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Locus-aware decomposition of gene trees with respect to polytomous species trees

Overview of attention for article published in Algorithms for Molecular Biology, June 2018
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Title
Locus-aware decomposition of gene trees with respect to polytomous species trees
Published in
Algorithms for Molecular Biology, June 2018
DOI 10.1186/s13015-018-0128-1
Pubmed ID
Authors

Michał Aleksander Ciach, Anna Muszewska, Paweł Górecki

Abstract

Horizontal gene transfer (HGT), a process of acquisition and fixation of foreign genetic material, is an important biological phenomenon. Several approaches to HGT inference have been proposed. However, most of them either rely on approximate, non-phylogenetic methods or on the tree reconciliation, which is computationally intensive and sensitive to parameter values. We investigate the locus tree inference problem as a possible alternative that combines the advantages of both approaches. We present several algorithms to solve the problem in the parsimony framework. We introduce a novel tree mapping, which allows us to obtain a heuristic solution to the problems of locus tree inference and duplication classification. Our approach allows for faster comparisons of gene and species trees and improves known algorithms for duplication inference in the presence of polytomies in the species trees. We have implemented our algorithms in a software tool available at https://github.com/mciach/LocusTreeInference.

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 6 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 67%
Professor > Associate Professor 1 17%
Other 1 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 2 33%
Computer Science 1 17%
Nursing and Health Professions 1 17%
Unknown 2 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 June 2018.
All research outputs
#13,900,658
of 23,577,761 outputs
Outputs from Algorithms for Molecular Biology
#101
of 251 outputs
Outputs of similar age
#171,938
of 331,027 outputs
Outputs of similar age from Algorithms for Molecular Biology
#3
of 4 outputs
Altmetric has tracked 23,577,761 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 251 research outputs from this source. They receive a mean Attention Score of 3.3. This one has gotten more attention than average, scoring higher than 56% 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 331,027 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.
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.