Integrating human sequence data sets provides a resource of benchmark SNP and indel genotype calls

By Justin M. Zook, Brad A. Chapman, Jason Wang, D. Mittelman, Oliver Hofmann, Winston A Hide, M. Salit. 2013

764 citations recorded by the source corpus.

Clinical adoption of human genome sequencing requires methods that output genotypes with known accuracy at millions or billions of positions across a genome. Because of substantial discordance among calls made by existing sequencing methods and algorithms, there is a need for a highly accurate set of genotypes across a genome that can be used as a benchmark. Here we present methods to make high-confidence, single-nucleotide polymorphism (SNP), indel and homozygous reference genotype calls for NA12878, the pilot genome for the Genome in a Bottle Consortium. We minimize bias toward any method by integrating and arbitrating between 14 data sets from five sequencing technologies, seven read mappers and three variant callers. We identify regions for which no confident genotype call could be made, and classify them into different categories based on reasons for uncertainty. Our genotype calls are publicly available on the Genome Comparison and Analytic Testing website to enable real-time benchmarking of any method.

Subfields: Cancer Research, Genetics

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Citations in the corpus

TGS-GapCloser: A fast and accurate gap closer for large genomes with low coverage of error-prone long reads (2020, positive): “Using only 10 × coverage of ONT or PacBio long reads [34, 35] applied to 3 de novo assembled human genomes we demonstrate an increase in the scaftig NG50 by 11.0-to 45.0-foldandanincreaseinthescaftigNGA50by6.”
Best practices for variant calling in clinical sequencing (2020, methodology): “Even so, multiple “gold standard” SV callsets such as GIAB [99], HS1011 [102], and HuRef [103] have been published which employ orthogonal sequencing technologies to define reference callsets comprising thousands of structural variants.”
The genome polishing tool POLCA makes fast and accurate corrections in genome assemblies (2019, methodology): “For polishing this assembly, we used Illumina data for the same subject, NA12878, from the Genome In A Bottle project (Zook et al., 2014), dataset 140115_D00360_0009_AH8962ADXX, which contains 553,657,530 149-bp reads.”
An open resource for accurately benchmarking small variant and reference calls (2019, methodology): “The new integration method is designed to give more accurate and comprehensive benchmark sets in several ways, which are detailed at the end of Methods: (1) We use newer input data, including 10x Genomics linked reads, and most data were measured from the homogeneous NIST RM batches of DNA.”
SVIM: structural variant identification using mapped long reads (2018, methodology): “For the first two, we utilized a real 53x coverage dataset of the NA12878 individual from a PacBio RS II machine (Genome in a Bottle consortium; Accession SRR3197748) [32].”
Scaling accurate genetic variant discovery to tens of thousands of samples (2017, methodology): “Genotype concordance (GT concordance), measures the accuracy of genotype calls and is calculated by dividing the number of correctly assigned genotypes (both heterozygous and homozygous variant) by the total number of true positive variants [26].”