BIGGIE: A Distributed Pipeline for Genomic Variant Callingkubitron/courses/... · and use the right...
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BIGGIE: A Distributed Pipeline for Genomic Variant Calling Richard Xia 1 , Sara Sheehan 1 , Yuchen Zhang 1 , Ameet Talwalkar 1 , Matei Zaharia 1 Jonathan Terhorst 2 , Michael Jordan 1,2 , Yun S. Song 1,2 , Armando Fox 1 , David Patterson 1 1 Computer Science Division, UC Berkeley; 2 Department of Statistics, UC Berkeley Motivation: faster, open-source genome variant calling tools Impact: Human genome variation is being used more and more to impact disease diagnosis and treatment, however: I current tools frequently disagree on variant calls I different types of variation require specialized tools Current Tools: I GATK [2]: slow and difficult to use I CASAVA [1]: fast, but not free I samtools mpileup [3]: slow and some accuracy issues Our Goal: I fast, distributed variant caller I separate the genome into regions of high and low complexity and use the right tool for the right region Figure 1: BIGGIE pipeline Per-base SNP caller Main idea: I Distrubted pipeline for variant calling using Spark [4] I Assign a complexity score to each base I Use a simple SNP caller at bases with a low complexity score I Use more robust structural variant callers at high complexity bases Complexity region examples: Figure 2: Different variant calling tools should be used for regions of the genome. Complexity score features: Name Weight Description Substitution 3 Number of aligned reads showing a sub- stitution with respect to the reference. Insertion 10 Number of aligned reads showing an in- sertion with respect to the reference. Deletion 10 Number of aligned reads showing a dele- tion with respect to the reference. Low Quality 3 Number of reads aligned with low map quality (a common indicator of a repeti- tive region). Table 1: Relative weight of features for computing complexity. Results Simulating data: I Used reads simulated from the consensus sequence for Venter’s genome I Better approximates the true pattern of SNPs, indels, and structural variants found in a true genome; reads were aligned using BWA and SNAP Effect of thresholds: Figure 3: On the left are the per-base results, measuring false negatives only on the regions we called. Both accuracy measures increase as the threshold increases, but the number of correct calls increases as well. We see a similar pattern on the right for the region results, where the number of false positives and false negatives increase with the density of complex bases in high-complexity regions, but the number of true calls increases as well. Incorporating high complexity regions Figure 4: Regions are fairly uniformly distributed, except near the chromosome ends. I We group bases into a high-complexity region in a greedy fashion, maintaining that the overall high-complexity base density is > t I We filter out regions that are < 500 bases long Stats, t = 5% Number of high complexity regions 3603 Percentage of genome is high complexity regions 16.6% Results g Timing Results: Algorithm Runtime GATK 35m 17s mpileup 49m 53s BIGGIE 4m 38s Table 2: Timing results for GATK, mpileup, and BIGGIE. The runtime is not significantly impacted by the complexity threshold. Low vs. High Complexity: region type false pos false neg correct low-complexity 1824 7455 38232 high-complexity 2289 2788 13046 Table 3: Our performance degrades in the high complexity regions, which is why a special purpose variant caller should be used. Figure 5: Accuracy comparison of BIGGIE with mpileup and GATK. False positives in BIGGIE are often associated with alignment errors or confusion with a small indel. For each algorithm, a very small percentage of correct SNP bases actually have the incorrect (unphased) genotype. Future Work: Use the high and low complexity regions to distribute the reads across machines, then call variants using appropriate algorithms. References g [1] CASAVA. (2012) http://support.illumina.com/sequencing/sequencing software/casava.ilmn. [2] DePristo M. et al, “A framework for variation discovery and genotyping using next-generation DNA sequencing data.” Nature Genetics (2011), 43:491-498. [3] Li H. et al and 1000 Genome Project Data Processing Subgroup, “The Sequence alignment/map (SAM) format and SAMtools.” Bioinformatics (2009), 25: 2078-9. [4] Zaharia M. et al, “Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing.” NSDI (2012). {rxia, ssheehan, yuczhang}@eecs.berkeley.edu
Transcript of BIGGIE: A Distributed Pipeline for Genomic Variant Callingkubitron/courses/... · and use the right...
