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Attention Score in Context
Title |
Bioinformatics tools and databases for analysis of next-generation sequence data
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---|---|
Published in |
Briefings in Functional Genomics, December 2011
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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
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.
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 1 | 50% |
United Kingdom | 1 | 50% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 2 | 100% |
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
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.