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Transcriptome-Based Differentiation of Closely-Related Miscanthus Lines

Overview of attention for article published in PLOS ONE, January 2012
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Title
Transcriptome-Based Differentiation of Closely-Related Miscanthus Lines
Published in
PLOS ONE, January 2012
DOI 10.1371/journal.pone.0029850
Pubmed ID
Authors

Philippe Chouvarine, Amanda M. Cooksey, Fiona M. McCarthy, David A. Ray, Brian S. Baldwin, Shane C. Burgess, Daniel G. Peterson

Abstract

Distinguishing between individuals is critical to those conducting animal/plant breeding, food safety/quality research, diagnostic and clinical testing, and evolutionary biology studies. Classical genetic identification studies are based on marker polymorphisms, but polymorphism-based techniques are time and labor intensive and often cannot distinguish between closely related individuals. Illumina sequencing technologies provide the detailed sequence data required for rapid and efficient differentiation of related species, lines/cultivars, and individuals in a cost-effective manner. Here we describe the use of Illumina high-throughput exome sequencing, coupled with SNP mapping, as a rapid means of distinguishing between related cultivars of the lignocellulosic bioenergy crop giant miscanthus (Miscanthus × giganteus). We provide the first exome sequence database for Miscanthus species complete with Gene Ontology (GO) functional annotations.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 68 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 4%
Malaysia 1 1%
Netherlands 1 1%
Poland 1 1%
Unknown 62 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 21 31%
Student > Ph. D. Student 19 28%
Student > Master 7 10%
Professor > Associate Professor 3 4%
Student > Bachelor 2 3%
Other 7 10%
Unknown 9 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 42 62%
Chemistry 4 6%
Biochemistry, Genetics and Molecular Biology 3 4%
Social Sciences 3 4%
Computer Science 2 3%
Other 4 6%
Unknown 10 15%