Rapid whole-genome mutational profiling using next-generation sequencing technologies
Author(s) -
Andrew R. Smith,
Aaron R. Quinlan,
Heather E. Peckham,
Kathryn Makowsky,
Wei Tao,
Betty Woolf,
Lei Shen,
William F. Donahue,
Nadeem Tusneem,
Michael P. Strömberg,
Donald A. Stewart,
Lu Zhang,
Swati Ranade,
Jason Warner,
Clarence Lee,
B E Coleman,
Zheng Zhang,
Stephen F. McLaughlin,
Joel A. Malek,
Jon M. Sorenson,
Alan P. Blanchard,
Jarrod Chapman,
David R. Hillman,
Feng Chen,
Daniel S. Rokhsar,
Kevin McKernan,
Thomas W. Jeffries,
Gábor Marth,
Paul M. Richardson
Publication year - 2008
Publication title -
genome research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 9.556
H-Index - 297
eISSN - 1549-5469
pISSN - 1088-9051
DOI - 10.1101/gr.077776.108
Subject(s) - biology , genetics , genome , dna sequencing , hybrid genome assembly , whole genome sequencing , computational biology , massive parallel sequencing , reference genome , forward genetics , genomics , gene
Forward genetic mutational studies, adaptive evolution, and phenotypic screening are powerful tools for creating new variant organisms with desirable traits. However, mutations generated in the process cannot be easily identified with traditional genetic tools. We show that new high-throughput, massively parallel sequencing technologies can completely and accurately characterize a mutant genome relative to a previously sequenced parental (reference) strain. We studied a mutant strain of Pichia stipitis, a yeast capable of converting xylose to ethanol. This unusually efficient mutant strain was developed through repeated rounds of chemical mutagenesis, strain selection, transformation, and genetic manipulation over a period of seven years. We resequenced this strain on three different sequencing platforms. Surprisingly, we found fewer than a dozen mutations in open reading frames. All three sequencing technologies were able to identify each single nucleotide mutation given at least 10-15-fold nominal sequence coverage. Our results show that detecting mutations in evolved and engineered organisms is rapid and cost-effective at the whole-genome level using new sequencing technologies. Identification of specific mutations in strains with altered phenotypes will add insight into specific gene functions and guide further metabolic engineering efforts.
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