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EvalMSA: A Program to Evaluate Multiple Sequence Alignments and Detect Outliers

Overview of attention for article published in Evolutionary Bioinformatics, November 2016
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About this Attention Score

  • Among the highest-scoring outputs from this source (#38 of 216)
  • Good Attention Score compared to outputs of the same age (66th percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

Mentioned by

twitter
3 tweeters
facebook
1 Facebook page
googleplus
1 Google+ user

Readers on

mendeley
20 Mendeley
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Title
EvalMSA: A Program to Evaluate Multiple Sequence Alignments and Detect Outliers
Published in
Evolutionary Bioinformatics, November 2016
DOI 10.4137/ebo.s40583
Pubmed ID
Authors

Alvaro Chiner-Oms, Fernando González-Candelas

Abstract

We present EvalMSA, a software tool for evaluating and detecting outliers in multiple sequence alignments (MSAs). This tool allows the identification of divergent sequences in MSAs by scoring the contribution of each row in the alignment to its quality using a sum-of-pair-based method and additional analyses. Our main goal is to provide users with objective data in order to take informed decisions about the relevance and/or pertinence of including/retaining a particular sequence in an MSA. EvalMSA is written in standard Perl and also uses some routines from the statistical language R. Therefore, it is necessary to install the R-base package in order to get full functionality. Binary packages are freely available from http://sourceforge.net/projects/evalmsa/for Linux and Windows.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 20 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 25%
Researcher 4 20%
Unspecified 3 15%
Student > Bachelor 3 15%
Student > Doctoral Student 2 10%
Other 3 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 8 40%
Unspecified 6 30%
Computer Science 4 20%
Biochemistry, Genetics and Molecular Biology 2 10%

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 09 December 2016.
All research outputs
#3,393,611
of 12,017,156 outputs
Outputs from Evolutionary Bioinformatics
#38
of 216 outputs
Outputs of similar age
#109,183
of 329,129 outputs
Outputs of similar age from Evolutionary Bioinformatics
#2
of 18 outputs
Altmetric has tracked 12,017,156 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 216 research outputs from this source. They receive a mean Attention Score of 2.4. This one has done well, scoring higher than 81% 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 329,129 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 66% of its contemporaries.
We're also able to compare this research output to 18 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.