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Sept2016 sv nist_intro
This document summarizes data from the Genome in a Bottle Consortium SV Data Jamboree. It discusses several approaches to integrating structural variant calls from multiple technologies to establish a benchmark set of high-confidence SVs: 1) Finding deletions supported by multiple technologies with concordant breakpoints and filtering questionable calls. This approach identified 524 deletions supported by 2+ technologies ranging in size from 20bp to over 3kb. 2) A method called svcompare that compares SV calls across technologies and outputs a multi-sample VCF with variant details from each caller. This identified over 2,000 regions with structural variants called by multiple technologies. 3) A method called svviz that analyzes read support for
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Sept2016 sv nist_intro
- 1. SV Data Jamboree JustinZook and Ali Bashir With the Genome in a Bottle Consortium September 15, 2016
- 2. Sequencing technologies and bioinformaticspipelines disagree O’Rawe et al. Genome Medicine 2013, 5:28
- 3. Sequencing technologies and bioinformaticspipelines disagree O’Rawe et al. Genome Medicine 2013, 5:28
- 4. Candidate NIST ReferenceMaterials Genome PGP ID Coriell ID NIST ID NIST RM # CEPH Mother/Daugh ter N/A GM12878 HG001 RM8398 AJ Son huAA53E0 GM24385 HG002 RM8391 (son)/RM8392 (trio) AJ Father hu6E4515 GM24149 HG003 RM8392 (trio) AJ Mother hu8E87A9 GM24143 HG004 RM8392 (trio) Asian Son hu91BD69 GM24631 HG005 RM8393 Asian Father huCA017E GM24694 N/A N/A Asian Mother hu38168C GM24695 N/A N/A
- 5. Data for GIABPGP Trios Dataset Characteristics Coverage Availability Most useful for… Illumina Paired-end WGS 150x150bp 250x250bp ~300x/individual ~50x/individual on SRA/FTP SNPs/indels/some SVs Complete Genomics 100x/individual on SRA/ftp SNPs/indels/some SVs SOLiD 5500W WGS 50bp single end 70x/son on FTP SNPs Illumina Paired-end WES 100x100bp ~300x/individual on SRA/FTP SNPs/indels in exome Ion Proton Exome 1000x/individual on SRA/FTP SNPs/indels in exome Illumina Mate pair ~6000 bp insert ~30x/individual on FTP SVs Illumina “moleculo” Custom library ~30x by long fragments on FTP SVs/phasing/assembly Complete Genomics LFR 100x/individual on SRA/FTP SNPs/indels/phasing 10X Pseudo-long reads 30-45x/individual on FTP SVs/phasing/assembly PacBio ~10kb reads ~70x on AJ son, ~30x on each AJ parent on SRA/FTP SVs/phasing/assembly /STRs Oxford Nanopore 5.8kb 2D reads 0.02x on AJ son on FTP SVs/assembly Nabsys 2.0 ~100kbp N50 nanopore maps 70x on AJ son SVs/assembly BioNano Genomics 200-250kbp optical map reads ~100x/AJ individual; 57x on Asian son on FTP SVs/assembly
- 6. Data for GIABPGP Trios Dataset Characteristics Coverage Availability Most useful for… Illumina Paired-end WGS 150x150bp 250x250bp ~300x/individual ~50x/individual on SRA/FTP SNPs/indels/some SVs Complete Genomics 100x/individual on SRA/ftp SNPs/indels/some SVs SOLiD 5500W WGS 50bp single end 70x/son on FTP SNPs Illumina Paired-end WES 100x100bp ~300x/individual on SRA/FTP SNPs/indels in exome Ion Proton Exome 1000x/individual on SRA/FTP SNPs/indels in exome Illumina Mate pair ~6000 bp insert ~30x/individual on FTP SVs Illumina “moleculo” Custom library ~30x by long fragments on FTP SVs/phasing/assembly Complete Genomics LFR 100x/individual on SRA/FTP SNPs/indels/phasing 10X Pseudo-long reads 30-45x/individual on FTP SVs/phasing/assembly PacBio ~10kb reads ~70x on AJ son, ~30x on each AJ parent on SRA/FTP SVs/phasing/assembly /STRs Oxford Nanopore 5.8kb 2D reads 0.02x on AJ son on FTP SVs/assembly Nabsys 2.0 ~100kbp N50 nanopore maps 70x on AJ son SVs/assembly BioNano Genomics 200-250kbp optical map reads ~100x/AJ individual; 57x on Asian son on FTP SVs/assembly
- 7. Paper describing data… 51authors 14 institutions 12 datasets 7 genomes Data described in ISA-tab
- 8. Integration Methods toEstablish Benchmark Small Variant Calls Candidate variants Concordant variants Find characteristics of bias Arbitrate using evidence of bias Confidence Level Zook et al., Nature Biotechnology, 2014.