BIGGIE: A Distributed Pipeline for Genomic Variant CallingRichard Xia1, Sara Sheehan1, Yuchen Zhang1, Ameet Talwalkar1, Matei Zaharia1 Jonathan Terhorst2,
Michael Jordan1,2, Yun S. Song1,2, Armando Fox1, David Patterson1
1 Computer Science Division, UC Berkeley; 2 Department of Statistics, UC Berkeley
Motivation: faster, open-source genome variant calling tools
Impact:Human genome variation is being used more and more to impactdisease diagnosis and treatment, however:
I current tools frequently disagree on variant callsI different types of variation require specialized tools
Current Tools:I GATK [2]: slow and difficult to useI CASAVA [1]: fast, but not freeI samtools mpileup [3]: slow and some accuracy issues
Our Goal:I fast, distributed variant callerI separate the genome into regions of high and low complexity
and use the right tool for the right region Figure 1: BIGGIE pipeline
Per-base SNP caller
Main idea:I Distrubted pipeline for variant calling using Spark [4]I Assign a complexity score to each baseI Use a simple SNP caller at bases with a low complexity scoreI Use more robust structural variant callers at high complexity bases
Complexity region examples:
Figure 2: Different variant calling tools should be used for regions of the genome.
Complexity score features:
Name Weight DescriptionSubstitution 3 Number of aligned reads showing a sub-
stitution with respect to the reference.Insertion 10 Number of aligned reads showing an in-
sertion with respect to the reference.Deletion 10 Number of aligned reads showing a dele-
tion with respect to the reference.Low Quality 3 Number of reads aligned with low map
quality (a common indicator of a repeti-tive region).
Table 1: Relative weight of features for computing complexity.
Results
Simulating data:I Used reads simulated from the consensus sequence for Venter’s genomeI Better approximates the true pattern of SNPs, indels, and structural variants
found in a true genome; reads were aligned using BWA and SNAP
Effect of thresholds:
10-2 10-1 100 101 102
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Accruacy vs. percentage complex bases
false posfalse neg
Figure 3: On the left are the per-base results, measuring false negatives only on the regions wecalled. Both accuracy measures increase as the threshold increases, but the number of correctcalls increases as well. We see a similar pattern on the right for the region results, where thenumber of false positives and false negatives increase with the density of complex bases inhigh-complexity regions, but the number of true calls increases as well.
Incorporating high complexity regions
0.8 1.0 1.2 1.4 1.6 1.8 2.0genome position 1e7
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High Complexity Regions, chr 21, part 1
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High Complexity Regions, chr 21, part 1
Figure 4: Regions are fairly uniformly distributed, except near the chromosome ends.
I We group bases into a high-complexity region in a greedy fashion,maintaining that the overall high-complexity base density is > t
I We filter out regions that are < 500 bases long
Stats, t = 5%Number of high complexity regions 3603
Percentage of genome is high complexity regions 16.6%
Results g
Timing Results:
Algorithm RuntimeGATK 35m 17s
mpileup 49m 53sBIGGIE 4m 38s
Table 2: Timing results for GATK,mpileup, and BIGGIE. The runtimeis not significantly impacted by thecomplexity threshold.
Low vs. High Complexity:
region type false pos false neg correctlow-complexity 1824 7455 38232high-complexity 2289 2788 13046
Table 3: Our performance degrades in the highcomplexity regions, which is why a special purposevariant caller should be used.
SNP false pos SNP false neg0
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60000BWA+GATKSNAP+GATKBWA+mpileupBWA+BIGGIE,base=0.1BWA+BIGGIE,region=5%
Figure 5: Accuracy comparison of BIGGIE with mpileup and GATK. False positives in BIGGIE areoften associated with alignment errors or confusion with a small indel. For each algorithm, a verysmall percentage of correct SNP bases actually have the incorrect (unphased) genotype.
Future Work: Use the high and low complexity regions to distribute the readsacross machines, then call variants using appropriate algorithms.
References g
[1] CASAVA. (2012) http://support.illumina.com/sequencing/sequencing software/casava.ilmn.[2] DePristo M. et al, “A framework for variation discovery and genotyping using next-generation DNA sequencing data.” Nature Genetics(2011), 43:491-498.[3] Li H. et al and 1000 Genome Project Data Processing Subgroup, “The Sequence alignment/map (SAM) format and SAMtools.”Bioinformatics (2009), 25: 2078-9.[4] Zaharia M. et al, “Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing.” NSDI (2012).
{rxia, ssheehan, yuczhang}@eecs.berkeley.edu
