26790
cortex_var discordant: SNPs 21342
cortex_var discordant: indels 5448

11% of the GATK calls and 14% of the cortex_var calls are discordant. The one area where cortex_var does especially well is on indels: 19% of the cortex_var indels do not agree with GATK, in comparison with 37% of the GATK calls and 25% of the samtools calls. The current downside to this is SNP calling, where cortex_var has 3 times more discordant calls than GATK.

Selection of classification metrics

Overlapping variant calls from different calling methods (GATK, FreeBayes, samtools and cortex_var) and sequencing technologies (Illumina, SOLiD and IonTorrent) produces 170,286 potential calls in the fosmid regions. 58% (99,227) of these are present in all callers and technologies, so we need to do better than the intersection in creating a consolidated callset.

As detailed in the previous post, we filter the full set in two ways. The first is to keep trusted variants based on their presence in a defined number of technologies or callers. We currently have an inclusive set of criteria: keep variants present in either 4 out of the 7 callsets, 2 distinct technologies, or 3 distinct callers. This creates a trusted set containing 95% (162,202) of the variants. Longer term the goal is to reduce the trusted count and rely on automated filtering approaches based on input metrics.

This second automated filtering step uses a support vector machine (SVM) to evaluate the remaining variants. We train the SVM on potential true positives from variants that overlap in all callers and technologies, and potential false positives found uniquely in one single caller. The challenge is to find useful metrics associated with these training variants that will help provide discrimination.

In version 0.1 we filtered with a vanilla set of metrics: depth and variant quality score. To identify additional metrics, we used a great variant visualization tool developed by Keming Labs alongside HSPH and EdgeBio. I’ll write up more details about the tool once we have a demonstration website but the code is already available on GitHub.

To remove variants preferentially associated with poorly mapping or misaligned reads, we identified two useful metrics. ReadPosEndDist, written as a GATK annotation by Justin Zook at NIST, identifies variants primarily supported by calls at the ends of reads. Based on visual examination, these associate with difficult to map regions, as identified by Genome Mappability Scores:

Secondly, we identified problematic allele balances that differ from the expected ratios. For haploid fosmid calls, we expect 100% of reads to support variants and 0% to support reference calls (in diploid calls, you also need to handle heterozygotes with 50% expected allele balance). In practice, the distribution of reads can differ due to sequencer and alignment errors. We use a metric that measures deviation from the expected allele balance and associates closely with variant likelihoods:

Improved consolidated calls

To assess the influence of adding de novo calls and additional filtering metrics on the resulting call set, we compare against whole genome Illumina and Complete Genomics calls for NA19239. Previously we’d noticed two major issues during this comparison: a high percentage of discordant indel calls and a technology bias signaled by better concordance with Illumina than Complete.

The comparison between fosmid and Illumina data shows a substantial improvement in the indel bias. Previously 46% of the total indel calls were discordant, indicative of a potential false positive problem. With de novo calls and improved filtering, we’ve lowered this to only 10% of the total calls.

concordant: total 147684
concordant: SNPs 133861
concordant: indels 13823
fosmid discordant: total 7519
fosmid discordant: SNPs 5856
fosmid discordant: indels 1663
Illumina discordant: total 5640
Illumina discordant: SNPs 1843
Illumina discordant: indels 3797

This improvement comes with a decrease in the total number of concordant indel calls, since we moved from 17,816 calls to 13,823. However a large number of these seemed to be Illumina specific since 60% of the previous calls were discordant when compared with Complete Genomics. The new callset reduces this discrepancy and only 22% of the indels are now discordant:

concordant: total 139155
concordant: SNPs 127243
concordant: indels 11912
fosmid discordant: total 15484
fosmid discordant: SNPs 12028
fosmid discordant: indels 3456
Complete Genomics discordant: total 7311
Complete Genomics discordant: SNPs 4972
Complete Genomics discordant: indels 2273

These comparisons provide some nice confirmation that we’re moving in the right direction on filtering. As before, we extract potential false positives and false negatives to continue to refine the calls: potential false positives are those called in the fosmid dataset and in neither of the Illumina or Complete Genomics sets. Potential false negatives are calls that both Illumina and Complete agree on that the fosmid calls lack.

In the new callsets, there are 5,499 (3.5%) potential false positives and 1,422 (0.9%) potential false negatives. We’ve reduced potential false positives in the previous set from 10% with a slight increase in false negatives. These subsets are available along with the full callset on GenomeSpace. We’re also working hard on an NA12878 callset with equivalent approaches and will make that available soon for community feedback.

I hope this discussion, open source code, and dataset release is useful to everyone working on problems of improving variant calling accuracy and filtering. I welcome feedback on calling, consolidation methods, interesting metrics to explore, machine learning or any of the other topics discussed here.

