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Identification of positive selection in genes is greatly improved by using experimentally informed site-specific models

Overview of attention for article published in Biology Direct, January 2017
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  • Good Attention Score compared to outputs of the same age (68th percentile)

Mentioned by

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6 tweeters

Citations

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51 Dimensions

Readers on

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44 Mendeley
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Title
Identification of positive selection in genes is greatly improved by using experimentally informed site-specific models
Published in
Biology Direct, January 2017
DOI 10.1186/s13062-016-0172-z
Pubmed ID
Authors

Jesse D. Bloom

Abstract

Sites of positive selection are identified by comparing observed evolutionary patterns to those expected under a null model for evolution in the absence of such selection. For protein-coding genes, the most common null model is that nonsynonymous and synonymous mutations fix at equal rates; this unrealistic model has limited power to detect many interesting forms of selection. I describe a new approach that uses a null model based on experimental measurements of a gene's site-specific amino-acid preferences generated by deep mutational scanning in the lab. This null model makes it possible to identify both diversifying selection for repeated amino-acid change and differential selection for mutations to amino acids that are unexpected given the measurements made in the lab. I show that this approach identifies sites of adaptive substitutions in four genes (lactamase, Gal4, influenza nucleoprotein, and influenza hemagglutinin) far better than a comparable method that simply compares the rates of nonsynonymous and synonymous substitutions. As rapid increases in biological data enable increasingly nuanced descriptions of the constraints on individual protein sites, approaches like the one here can improve our ability to identify many interesting forms of selection in natural sequences. This article was reviewed by Sebastian Maurer-Stroh, Olivier Tenaillon, and Tal Pupko. All three reviewers are members of the Biology Direct editorial board.

Twitter Demographics

The data shown below were collected from the profiles of 6 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 44 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 44 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 34%
Student > Master 7 16%
Researcher 6 14%
Student > Bachelor 4 9%
Professor 3 7%
Other 5 11%
Unknown 4 9%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 19 43%
Agricultural and Biological Sciences 14 32%
Computer Science 2 5%
Environmental Science 1 2%
Business, Management and Accounting 1 2%
Other 1 2%
Unknown 6 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 04 July 2017.
All research outputs
#2,939,177
of 11,424,220 outputs
Outputs from Biology Direct
#193
of 546 outputs
Outputs of similar age
#99,912
of 320,587 outputs
Outputs of similar age from Biology Direct
#6
of 6 outputs
Altmetric has tracked 11,424,220 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 546 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.3. This one has gotten more attention than average, scoring higher than 64% 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 320,587 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 68% of its contemporaries.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one.