Title |
Assessing the benefits of using mate-pairs to resolve repeats in de novo short-read prokaryotic assemblies
|
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Published in |
BMC Bioinformatics, April 2011
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DOI | 10.1186/1471-2105-12-95 |
Pubmed ID | |
Authors |
Joshua Wetzel, Carl Kingsford, Mihai Pop |
Abstract |
Next-generation sequencing technologies allow genomes to be sequenced more quickly and less expensively than ever before. However, as sequencing technology has improved, the difficulty of de novo genome assembly has increased, due in large part to the shorter reads generated by the new technologies. The use of mated sequences (referred to as mate-pairs) is a standard means of disambiguating assemblies to obtain a more complete picture of the genome without resorting to manual finishing. Here, we examine the effectiveness of mate-pair information in resolving repeated sequences in the DNA (a paramount issue to overcome). While it has been empirically accepted that mate-pairs improve assemblies, and a variety of assemblers use mate-pairs in the context of repeat resolution, the effectiveness of mate-pairs in this context has not been systematically evaluated in previous literature. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 3 | 60% |
Netherlands | 1 | 20% |
Norway | 1 | 20% |
Demographic breakdown
Type | Count | As % |
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Scientists | 3 | 60% |
Members of the public | 2 | 40% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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United States | 9 | 5% |
United Kingdom | 6 | 3% |
Brazil | 5 | 3% |
Netherlands | 3 | 2% |
Italy | 2 | 1% |
Russia | 2 | 1% |
Germany | 2 | 1% |
Australia | 2 | 1% |
Austria | 2 | 1% |
Other | 11 | 6% |
Unknown | 152 | 78% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 58 | 30% |
Researcher | 54 | 28% |
Student > Master | 16 | 8% |
Student > Doctoral Student | 9 | 5% |
Student > Bachelor | 9 | 5% |
Other | 31 | 16% |
Unknown | 19 | 10% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 108 | 55% |
Computer Science | 23 | 12% |
Biochemistry, Genetics and Molecular Biology | 22 | 11% |
Medicine and Dentistry | 5 | 3% |
Engineering | 4 | 2% |
Other | 12 | 6% |
Unknown | 22 | 11% |