Tweet
Interesting paper out in Genome Research comparing resequencing performance using the three leading platforms.
Rapid whole-genome mutational profiling using next-generation sequencing technologies
Douglas R. Smith1,7,9, Aaron R. Quinlan2,7, Heather E. Peckham3,7, Kathryn Makowsky1, Wei Tao1, Betty Woolf1, Lei Shen1, William F. Donahue1, Nadeem Tusneem1, Michael P. Stromberg2, Donald A. Stewart2, Lu Zhang2, Swati S. Ranade3, Jason B. Warner3, Clarence C. Lee3, Brittney E. Coleman3, Zheng Zhang3,4, Stephen F. McLaughlin3, Joel A. Malek3, Jon M. Sorenson3,4, Alan P. Blanchard3, Jarrod Chapman5, David Hillman5, Feng Chen5, Daniel S. Rokhsar5, Kevin J. McKernan3, Thomas W. Jeffries6, Gabor T. Marth2,9, and Paul M. Richardson5,8,9
1 Agencourt Bioscience Corporation, Beverly, Massachusetts 01915, USA; 2 Boston College Biology Department, Higgins Hall, Chestnut Hill, Massachusetts 02467, USA; 3 Applied Biosystems, Beverly, Massachusetts 01915, USA; 4 Appplied Biosystems, Foster City, California 94404, USA; 5 US Department of Energy Joint Genome Institute, Walnut Creek, California 94598, USA; 6 Institute for Microbial and Biochemical Technology, US Forest Products Laboratory, Madison, Wisconsin 53726, USA
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.
Douglas R. Smith1,7,9, Aaron R. Quinlan2,7, Heather E. Peckham3,7, Kathryn Makowsky1, Wei Tao1, Betty Woolf1, Lei Shen1, William F. Donahue1, Nadeem Tusneem1, Michael P. Stromberg2, Donald A. Stewart2, Lu Zhang2, Swati S. Ranade3, Jason B. Warner3, Clarence C. Lee3, Brittney E. Coleman3, Zheng Zhang3,4, Stephen F. McLaughlin3, Joel A. Malek3, Jon M. Sorenson3,4, Alan P. Blanchard3, Jarrod Chapman5, David Hillman5, Feng Chen5, Daniel S. Rokhsar5, Kevin J. McKernan3, Thomas W. Jeffries6, Gabor T. Marth2,9, and Paul M. Richardson5,8,9
1 Agencourt Bioscience Corporation, Beverly, Massachusetts 01915, USA; 2 Boston College Biology Department, Higgins Hall, Chestnut Hill, Massachusetts 02467, USA; 3 Applied Biosystems, Beverly, Massachusetts 01915, USA; 4 Appplied Biosystems, Foster City, California 94404, USA; 5 US Department of Energy Joint Genome Institute, Walnut Creek, California 94598, USA; 6 Institute for Microbial and Biochemical Technology, US Forest Products Laboratory, Madison, Wisconsin 53726, USA
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.
Comment