Written by Brad Chapman

September 17, 2012 at 8:41 am

Genomics X Prize public phase: reference genome preparation and comparisons to Illumina and Complete Genomics

with 3 comments


The Archon Genomics X Prize, presented by Express Scripts, is a 10 million dollar competition to establish highly accurate clinical grade sequencing and variation detection methods. Our group at Harvard School of Public Health works with the EdgeBio team on developing the infrastructure for the competition: identify variations in the grading genomes and provide software to compare these reference variation sets against a competitor’s list of variations.

The exciting aspect of the Genomics X Prize is that it enables open comparisons between sequencing technologies and variant calling methodologies. Sequencing genomes to the high degree of accuracy sufficient for clinical usage is a difficult, open, problem. Here I’ll present detailed numbers comparing variants called by different sequencing technologies and variant callers.

The public phase of the Genomics X Prize starts today, August 15th. The goal of this six month period is to have an open dialog with everyone working in the sequencing and variant calling communities. We want to refine our methods to provide the most accurate and fair variant calling for the reference genomes. To start the discussion we’ve prepared:

The goal of this writeup, and the X Prize public phase, is to iterate over calling and unification methods to improve our algorithms and approaches. Rather than promoting or disparaging any particular technology or calling method, we’re instead providing full transparency and a good-faith effort to combining approaches. Our hope is that this will help engage the community, encourage feedback, and result in a unbiased and accurate set of reference genomes for the competition.

Unification of variant calls

For the August 15th public phase kickoff, we prepared a reference data set of NA19239 based on pooled sequencing of haploid fosmid clones. The callable regions of these clones totaled 129,513,026 total bases, covering ~4% of the 3.1 billion bases in the human genome. We use fosmid clones to obtain complete regional haplotype coverage and focus on partial genome coverage to achieve high coverage depth and accuracy for assessed regions.

Version 0.1 of the NA19239 reference set uses variant calls from two technologies: Illumina and SOLiD; and three callers: GATK’s Unified Genotyper, FreeBayes and SAMtools. To move from these data to a unified call set we:

  • Align to GRCh37 reference genome with Novoalign.
  • Perform post-processing and indel realignment with GATK’s IndelRealigner.
  • Perform variant calling with GATK’s UnifiedGenotyper, FreeBayes and samtools mpileup.
  • Do pairwise comparisons between all technology/caller approaches.
  • Generate the union of all possible calls and merge with initial GATK calls, recalling any no-call positions at expected sites.
  • Use validation information on variants found in multiple technologies, plus metrics associated with common variants, to filter the full call set to a final set of trusted calls.

The challenging decisions begin when merging and filtering the final call set. This requires careful bookkeeping and variant representation to ensure identical variants are directly comparable, followed by setting cutoffs for variant inclusion.

Comparison details

The details of variant comparisons introduce an additional layer of complexity during assessment. The approach we’ve taken is create a normalized set of variants so all comparison differences are due to actual call differences rather than variant representation. We split multiple nucleotide polymorphisms into individual calls, split complex indel-variant combinations, and left-align remaining variants.

For haploid/diploid comparisons, we establish haplotype blocks for the diploid sequence based on phasing provided in the input variant file, and then compare the best matching haplotype to our fosmid reference. Single nucleotide polymorphisms and indels less than 30bp require exact machines between two comparison genomes. Larger indels and structural variations receive more flexible matching with confidence intervals around start and end coordinates.

The goal of the normalized, compared variants is to reflect real underlying differences in calling approaches relative to how well we can currently resolve variation endpoints.

Comparisons between variation callers

For a concrete example of two different variant calling approaches, below is a table comparing GATK variants against samtools calls for the NA19239 sample, using identically aligned and post-processed BAMs:

concordant: total 160851
concordant: SNPs 136146
concordant: indels 24705
GATK discordant: total 13925
GATK discordant: SNPs 1315
GATK discordant: indels 12610
samtools discordant: total 25368
samtools discordant: SNPs 17247
samtools discordant: indels 8121

The number of discordant variant calls is high, making up 8% of the GATK calls and 14% of the samtools calls, and samtools calls almost 16,000 additional SNPs compared to GATK. As a result, a large percentage of variants require making hard decisions: are those additional calls interesting, real variants in samtools and false negatives in the GATK calls? Or conversely, are they false positives in samtools that GATK correctly excludes?

Comparisons between sequencing technologies

There is a similar level of discrepancy when comparing variant calls between Illumina and SOLiD sequencing. Below is a comparison between GATK Unified genotyper calls on the two technologies:

concordant: total 135263
concordant: SNPs 122267
concordant: indels 12996
Illumina discordant: total 39491
Illumina discordant: unique 7079
Illumina discordant: SNPs 15188
Illumina discordant: indels 24303
SOLiD discordant: total 16022
SOLiD discordant: unique 3800
SOLiD discordant: SNPs 3908
SOLiD discordant: indels 12114

Unique coverage explains some differences: 4% of the Illumina variants (7079) and 2.5% (3800) of the SOLiD variants were uniquely covered by the technologies. However, the remaining variant discordant calls are on the order of those seen in the technology comparisons. Adding to the complexity, we find only 84% of the total concordant variants compared to the Illumina only GATK/samtools comparison.

