Integrating sequencing datasets to form highly confident SNP and indel genotype calls for a whole human genome

Abstract

Clinical adoption of human genome sequencing requires methods with known accuracy of genotype calls at millions or billions of positions across a genome. Previous work showing discordance amongst sequencing methods and algorithms has made clear the need for a highly accurate set of genotypes across a whole genome that could be used as a benchmark. We present methods to make highly confident SNP, indel, and homozygous reference genotype calls for NA12878, the pilot genome for the Genome in a Bottle Consortium. We minimize bias towards any method by integrating and arbitrating between 14 datasets from 5 sequencing technologies, 7 mappers, and 3 variant callers. Regions for which no confident genotype call could be made are identified as uncertain, and classified into different reasons for uncertainty. Our highly confident genotype calls are publicly available on the Genome Comparison and Analytic Testing (GCAT) website to enable real-time benchmarking of any method.

References in corpus (2)

• Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM
• Haplotype-based variant detection from short-read sequencing

Cited by in corpus (6)

• Towards Better Understanding of Artifacts in Variant Calling from High-Coverage Samples
• Computing the BWT and LCP array of a Set of Strings in External Memory
• Finding easy regions for short-read variant calling from pangenome data
• AirLift: A Fast and Comprehensive Technique for Remapping Alignments between Reference Genomes
• FermiKit: assembly-based variant calling for Illumina resequencing data
• BEETL-fastq: a searchable compressed archive for DNA reads