RT Journal Article
SR Electronic
T1 Genome Graphs
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 101378
DO 10.1101/101378
A1 Adam M. Novak
A1 Glenn Hickey
A1 Erik Garrison
A1 Sean Blum
A1 Abram Connelly
A1 Alexander Dilthey
A1 Jordan Eizenga
A1 M. A. Saleh Elmohamed
A1 Sally Guthrie
A1 André Kahles
A1 Stephen Keenan
A1 Jerome Kelleher
A1 Deniz Kural
A1 Heng Li
A1 Michael F. Lin
A1 Karen Miga
A1 Nancy Ouyang
A1 Goran Rakocevic
A1 Maciek Smuga-Otto
A1 Alexander Wait Zaranek
A1 Richard Durbin
A1 Gil McVean
A1 David Haussler
A1 Benedict Paten
YR 2017
UL http://biorxiv.org/content/early/2017/01/18/101378.abstract
AB There is increasing recognition that a single, monoploid reference genome is a poor universal reference structure for human genetics, because it represents only a tiny fraction of human variation. Adding this missing variation results in a structure that can be described as a mathematical graph: a genome graph. We demonstrate that, in comparison to the existing reference genome (GRCh38), genome graphs can substantially improve the fractions of reads that map uniquely and perfectly. Furthermore, we show that this fundamental simplification of read mapping transforms the variant calling problem from one in which many non-reference variants must be discovered de-novo to one in which the vast majority of variants are simply re-identified within the graph. Using standard benchmarks as well as a novel reference-free evaluation, we show that a simplistic variant calling procedure on a genome graph can already call variants at least as well as, and in many cases better than, a state-of-the-art method on the linear human reference genome. We anticipate that graph-based references will supplant linear references in humans and in other applications where cohorts of sequenced individuals are available.