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LMAP: Lightweight Multigene Analyses in PAML

Overview of attention for article published in BMC Bioinformatics, September 2016
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (66th percentile)
  • High Attention Score compared to outputs of the same age and source (89th percentile)

Mentioned by

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9 tweeters

Citations

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12 Dimensions

Readers on

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61 Mendeley
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Title
LMAP: Lightweight Multigene Analyses in PAML
Published in
BMC Bioinformatics, September 2016
DOI 10.1186/s12859-016-1204-5
Pubmed ID
Authors

Emanuel Maldonado, Daniela Almeida, Tibisay Escalona, Imran Khan, Vitor Vasconcelos, Agostinho Antunes

Abstract

Uncovering how phenotypic diversity arises and is maintained in nature has long been a major interest of evolutionary biologists. Recent advances in genome sequencing technologies have remarkably increased the efficiency to pinpoint genes involved in the adaptive evolution of phenotypes. Reliability of such findings is most often examined with statistical and computational methods using Maximum Likelihood codon-based models (i.e., site, branch, branch-site and clade models), such as those available in codeml from the Phylogenetic Analysis by Maximum Likelihood (PAML) package. While these models represent a well-defined workflow for documenting adaptive evolution, in practice they can be challenging for researchers having a vast amount of data, as multiple types of relevant codon-based datasets are generated, making the overall process hard and tedious to handle, error-prone and time-consuming. We introduce LMAP (Lightweight Multigene Analyses in PAML), a user-friendly command-line and interactive package, designed to handle the codeml workflow, namely: directory organization, execution, results gathering and organization for Likelihood Ratio Test estimations with minimal manual user intervention. LMAP was developed for the workstation multi-core environment and provides a unique advantage for processing one, or more, if not all codeml codon-based models for multiple datasets at a time. Our software, proved efficiency throughout the codeml workflow, including, but not limited, to simultaneously handling more than 20 datasets. We have developed a simple and versatile LMAP package, with outstanding performance, enabling researchers to analyze multiple different codon-based datasets in a high-throughput fashion. At minimum, two file types are required within a single input directory: one for the multiple sequence alignment and another for the phylogenetic tree. To our knowledge, no other software combines all codeml codon substitution models of adaptive evolution. LMAP has been developed as an open-source package, allowing its integration into more complex open-source bioinformatics pipelines. LMAP package is released under GPLv3 license and is freely available at http://lmapaml.sourceforge.net/ .

Twitter Demographics

The data shown below were collected from the profiles of 9 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 61 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 61 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 23%
Researcher 10 16%
Student > Master 10 16%
Student > Bachelor 6 10%
Professor 4 7%
Other 8 13%
Unknown 9 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 17 28%
Biochemistry, Genetics and Molecular Biology 12 20%
Computer Science 7 11%
Engineering 3 5%
Pharmacology, Toxicology and Pharmaceutical Science 2 3%
Other 9 15%
Unknown 11 18%

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 12 September 2016.
All research outputs
#5,408,601
of 17,800,904 outputs
Outputs from BMC Bioinformatics
#2,265
of 6,267 outputs
Outputs of similar age
#89,197
of 272,821 outputs
Outputs of similar age from BMC Bioinformatics
#4
of 28 outputs
Altmetric has tracked 17,800,904 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 6,267 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 62% 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 272,821 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 28 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.