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Computing all hybridization networks for multiple binary phylogenetic input trees

Overview of attention for article published in BMC Bioinformatics, July 2015
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Title
Computing all hybridization networks for multiple binary phylogenetic input trees
Published in
BMC Bioinformatics, July 2015
DOI 10.1186/s12859-015-0660-7
Pubmed ID
Authors

Benjamin Albrecht

Abstract

The computation of phylogenetic trees on the same set of species that are based on different orthologous genes can lead to incongruent trees. One possible explanation for this behavior are interspecific hybridization events recombining genes of different species. An important approach to analyze such events is the computation of hybridization networks. This work presents the first algorithm computing the hybridization number as well as a set of representative hybridization networks for multiple binary phylogenetic input trees on the same set of taxa. To improve its practical runtime, we show how this algorithm can be parallelized. Moreover, we demonstrate the efficiency of the software Hybroscale, containing an implementation of our algorithm, by comparing it to PIRNv2.0, which is so far the best available software computing the exact hybridization number for multiple binary phylogenetic trees on the same set of taxa. The algorithm is part of the software Hybroscale, which was developed specifically for the investigation of hybridization networks including their computation and visualization. Hybroscale is freely available(1) and runs on all three major operating systems. Our simulation study indicates that our approach is on average 100 times faster than PIRNv2.0. Moreover, we show how Hybroscale improves the interpretation of the reported hybridization networks by adding certain features to its graphical representation.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Portugal 1 8%
Unknown 12 92%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 4 31%
Student > Doctoral Student 2 15%
Researcher 2 15%
Student > Ph. D. Student 2 15%
Professor 1 8%
Other 2 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 8 62%
Computer Science 3 23%
Mathematics 1 8%
Unknown 1 8%
Attention Score in Context

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 30 January 2016.
All research outputs
#15,340,815
of 22,818,766 outputs
Outputs from BMC Bioinformatics
#5,374
of 7,284 outputs
Outputs of similar age
#153,726
of 263,145 outputs
Outputs of similar age from BMC Bioinformatics
#75
of 108 outputs
Altmetric has tracked 22,818,766 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,284 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 18th percentile – i.e., 18% 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 263,145 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 108 others from the same source and published within six weeks on either side of this one. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.