RT Journal Article
SR Electronic
T1 Fast and Accurate Genomic Analyses using Genome Graphs
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 194530
DO 10.1101/194530
A1 Goran Rakocevic
A1 Vladimir Semenyuk
A1 James Spencer
A1 John Browning
A1 Ivan Johnson
A1 Vladan Arsenijevic
A1 Jelena Nadj
A1 Kaushik Ghose
A1 Maria C. Suciu
A1 Sun-Gou Ji
A1 Gülfem Demir
A1 Lizao Li
A1 Berke Ç. Toptaş
A1 Alexey Dolgoborodov
A1 Björn Pollex
A1 Irina Glotova
A1 Péter Kómár
A1 Yilong Li
A1 Milos Popovic
A1 Wan-Ping Lee
A1 Morten Källberg
A1 Amit Jain
A1 Deniz Kural
YR 2017
UL http://biorxiv.org/content/early/2017/09/29/194530.abstract
AB The human reference genome serves as the foundation for genomics by providing a scaffold for sequencing read alignment, but currently only reflects a single consensus haplotype, impairing read alignment and downstream analysis accuracy. Reference genome structures incorporating known genetic variation have been shown to improve the accuracy of genomic analyses, but have so far remained computationally prohibitive for routine large-scale use. Here we present a graph genome implementation that enables read alignment across 2,800 diploid genomes encompassing 12.6 million SNPs and 4.0 million indels. Our graph genome aligner and variant calling pipeline consume around 5.5 and 2 hours per high coverage whole-genome-sequenced sample, respectively, comparable to those of state-of-the-art linear reference genome-based methods. Using orthogonal benchmarks based on real and simulated data, we show that using a graph genome reference improves read mapping sensitivity and produces a 0.5 percentage point increase in variant calling recall, which extrapolates into 20,000 additional variants being detected per sample, while variant calling specificity is unaffected. Structural variations (SVs) incorporated into a graph genome can be directly genotyped from read alignments in a rapid and accurate fashion. Finally, we show that iterative augmentation of graph genomes yields incremental gains in variant calling accuracy. Our implementation is the first practical step towards fulfilling the promise of graph genomes to radically enhance the scalability and precision of genomic analysis by incorporating prior knowledge of population characteristics.One Sentence Summary Genome graphs incorporating common genetic variation enable efficient variant identification at population scale.