A framework for variation discovery and genotyping using next-generation DNA sequencing data

By Mark A. DePristo, Eric Banks, Ryan Poplin, K. Garimella, J. Maguire, C. Hartl, A. Philippakis, G. Del Angel, M. Rivas, M. Hanna and 8 more. 2011

11,249 citations recorded by the source corpus.

Recent advances in sequencing technology make it possible to comprehensively catalog genetic variation in population samples, creating a foundation for understanding human disease, ancestry and evolution. The amounts of raw data produced are prodigious, and many computational steps are required to translate this output into high-quality variant calls. We present a unified analytic framework to discover and genotype variation among multiple samples simultaneously that achieves sensitive and specific results across five sequencing technologies and three distinct, canonical experimental designs. Our process includes (i) initial read mapping; (ii) local realignment around indels; (iii) base quality score recalibration; (iv) SNP discovery and genotyping to find all potential variants; and (v) machine learning to separate true segregating variation from machine artifacts common to next-generation sequencing technologies. We here discuss the application of these tools, instantiated in the Genome Analysis Toolkit, to deep whole-genome, whole-exome capture and multi-sample low-pass (∼4×) 1000 Genomes Project datasets.

Subfields: Genetics, Molecular Biology

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Citations in the corpus

Next-generation sequencing data interpretation: enhancing reproducibility and accessibility (2012, methodology): “Although most publications discussed in BOX 1 (all are resequencing studies with experimental design similar to that of the 1000 Genomes Project) used recommended mappers (13) and variant callers (12), only four studies used the complete workflow.”
A reference data set of 5.4 million phased human variants validated by genetic inheritance from sequencing a three-generation 17-member pedigree (2016, positive): “Inheritance vectors describing the transmission of the parental haplotypes were calculated for the entire genome using the GATK3 SNV calls (DePristo et al. 2011) combined with the linkage software Merlin (Abecasis et al.”
An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge (2014, methodology): “SAMtools was used by some teams to jointly call SNPs and indels while recalibrating quality scores, while other teams used GATK to call SNPs and indels separately.”
Host genetic variation impacts microbiome composition across human body sites (2015, methodology): “used GATK and applied several hard filters that are recommended for low-coverage whole-genome data [64].”
Whole Genome Sequencing in Autism Identifies Hotspots for De Novo Germline Mutation (2012, neutral): “Subsequent processing was carried out using SAMtools version 0.18, GATK version 1.2-52 (DePristo et al., 2011), and Picard tools version 1.52, which consisted of the following steps: sorting and merging of the BAM files, indel realignment, fixing mate pairs, removal of duplicate reads, base quality…”