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Evaluation of Methods for De Novo Genome Assembly from High-Throughput Sequencing Reads Reveals Dependencies That Affect the Quality of the Results

Overview of attention for article published in PLOS ONE, September 2011
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Title
Evaluation of Methods for De Novo Genome Assembly from High-Throughput Sequencing Reads Reveals Dependencies That Affect the Quality of the Results
Published in
PLOS ONE, September 2011
DOI 10.1371/journal.pone.0024182
Pubmed ID
Authors

Niina Haiminen, David N. Kuhn, Laxmi Parida, Isidore Rigoutsos

Abstract

Recent developments in high-throughput sequencing technology have made low-cost sequencing an attractive approach for many genome analysis tasks. Increasing read lengths, improving quality and the production of increasingly larger numbers of usable sequences per instrument-run continue to make whole-genome assembly an appealing target application. In this paper we evaluate the feasibility of de novo genome assembly from short reads (≤100 nucleotides) through a detailed study involving genomic sequences of various lengths and origin, in conjunction with several of the currently popular assembly programs. Our extensive analysis demonstrates that, in addition to sequencing coverage, attributes such as the architecture of the target genome, the identity of the used assembly program, the average read length and the observed sequencing error rates are powerful variables that affect the best achievable assembly of the target sequence in terms of size and correctness.

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Mendeley readers

The data shown below were compiled from readership statistics for 197 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 10 5%
Brazil 4 2%
France 4 2%
Netherlands 3 2%
Norway 2 1%
Italy 1 <1%
Cuba 1 <1%
Chile 1 <1%
Sweden 1 <1%
Other 7 4%
Unknown 163 83%

Demographic breakdown

Readers by professional status Count As %
Researcher 67 34%
Student > Ph. D. Student 47 24%
Student > Master 20 10%
Student > Postgraduate 10 5%
Student > Bachelor 9 5%
Other 33 17%
Unknown 11 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 125 63%
Biochemistry, Genetics and Molecular Biology 30 15%
Computer Science 11 6%
Medicine and Dentistry 4 2%
Environmental Science 3 2%
Other 10 5%
Unknown 14 7%