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Gene tree parsimony for incomplete gene trees: addressing true biological loss

Overview of attention for article published in Algorithms for Molecular Biology, January 2018
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  • Among the highest-scoring outputs from this source (#42 of 183)
  • Good Attention Score compared to outputs of the same age (69th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (60th percentile)

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Title
Gene tree parsimony for incomplete gene trees: addressing true biological loss
Published in
Algorithms for Molecular Biology, January 2018
DOI 10.1186/s13015-017-0120-1
Pubmed ID
Authors

Md Shamsuzzoha Bayzid, Tandy Warnow

Abstract

Species tree estimation from gene trees can be complicated by gene duplication and loss, and "gene tree parsimony" (GTP) is one approach for estimating species trees from multiple gene trees. In its standard formulation, the objective is to find a species tree that minimizes the total number of gene duplications and losses with respect to the input set of gene trees. Although much is known about GTP, little is known about how to treat inputs containing some incomplete gene trees (i.e., gene trees lacking one or more of the species). We present new theory for GTP considering whether the incompleteness is due to gene birth and death (i.e., true biological loss) or taxon sampling, and present dynamic programming algorithms that can be used for an exact but exponential time solution for small numbers of taxa, or as a heuristic for larger numbers of taxa. We also prove that the "standard" calculations for duplications and losses exactly solve GTP when incompleteness results from taxon sampling, although they can be incorrect when incompleteness results from true biological loss. The software for the DP algorithm is freely available as open source code at https://github.com/smirarab/DynaDup.

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Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 02 February 2018.
All research outputs
#3,109,751
of 12,452,101 outputs
Outputs from Algorithms for Molecular Biology
#42
of 183 outputs
Outputs of similar age
#101,894
of 338,798 outputs
Outputs of similar age from Algorithms for Molecular Biology
#2
of 5 outputs
Altmetric has tracked 12,452,101 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 183 research outputs from this source. They receive a mean Attention Score of 2.9. This one has done well, scoring higher than 77% 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 338,798 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 69% 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 3 of them.