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Large Scale Analyses and Visualization of Adaptive Amino Acid Changes Projects

Overview of attention for article published in Interdisciplinary Sciences: Computational Life Sciences, January 2018
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About this Attention Score

  • Among the highest-scoring outputs from this source (#11 of 145)
  • Above-average Attention Score compared to outputs of the same age (64th percentile)

Mentioned by

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6 tweeters

Readers on

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8 Mendeley
Title
Large Scale Analyses and Visualization of Adaptive Amino Acid Changes Projects
Published in
Interdisciplinary Sciences: Computational Life Sciences, January 2018
DOI 10.1007/s12539-018-0282-7
Pubmed ID
Authors

Noé Vázquez, Cristina P. Vieira, Bárbara S. R. Amorim, André Torres, Hugo López-Fernández, Florentino Fdez-Riverola, José L. R. Sousa, Miguel Reboiro-Jato, Jorge Vieira

Abstract

When changes at few amino acid sites are the target of selection, adaptive amino acid changes in protein sequences can be identified using maximum-likelihood methods based on models of codon substitution (such as codeml). Although such methods have been employed numerous times using a variety of different organisms, the time needed to collect the data and prepare the input files means that tens or hundreds of coding regions are usually analyzed. Nevertheless, the recent availability of flexible and easy to use computer applications that collect relevant data (such as BDBM) and infer positively selected amino acid sites (such as ADOPS), means that the entire process is easier and quicker than before. However, the lack of a batch option in ADOPS, here reported, still precludes the analysis of hundreds or thousands of sequence files. Given the interest and possibility of running such large-scale projects, we have also developed a database where ADOPS projects can be stored. Therefore, this study also presents the B+ database, which is both a data repository and a convenient interface that looks at the information contained in ADOPS projects without the need to download and unzip the corresponding ADOPS project file. The ADOPS projects available at B+ can also be downloaded, unzipped, and opened using the ADOPS graphical interface. The availability of such a database ensures results repeatability, promotes data reuse with significant savings on the time needed for preparing datasets, and effortlessly allows further exploration of the data contained in ADOPS projects.

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 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 8 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 2 25%
Other 2 25%
Student > Master 1 13%
Professor 1 13%
Student > Ph. D. Student 1 13%
Other 1 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 2 25%
Computer Science 2 25%
Medicine and Dentistry 2 25%
Unspecified 1 13%
Engineering 1 13%
Other 0 0%

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 28 August 2018.
All research outputs
#3,319,719
of 11,834,873 outputs
Outputs from Interdisciplinary Sciences: Computational Life Sciences
#11
of 145 outputs
Outputs of similar age
#111,293
of 313,771 outputs
Outputs of similar age from Interdisciplinary Sciences: Computational Life Sciences
#1
of 4 outputs
Altmetric has tracked 11,834,873 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 145 research outputs from this source. They receive a mean Attention Score of 2.2. This one has done particularly well, scoring higher than 92% 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 313,771 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 64% of its contemporaries.
We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them