title: Leveraging long read sequencing from a single individual to provide a comprehensive resource for benchmarking variant calling methods.
abstract: A high-confidence, comprehensive human variant set is critical in assessing accuracy of sequencing algorithms, which are crucial in precision medicine based on high-throughput sequencing. Although recent works have attempted to provide such a resource, they still do not encompass all major types of variants including structural variants (SVs). Thus, we leveraged the massive high-quality Sanger sequences from the HuRef genome to construct by far the most comprehensive gold set of a single individual, which was cross validated with deep Illumina sequencing, population datasets, and well-established algorithms. It was a necessary effort to completely reanalyze the HuRef genome as its previously published variants were mostly reported five years ago, suffering from compatibility, organization, and accuracy issues that prevent their direct use in benchmarking. Our extensive analysis and validation resulted in a gold set with high specificity and sensitivity. In contrast to the current gold sets of the NA12878 or HS1011 genomes, our gold set is the first that includes small variants, deletion SVs and insertion SVs up to a hundred thousand base-pairs. We demonstrate the utility of our HuRef gold set to benchmark several published SV detection tools.
authors.affiliation: Bina Technologies, Roche Sequencing, Redwood City, CA 94065, USA.
authors.forename: John C
authors.initials: JC
authors.lastname: Mu
authors.affiliation: Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA.
authors.forename: Pegah
authors.initials: P
authors.lastname: Tootoonchi Afshar
authors.affiliation: Bina Technologies, Roche Sequencing, Redwood City, CA 94065, USA.
authors.forename: Marghoob
authors.initials: M
authors.lastname: Mohiyuddin
authors.affiliation: Department of Statistics, Stanford University, Stanford, CA 94305, USA.
authors.forename: Xi
authors.initials: X
authors.lastname: Chen
authors.affiliation: Bina Technologies, Roche Sequencing, Redwood City, CA 94065, USA.
authors.forename: Jian
authors.initials: J
authors.lastname: Li
authors.affiliation: Bina Technologies, Roche Sequencing, Redwood City, CA 94065, USA.
authors.forename: Narges
authors.initials: N
authors.lastname: Bani Asadi
authors.affiliation: Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.
authors.forename: Mark B
authors.initials: MB
authors.lastname: Gerstein
authors.affiliation: Department of Statistics, Stanford University, Stanford, CA 94305, USA.|Department of Health Research and Policy, Stanford University, Stanford, CA 94305, USA.
authors.forename: Wing H
authors.initials: WH
authors.lastname: Wong
authors.affiliation: Bina Technologies, Roche Sequencing, Redwood City, CA 94065, USA.
authors.forename: Hugo Y K
authors.initials: HY
authors.lastname: Lam
mesh_terms: D019985:Benchmarking; D014644:Genetic Variation; D015894:Genome, Human; D023281:Genomics / Q000379:methods; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods* / Q000592:standards; D006801:Humans