- 9. How can weextend this approach to SVs? Similarities to small variants • Collect callsets from multiple technologies • Compare callsets to find calls supported by multiple technologies Differences from small variants • Callsets generally are not sufficiently sensitive to assume that regions without calls are homozygous reference – SVs of different types/sizes are not always detected easily • Variants are often imprecisely characterized – breakpoints, size, type, etc. • Representation of variants is poorly standardized, especially when complex • Comparison tools in infancy
- 10. Callsets Contributed sofar Short reads • Illumina – Spiral Genetics – cortex – Commonlaw – MetaSV • Complete Genomics • CG-SV • CG-CNV • CG-vcfBeta Long reads and Linked reads • PacBio • CSHL-assembly • Sniffles • PBHoney-spots and –tails • Parliament/pacbio • Parliament/assembly • MultibreakSV • smrt-sv.dip • Assemblytics-Falcon and-MHAP • NHGRI assembly-based • Nanopore mapping • Nabsys force calls • optical mapping • BioNano with and without haplotype-aware assembly • 10X Genomics Chromium • Deletions • Large SVs
- 11. AJ Trio Assemblies OnFTP • PacBio – Falcon – Canu • BioNano – Haploid – Diploid In Process • Illumina – DISCOVAR – contig N50 ~100k • PacBio – Falcon diploid in process • Dovetail scaffolding – With PacBio-falcon – With PacBIo-Canu – With DISCOVAR • 10X? – By itself – Phasing PacBio
- 12. APPROACH #1: FINDDELETIONS WITH SUPPORT FROM MULTIPLE TECHS AND CONCORDANT BREAKPOINTS
- 13. Step 1: Mergingcalls • Process – Find union of calls >19bp from all deletion callsets and merge any regions if within 1000 bp (results in 28460 regions) – Annotate each merged region with fraction covered by calls from each callset – Split out those overlapping tandem repeats longer than 200bp by >25% (2715 regions) • Helps mitigate different representations of calls in repetitive regions and imprecision of breakpoints from many callers • Limitations – may not appropriately call compound heterozygous SVs – Ignores other types of SVs in the region – Loses genotype information Callset #1 Callset #2
- 14. Step 2: Findsize prediction accuracy • Find “size prediction accuracy” of each callset by calculating the difference from the median predicted size for regions with calls from >3 callers, and rank callers for <3kb and >3kb size ranges Spiral 0.00% Cortex 0.24% CGSV 0.65% AssemblyticsFalcon 0.79% CGvcf 1.09% fermikit 1.28% smrtsvdip 1.43% MetaSV 1.57% MultibreakSV 1.62% PBHoneySpots 2.13% AssemblyticsMHAP 2.21% ParliamentAssemblyForce 2.26% CSHLassembly 2.29% ParliamentPacBio 2.92% ParliamentAssembly 3.00% Spiral 0.04% AssemblyticsFalcon 0.06% CGSV 0.06% CSHLassembly 0.08% AssemblyticsMHAP 0.08% MultibreakSV 0.10% fermikit 0.11% PBHoneyTails 0.38% CommonLaw 0.48% ParliamentPacBio 0.58% smrtsvdip 0.62% MetaSV 1.12% sniffles 1.57% Nabsys2tech01Force 3.02% BioNano 3.67% Size >3kbSize <3kb IMPORTANT NOTE: These stats are intended for integration and to help developers improve their methods, not to compare methods, since they likely do not reflect actual size prediction accuracy for all methods.
