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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 (#36 of 199)
  • Good Attention Score compared to outputs of the same age (71st percentile)

Mentioned by

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13 tweeters

Citations

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6 Dimensions

Readers on

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18 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.

Twitter Demographics

The data shown below were collected from the profiles of 13 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 18 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 18 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 39%
Researcher 4 22%
Other 2 11%
Professor 1 6%
Student > Master 1 6%
Other 1 6%
Unknown 2 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 6 33%
Computer Science 4 22%
Biochemistry, Genetics and Molecular Biology 2 11%
Unknown 6 33%

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 27 July 2018.
All research outputs
#2,924,896
of 13,288,667 outputs
Outputs from Algorithms for Molecular Biology
#36
of 199 outputs
Outputs of similar age
#74,689
of 266,362 outputs
Outputs of similar age from Algorithms for Molecular Biology
#1
of 2 outputs
Altmetric has tracked 13,288,667 research outputs across all sources so far. Compared to these this one has done well and is in the 77th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 199 research outputs from this source. They receive a mean Attention Score of 3.0. This one has done well, scoring higher than 81% 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 266,362 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 71% of its contemporaries.
We're also able to compare this research output to 2 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