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. 2008 Oct;18(10):1638-42.
doi: 10.1101/gr.077776.108. Epub 2008 Sep 4.

Rapid whole-genome mutational profiling using next-generation sequencing technologies

Douglas R Smith  1 , Aaron R Quinlan, Heather E Peckham, Kathryn Makowsky, Wei Tao, Betty Woolf, Lei Shen, William F Donahue, Nadeem Tusneem, Michael P Stromberg, Donald A Stewart, Lu Zhang, Swati S Ranade, Jason B Warner, Clarence C Lee, Brittney E Coleman, Zheng Zhang, Stephen F McLaughlin, Joel A Malek, Jon M Sorenson, Alan P Blanchard, Jarrod Chapman, David Hillman, Feng Chen, Daniel S Rokhsar, Kevin J McKernan, Thomas W Jeffries, Gabor T Marth, Paul M Richardson
Affiliations

Rapid whole-genome mutational profiling using next-generation sequencing technologies

Douglas R Smith et al. Genome Res. 2008 Oct.

Abstract

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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Figures

Figure 1.
Figure 1.
Distribution of genome sequence coverage. The distribution of sequence coverage across the unmasked portion of the genome is shown for each technology. Here we represent comparable mean coverage levels for Illumina (red line, 13.00× mean genome coverage), 454 FLX (blue line, 10.78× mean genome coverage), and Applied Biosystems SOLiD (black line, 10.00× mean genome coverage) technologies. For each, we compare the observed coverage distribution to the expected Poisson coverage distribution (dotted lines of the same color for each technology).
Figure 2.
Figure 2.
The effect of sequence coverage on mutation discovery accuracy. The total number of mutation discovery errors is shown for the three sequencing technologies at various levels of aligned sequence coverage. (Blue circles) 454 FLX; (red circles) Illumina; (black circles) Applied Biosystems SOLiD.
See this image and copyright information in PMC

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