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Bioinformatics tools and databases for analysis of next-generation sequence data

Overview of attention for article published in Briefings in Functional Genomics, December 2011
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  • Good Attention Score compared to outputs of the same age (73rd percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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2 X users
patent
2 patents

Citations

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71 Dimensions

Readers on

mendeley
331 Mendeley
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18 CiteULike
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Title
Bioinformatics tools and databases for analysis of next-generation sequence data
Published in
Briefings in Functional Genomics, December 2011
DOI 10.1093/bfgp/elr037
Pubmed ID
Authors

Hong C. Lee, Kaitao Lai, Michał Tadeusz Lorenc, Michael Imelfort, Chris Duran, David Edwards

Abstract

Genome sequencing has been revolutionized by next-generation technologies, which can rapidly produce vast quantities of data at relatively low cost. With data production now no longer being limited, there is a huge challenge to analyse the data flood and interpret biological meaning. Bioinformatics scientists have risen to the challenge and a large number of software tools and databases have been produced and these continue to evolve with this rapidly advancing field. Here, we outline some of the tools and databases commonly used for the analysis of next-generation sequence data with comment on their utility.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 331 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 10 3%
Brazil 5 2%
Netherlands 3 <1%
Norway 2 <1%
France 2 <1%
Italy 2 <1%
Germany 2 <1%
Spain 2 <1%
United Kingdom 2 <1%
Other 8 2%
Unknown 293 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 79 24%
Researcher 70 21%
Student > Master 62 19%
Student > Bachelor 17 5%
Professor > Associate Professor 16 5%
Other 53 16%
Unknown 34 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 180 54%
Biochemistry, Genetics and Molecular Biology 45 14%
Computer Science 20 6%
Engineering 8 2%
Environmental Science 5 2%
Other 28 8%
Unknown 45 14%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 24 March 2020.
All research outputs
#6,749,220
of 22,661,413 outputs
Outputs from Briefings in Functional Genomics
#142
of 440 outputs
Outputs of similar age
#62,556
of 242,887 outputs
Outputs of similar age from Briefings in Functional Genomics
#5
of 9 outputs
Altmetric has tracked 22,661,413 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 440 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one has gotten more attention than average, scoring higher than 66% of its peers.
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 242,887 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than 4 of them.