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X Demographics
Mendeley readers
Attention Score in Context
| 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. |
X Demographics
The data shown below were collected from the profiles of 3 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 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 34 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 6 | 18% |
| Unspecified | 4 | 12% |
| Student > Ph. D. Student | 4 | 12% |
| Student > Doctoral Student | 3 | 9% |
| Student > Bachelor | 3 | 9% |
| Other | 6 | 18% |
| Unknown | 8 | 24% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 8 | 24% |
| Computer Science | 6 | 18% |
| Unspecified | 4 | 12% |
| Biochemistry, Genetics and Molecular Biology | 3 | 9% |
| Economics, Econometrics and Finance | 1 | 3% |
| Other | 0 | 0% |
| Unknown | 12 | 35% |
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
#8,476,767
of 25,377,790 outputs
Outputs from Evolutionary Bioinformatics
#92
of 408 outputs
Outputs of similar age
#141,252
of 417,122 outputs
Outputs of similar age from Evolutionary Bioinformatics
#2
of 7 outputs
Altmetric has tracked 25,377,790 research outputs across all sources so far. This one has received more attention than most of these and is in the 66th percentile.
So far Altmetric has tracked 408 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.9. This one has done well, scoring higher than 77% 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 417,122 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 65% of its contemporaries.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one. This one has scored higher than 5 of them.