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Assessing the benefits of using mate-pairs to resolve repeats in de novo short-read prokaryotic assemblies

Overview of attention for article published in BMC Bioinformatics, April 2011
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Title
Assessing the benefits of using mate-pairs to resolve repeats in de novo short-read prokaryotic assemblies
Published in
BMC Bioinformatics, April 2011
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.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 196 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
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%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 07 April 2023.
All research outputs
#13,918,542
of 24,274,366 outputs
Outputs from BMC Bioinformatics
#3,904
of 7,510 outputs
Outputs of similar age
#82,501
of 112,430 outputs
Outputs of similar age from BMC Bioinformatics
#44
of 64 outputs
Altmetric has tracked 24,274,366 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,510 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one is in the 45th percentile – i.e., 45% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 112,430 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 64 others from the same source and published within six weeks on either side of this one. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.