Format

Send to

Choose Destination
BMC Genomics. 2016 Jan 16;17:64. doi: 10.1186/s12864-016-2366-2.

svclassify: a method to establish benchmark structural variant calls.

Author information

1
Genome-Scale Measurements Group, Material Measurement Laboratory, National Institute of Standards and Technology, 100 Bureau Dr, MS8313, Gaithersburg, MD, 20899, USA. parikhhemangm@gmail.com.
2
Dakota Consulting Inc., 1110 Bonifant Street, Suite 310, Silver Spring, MD, 20910, USA. parikhhemangm@gmail.com.
3
Bina Technologies, Roche Sequencing, Redwood City, CA, 94065, USA. marghoob.mohiyuddin@bina.roche.com.
4
Bina Technologies, Roche Sequencing, Redwood City, CA, 94065, USA. hugo.lam@bina.roche.com.
5
Statistical Engineering Division, National Institute of Standards and Technology, Gaithersburg, MD, 20899, USA. hariharan.iyer@nist.gov.
6
Institute for Research in Electronics and Applied Physics, University of Maryland, College Park, MD, 20742, USA. desuchen@umd.edu.
7
Personalis Inc., 1350 Willow Road, Suite 202, Menlo Park, CA, 94025, USA. markrpratt@gmail.com.
8
Personalis Inc., 1350 Willow Road, Suite 202, Menlo Park, CA, 94025, USA. gabor.bartha@personalis.com.
9
Genome-Scale Measurements Group, Material Measurement Laboratory, National Institute of Standards and Technology, 100 Bureau Dr, MS8313, Gaithersburg, MD, 20899, USA. nspies@stanford.edu.
10
Department of Pathology, Stanford University, Stanford, CA, USA. nspies@stanford.edu.
11
Institute for Research in Electronics and Applied Physics, University of Maryland, College Park, MD, 20742, USA. wlosert@umd.edu.
12
Genome-Scale Measurements Group, Material Measurement Laboratory, National Institute of Standards and Technology, 100 Bureau Dr, MS8313, Gaithersburg, MD, 20899, USA. jzook@nist.gov.
13
Genome-Scale Measurements Group, Material Measurement Laboratory, National Institute of Standards and Technology, 100 Bureau Dr, MS8313, Gaithersburg, MD, 20899, USA. salit@nist.gov.
14
Bioengineering Department, Stanford University, Stanford, CA, USA. salit@nist.gov.

Abstract

BACKGROUND:

The human genome contains variants ranging in size from small single nucleotide polymorphisms (SNPs) to large structural variants (SVs). High-quality benchmark small variant calls for the pilot National Institute of Standards and Technology (NIST) Reference Material (NA12878) have been developed by the Genome in a Bottle Consortium, but no similar high-quality benchmark SV calls exist for this genome. Since SV callers output highly discordant results, we developed methods to combine multiple forms of evidence from multiple sequencing technologies to classify candidate SVs into likely true or false positives. Our method (svclassify) calculates annotations from one or more aligned bam files from many high-throughput sequencing technologies, and then builds a one-class model using these annotations to classify candidate SVs as likely true or false positives.

RESULTS:

We first used pedigree analysis to develop a set of high-confidence breakpoint-resolved large deletions. We then used svclassify to cluster and classify these deletions as well as a set of high-confidence deletions from the 1000 Genomes Project and a set of breakpoint-resolved complex insertions from Spiral Genetics. We find that likely SVs cluster separately from likely non-SVs based on our annotations, and that the SVs cluster into different types of deletions. We then developed a supervised one-class classification method that uses a training set of random non-SV regions to determine whether candidate SVs have abnormal annotations different from most of the genome. To test this classification method, we use our pedigree-based breakpoint-resolved SVs, SVs validated by the 1000 Genomes Project, and assembly-based breakpoint-resolved insertions, along with semi-automated visualization using svviz.

CONCLUSIONS:

We find that candidate SVs with high scores from multiple technologies have high concordance with PCR validation and an orthogonal consensus method MetaSV (99.7 % concordant), and candidate SVs with low scores are questionable. We distribute a set of 2676 high-confidence deletions and 68 high-confidence insertions with high svclassify scores from these call sets for benchmarking SV callers. We expect these methods to be particularly useful for establishing high-confidence SV calls for benchmark samples that have been characterized by multiple technologies.

PMID:
26772178
PMCID:
PMC4715349
DOI:
10.1186/s12864-016-2366-2
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for BioMed Central Icon for PubMed Central
Loading ...
Support Center