|Publication Type||Journal Article|
|Year of Publication||2011|
|Authors||DePristo, MA, Banks, E, Poplin, R, Garimella, KV, Maguire, JR, Hartl, C, Philippakis, AA, del Angel, G, Rivas, MA, Hanna, M, McKenna, A, Fennell, TJ, Kernytsky, AM, Sivachenko, AY, Cibulskis, K, Gabriel, SB, Altshuler, D, Daly, MJ|
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.