- 15. Step 3: Findcalls supported by 2 techs 1. Find calls supported by calls from 2 or more technologies with size prediction within 20% 2. Find sensitivity of each caller to these calls in size ranges 20-50, 50-100, 100-1000, 1000- 3000, and >3000 bp
- 16. Step 4: Filterquestionable calls supported by 2+ technologies • 316 calls covered >25% by segmental duplication >10kb • 631 calls with at least one caller predicting a size >2x different from the consensus size • 34 calls where callsets missing this call from multiple technologies have a multiplied (1- sensitivity) < 2% in this size tranche • 87 calls that overlap Ns in the reference
- 17. Overview of process Merge deletions within1kb Rank calls by closeness of predicted size to median size and select call in each region from best callset Find calls supported by 2+ technologies with size within 20% Filter calls overlapping seg dups, reference N’s, or with call with predicted size 2x larger
- 18. Number of CallsSupported by 2 Technologies by Size Range <50bp 50-100bp 100-1000bp 1kb-3kb >3kb pre-filtered 2542 1567 2447 731 730 filtered 2427 1415 2207 638 524
- 19.
- 20. Support for allcandidate regions # of callsets # of technologies
- 21. Support for benchmarkcalls # of callsets # of technologies
- 22. Approach #2: svcompare(NCBI hackathon) Builds on SURVIVOR • Compares each new callset to the first and adds new calls not within 1kb of existing calls • Outputs multi-sample vcf with type, size, and breakpoints from each callset in each candidate region • Integrates multiple types, but doesn’t currently output size of insertions or exact sequence • Developed by Fritz Sedlazeck, JHU Output stats • 130k input regions from calls >19bp • 876 regions have >1 type within a callset • 2276 regions have >1 type across callsets • How to integrate discordant types in same region? https://github.com/NCBI-Hackathons/svcompare
- 23. Example start positiondistance from median start by callset (400-1000bp)
- 24. Approach #3: “Type”candidate calls in each dataset svviz • Looks for whether reads support REF or ALT allele – Can often easily infer genotype • Also generates other stats about mapping reads • Generates visualization of mapped reads as well • Nabsys has developed a similar approach for their mapping data Compatible datasets • PacBio • Illumina 150bp and 250bp paired end • Illumina 6kb mate-pair • 10X haplotype-separated
- 25. 10X SV analyses withsvviz • Find reads supporting ref and alt alleles in each haplotype • Verify support for ref and alt is on different haplotypes for hets • Verify support from both haplotypes for confidence homo var or hom ref call SonDadMomSonDadMom
- 26. Goals for DataJamboree Share progress in algorithm development • New technologies • New analysis methods • Visualization methods • Integration/comparison methods Outstanding questions to discuss • Integration – How to form high-confidence calls, breakpoints, and genotypes from multiple calls? – What is the minimum viable product for a practical benchmark set? • Is this a good criterion: “When an individual callset is compared to ours, most FPs/FNs should be errors in the individual callset” – How to handle non-deletions? • SV typing • Future work – How to form high-confidence regions? – SV phasing – Is anyone developing SV benchmarking tools?
- 27. Things to resolve Integration •How to compare events with variable breakpoints across callsets? – Tandem repeats • How to compare non- deletions? – Start with insertions? • Distinguish precise breakpoints when possible Typing • Leverage long-range information to type with short reads? • How to deal with imprecise breakpoints? • At what point is something validated? – Potentially high-confidence variants (or reference?) – Haplotype-separated
- 28. Acknowledgements • NIST – MarcSalit – Jenny McDaniel – Lindsay Vang – David Catoe – Hemang Parikh • Genome in a Bottle Consortium • GA4GH Benchmarking Team • FDA – Liz Mansfield • SV Callset Contributors – CSHL/JHU – Mt Sinai – 10X – Nabsys – Spiral Genetics/Stanford – Heng Li/Mike Lin – DNAnexus – Complete Genomics – Baylor – Bina/Roche – BioNano Genomics – Mark Chaisson – NIH/NCBI – NIH/NHGRI – Can Alkan/Stanford
Editor's Notes
- #2 Per incoming students – we constantly get proven wrong add stem cell body map slide talk about single-cell normalization with ERCCs calibration with ERCCs touch on GTC connection and Ron Davis’ remarks can we build a sufficiently large technology development portfolio? per Caroline Bertozzi – “Anything can be Interesting.”