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Rapid Genome Mapping in Nanochannel Arrays for Highly Complete and Accurate De Novo Sequence Assembly of the Complex Aegilops tauschii Genome

Overview of attention for article published in PLOS ONE, February 2013
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Title
Rapid Genome Mapping in Nanochannel Arrays for Highly Complete and Accurate De Novo Sequence Assembly of the Complex Aegilops tauschii Genome
Published in
PLOS ONE, February 2013
DOI 10.1371/journal.pone.0055864
Pubmed ID
Authors

Alex R. Hastie, Lingli Dong, Alexis Smith, Jeff Finklestein, Ernest T. Lam, Naxin Huo, Han Cao, Pui-Yan Kwok, Karin R. Deal, Jan Dvorak, Ming-Cheng Luo, Yong Gu, Ming Xiao

Abstract

Next-generation sequencing (NGS) technologies have enabled high-throughput and low-cost generation of sequence data; however, de novo genome assembly remains a great challenge, particularly for large genomes. NGS short reads are often insufficient to create large contigs that span repeat sequences and to facilitate unambiguous assembly. Plant genomes are notorious for containing high quantities of repetitive elements, which combined with huge genome sizes, makes accurate assembly of these large and complex genomes intractable thus far. Using two-color genome mapping of tiling bacterial artificial chromosomes (BAC) clones on nanochannel arrays, we completed high-confidence assembly of a 2.1-Mb, highly repetitive region in the large and complex genome of Aegilops tauschii, the D-genome donor of hexaploid wheat (Triticum aestivum). Genome mapping is based on direct visualization of sequence motifs on single DNA molecules hundreds of kilobases in length. With the genome map as a scaffold, we anchored unplaced sequence contigs, validated the initial draft assembly, and resolved instances of misassembly, some involving contigs <2 kb long, to dramatically improve the assembly from 75% to 95% complete.

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Geographical breakdown

Country Count As %
United States 6 3%
Netherlands 2 1%
France 1 <1%
Paraguay 1 <1%
Germany 1 <1%
Argentina 1 <1%
United Kingdom 1 <1%
Spain 1 <1%
Belgium 1 <1%
Other 0 0%
Unknown 179 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 55 28%
Student > Ph. D. Student 52 27%
Student > Master 14 7%
Professor > Associate Professor 10 5%
Student > Doctoral Student 10 5%
Other 35 18%
Unknown 18 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 97 50%
Biochemistry, Genetics and Molecular Biology 23 12%
Engineering 12 6%
Computer Science 10 5%
Physics and Astronomy 8 4%
Other 18 9%
Unknown 26 13%