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GATC: a genetic algorithm for gene tree construction under the Duplication-Transfer-Loss model of evolution

Overview of attention for article published in BMC Genomics, May 2018
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Mentioned by

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4 tweeters

Citations

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1 Dimensions

Readers on

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22 Mendeley
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Title
GATC: a genetic algorithm for gene tree construction under the Duplication-Transfer-Loss model of evolution
Published in
BMC Genomics, May 2018
DOI 10.1186/s12864-018-4455-x
Pubmed ID
Authors

Emmanuel Noutahi, Nadia El-Mabrouk

Abstract

Several methods have been developed for the accurate reconstruction of gene trees. Some of them use reconciliation with a species tree to correct, a posteriori, errors in gene trees inferred from multiple sequence alignments. Unfortunately the best fit to sequence information can be lost during this process. We describe GATC, a new algorithm for reconstructing a binary gene tree with branch length. GATC returns optimal solutions according to a measure combining both tree likelihood (according to sequence evolution) and a reconciliation score under the Duplication-Transfer-Loss (DTL) model. It can either be used to construct a gene tree from scratch or to correct trees infered by existing reconstruction method, making it highly flexible to various input data types. The method is based on a genetic algorithm acting on a population of trees at each step. It substantially increases the efficiency of the phylogeny space exploration, reducing the risk of falling into local minima, at a reasonable computational time. We have applied GATC to a dataset of simulated cyanobacterial phylogenies, as well as to an empirical dataset of three reference gene families, and showed that it is able to improve gene tree reconstructions compared with current state-of-the-art algorithms. The proposed algorithm is able to accurately reconstruct gene trees and is highly suitable for the construction of reference trees. Our results also highlight the efficiency of multi-objective optimization algorithms for the gene tree reconstruction problem. GATC is available on Github at: https://github.com/UdeM-LBIT/GATC .

Twitter Demographics

The data shown below were collected from the profiles of 4 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 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 22 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 6 27%
Researcher 3 14%
Student > Bachelor 2 9%
Other 2 9%
Professor > Associate Professor 2 9%
Other 2 9%
Unknown 5 23%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 4 18%
Agricultural and Biological Sciences 4 18%
Immunology and Microbiology 2 9%
Computer Science 1 5%
Nursing and Health Professions 1 5%
Other 3 14%
Unknown 7 32%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 17 May 2018.
All research outputs
#8,129,536
of 12,959,714 outputs
Outputs from BMC Genomics
#4,801
of 7,610 outputs
Outputs of similar age
#162,607
of 270,115 outputs
Outputs of similar age from BMC Genomics
#9
of 20 outputs
Altmetric has tracked 12,959,714 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,610 research outputs from this source. They receive a mean Attention Score of 4.3. This one is in the 28th percentile – i.e., 28% of its peers scored the same or lower than it.
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 270,115 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 20 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 50% of its contemporaries.