Unified call set

The level of discrepancy between calling methods and sequencing approaches introduces complexity in the preparation of the final call set: How much evidence does a variant need for inclusion? Can single calls be true positives if supported by high confidence values? This will require extensive refinement throughout the public phase. For the initial version 0.1 release of NA19239, we took the following high level approach to filtering:

  • Retain variants found in 4 out of 6 calling/technology methods (including genotyping data).
  • Retain variants identified across multiple technologies.
  • Retain variants found in both more stringent (GATK) and more lenient (FreeBayes, samtools) callers.
  • Assess remaining variants using a Support Vector Machine with quality score, read depth and variant distance from read ends metrics, training the classifier on likely true and false positives from the pairwise overlap comparisons.

The result is a unified call set of 171,009 variants derived from all technologies and callers, that we’re releasing as NA19239 version 0.1.

Comparisons with whole genome datasets

To assess the quality of the unified call set, we compared to two public genomes:

This provides us with three independent call sets to assess variability between approaches. To provide a baseline, here is the comparison of the Illumina and Complete Genomics calls in our assessment regions:

Overall genotype concordance 98.47
concordant: total 205868
concordant: SNPs 186365
concordant: indels 19503
Illumina discordant: total 31267
Illumina discordant: SNPs 19334
Illumina discordant: indels 11933
Complete Genomics discordant: total 15174
Complete Genomics discordant: SNPs 9586
Complete Genomics discordant: indels 5510

We see familiar discordance rates: 13% of the Illumina calls and 7% of the Complete Genomics calls differ. Since it’s diploid versus diploid, this comparison includes all heterozygous variant matches. As a result the numbers in this comparison will be higher, but it is a good order of magnitude approximation for looking at our fosmid reference set versus each individual technology.


The comparison against the Illumina whole genome variant calls contains 12% discordant calls in our fosmid reference set, with 79% of those being indel differences. Indels are notoriously more difficult to identify and assess, so this will be an area of increased focus as we move forward:

concordant: total 150420
concordant: SNPs 132604
concordant: indels 17816
fosmid discordant: total 19624
fosmid discordant: SNPs 4165
fosmid discordant: indels 15459
Illumina discordant: total 5475
Illumina discordant: SNPs 2952
Illumina discordant: indels 2523

Complete Genomics

The Complete Genomics comparison has 17% discordant calls including 2x more discordant SNP calls. This highlights another key area of call set refinement: identifying and correcting for technology specific calls.

concordant: total 139559
concordant: SNPs 126296
concordant: indels 13263
fosmid discordant: total 29883
fosmid discordant: SNPs 10162
fosmid discordant: indels 19721
Complete Genomics discordant: total 7571
Complete Genomics discordant: SNPs 5542
Complete Genomics discordant: indels 1965


The initial NA19239 public genome for the Genomics X Prize provides unified variant calls based on two sequencing technologies and three calling methods. I’ve delved into a lot of details on our approaches, challenges and goals with the hopes of encouraging suggestions from other researchers working on these problems. We’re especially interested in feedback on these areas of ongoing research:

  • Digging deeper into potential false positives and negatives: By combining comparison information between the unified callset and external resources, we can identify 17654 fosmid variants (10%) not found in both the Complete Genomics and Illumina datasets. These require additional in-depth analysis to classify as uniquely identified fosmid calls or potential false positives. Similarly, Illumina and Complete Genomics combine to call 1228 variants (0.7%) that are not in the fosmid call set. These need examination to classify as fosmid false negatives, or false positive calls in the individual technologies.
  • Additional public genomes: We’re actively working with teams like the Genome in a Bottle Consortium and Genome Research Consortium to compare with their reference sets and approaches. Our next target public genome is NA12878, used in both of these projects and widely studied.
  • Improve variant representation and assessment: The variation software framework works hard to make variant representations as uniform as possible. Indels are especially challenging and we welcome practical examples of regions that need additional standardization.
  • Refine approaches to unifying variant calls: What we learn from the additional inspection of discordant variants can help inform improved approaches to filtering. This is a great opportunity to develop generalized, reusable methods for combining variants from multiple approaches.

The call sets used here are available as public data folders on GenomeSpace:

  • Public/chapmanb/xprize/NA19239-v0_1 – The combined final call set along with training true/false positives and Illumina/Complete Genomics comparison based potential false positives and negatives.
  • Public/EdgeBio/PublicData/Release1 – All of the raw input data, including fastq files, BAM alignments and individual variant calls.

Combined with the open source code and configurations, we hope this will provided interested researchers with all the raw materials needed to reproduce and extend these analyses. Your feedback and suggestions are very welcome.

Written by Brad Chapman

August 15, 2012 at 9:10 am