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Split-inducing indels in phylogenomic analysis

Overview of attention for article published in Algorithms for Molecular Biology, July 2018
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#28 of 251)
  • Good Attention Score compared to outputs of the same age (76th percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

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11 X users
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1 Q&A thread

Citations

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31 Mendeley
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Title
Split-inducing indels in phylogenomic analysis
Published in
Algorithms for Molecular Biology, July 2018
DOI 10.1186/s13015-018-0130-7
Pubmed ID
Authors

Alexander Donath, Peter F. Stadler

Abstract

Most phylogenetic studies using molecular data treat gaps in multiple sequence alignments as missing data or even completely exclude alignment columns that contain gaps. Here we show that gap patterns in large-scale, genome-wide alignments are themselves phylogenetically informative and can be used to infer reliable phylogenies provided the gap data are properly filtered to reduce noise introduced by the alignment method. We introduce here the notion of split-inducing indels (splids) that define an approximate bipartition of the taxon set. We show both in simulated data and in case studies on real-life data that splids can be efficiently extracted from phylogenomic data sets. Suitably processed gap patterns extracted from genome-wide alignment provide a surprisingly clear phylogenetic signal and an allow the inference of accurate phylogenetic trees.

X Demographics

X Demographics

The data shown below were collected from the profiles of 11 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 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 31 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 26%
Researcher 5 16%
Student > Master 4 13%
Other 2 6%
Professor 2 6%
Other 4 13%
Unknown 6 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 9 29%
Computer Science 5 16%
Biochemistry, Genetics and Molecular Biology 4 13%
Unspecified 1 3%
Chemistry 1 3%
Other 0 0%
Unknown 11 35%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 15 April 2022.
All research outputs
#4,138,462
of 23,543,207 outputs
Outputs from Algorithms for Molecular Biology
#28
of 251 outputs
Outputs of similar age
#78,027
of 327,766 outputs
Outputs of similar age from Algorithms for Molecular Biology
#1
of 5 outputs
Altmetric has tracked 23,543,207 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 251 research outputs from this source. They receive a mean Attention Score of 3.3. This one has done well, scoring higher than 88% 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 327,766 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 76% of its contemporaries.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them