Discovery of Lineage-Specific Genome Change in Rice Through Analysis of Resequencing Data
Author(s) -
Robert A. Arthur,
Jeffrey L. Bennetzen
Publication year - 2018
Publication title -
genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.792
H-Index - 246
eISSN - 1943-2631
pISSN - 0016-6731
DOI - 10.1534/genetics.118.300848
Subject(s) - biology , genome , genetics , lineage (genetic) , computational biology , evolutionary biology , gene
New mutations are rare, which makes their discovery laborious and time-consuming. Arthur and Bennetzen describe an approach for enriching recent mutations that relies only on a reference genome sequence and resequencing data for other... Genome comparisons provide information on the nature of genetic change, but such comparisons are challenged to differentiate the importance of the actual sequence change processes relative to the role of selection. This problem can be overcome by identifying changes that have not yet had the time to undergo millions of years of natural selection. We describe a strategy to discover accession-specific changes in the rice genome using an abundant resource routinely provided for many genome analyses, resequencing data. The sequence of the fully sequenced rice genome from variety Nipponbare was compared to the pooled (∼114×) resequencing data from 126 japonica rice accessions to discover “Nipponbare-specific” sequences. Analyzing nonrepetitive sequences, 8504 “candidate” Nipponbare-specific changes were detected, of which around two-thirds are true novel sequence changes and the rest are predicted genome sequencing errors. Base substitutions outnumbered indels in this data set by > 28:1, with ∼8:5 bias toward transversions over transitions, and no transposable element insertions or excisions were observed. These results indicate that the strategy employed is effective for finding recent sequence changes, sequencing errors, and rare alleles in any organism that has both a reference genome sequence and a wealth of resequencing data.
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