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Gene-Boosted Assembly of a Novel Bacterial Genome from Very Short Reads

Overview of attention for article published in PLoS Computational Biology, September 2008
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Title
Gene-Boosted Assembly of a Novel Bacterial Genome from Very Short Reads
Published in
PLoS Computational Biology, September 2008
DOI 10.1371/journal.pcbi.1000186
Pubmed ID
Authors

Steven L. Salzberg, Daniel D. Sommer, Daniela Puiu, Vincent T. Lee

Abstract

Recent improvements in technology have made DNA sequencing dramatically faster and more efficient than ever before. The new technologies produce highly accurate sequences, but one drawback is that the most efficient technology produces the shortest read lengths. Short-read sequencing has been applied successfully to resequence the human genome and those of other species but not to whole-genome sequencing of novel organisms. Here we describe the sequencing and assembly of a novel clinical isolate of Pseudomonas aeruginosa, strain PAb1, using very short read technology. From 8,627,900 reads, each 33 nucleotides in length, we assembled the genome into one scaffold of 76 ordered contiguous sequences containing 6,290,005 nucleotides, including one contig spanning 512,638 nucleotides, plus an additional 436 unordered contigs containing 416,897 nucleotides. Our method includes a novel gene-boosting algorithm that uses amino acid sequences from predicted proteins to build a better assembly. This study demonstrates the feasibility of very short read sequencing for the sequencing of bacterial genomes, particularly those for which a related species has been sequenced previously, and expands the potential application of this new technology to most known prokaryotic species.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 161 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 16 10%
United Kingdom 4 2%
Brazil 4 2%
France 2 1%
Argentina 2 1%
Spain 2 1%
Belgium 2 1%
Hong Kong 1 <1%
Sweden 1 <1%
Other 8 5%
Unknown 119 74%

Demographic breakdown

Readers by professional status Count As %
Researcher 59 37%
Student > Ph. D. Student 35 22%
Professor 12 7%
Professor > Associate Professor 12 7%
Student > Master 11 7%
Other 21 13%
Unknown 11 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 105 65%
Computer Science 19 12%
Biochemistry, Genetics and Molecular Biology 11 7%
Environmental Science 3 2%
Mathematics 3 2%
Other 5 3%
Unknown 15 9%