↓ Skip to main content

Time-consistent reconciliation maps and forbidden time travel

Overview of attention for article published in Algorithms for Molecular Biology, February 2018
Altmetric Badge

Mentioned by

twitter
2 X users

Citations

dimensions_citation
13 Dimensions

Readers on

mendeley
13 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Time-consistent reconciliation maps and forbidden time travel
Published in
Algorithms for Molecular Biology, February 2018
DOI 10.1186/s13015-018-0121-8
Pubmed ID
Authors

Nikolai Nøjgaard, Manuela Geiß, Daniel Merkle, Peter F. Stadler, Nicolas Wieseke, Marc Hellmuth

Abstract

In the absence of horizontal gene transfer it is possible to reconstruct the history of gene families from empirically determined orthology relations, which are equivalent toevent-labeledgene trees. Knowledge of the event labels considerably simplifies the problem of reconciling a gene treeTwith a species treesS, relative to the reconciliation problem without prior knowledge of the event types. It is well-known that optimal reconciliations in the unlabeled case may violate time-consistency and thus are not biologically feasible. Here we investigate the mathematical structure of the event labeled reconciliation problem with horizontal transfer. We investigate the issue of time-consistency for the event-labeled version of the reconciliation problem, provide a convenient axiomatic framework, and derive a complete characterization of time-consistent reconciliations. This characterization depends on certain weak conditions on the event-labeled gene trees that reflect conditions under which evolutionary events are observable at least in principle. We give an [Formula: see text]-time algorithm to decide whether a time-consistent reconciliation map exists. It does not require the construction of explicit timing maps, but relies entirely on the comparably easy task of checking whether a small auxiliary graph is acyclic. The algorithms are implemented in C++ using the boost graph library and are freely available at https://github.com/Nojgaard/tc-recon. The combinatorial characterization of time consistency and thus biologically feasible reconciliation is an important step towards the inference of gene family histories with horizontal transfer from orthology data, i.e., without presupposed gene and species trees. The fast algorithm to decide time consistency is useful in a broader context because it constitutes an attractive component for all tools that address tree reconciliation problems.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 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 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 23%
Student > Bachelor 3 23%
Researcher 2 15%
Student > Doctoral Student 1 8%
Professor > Associate Professor 1 8%
Other 0 0%
Unknown 3 23%
Readers by discipline Count As %
Computer Science 4 31%
Engineering 3 23%
Mathematics 2 15%
Biochemistry, Genetics and Molecular Biology 1 8%
Unknown 3 23%
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 13 November 2018.
All research outputs
#18,345,702
of 23,577,761 outputs
Outputs from Algorithms for Molecular Biology
#169
of 251 outputs
Outputs of similar age
#311,225
of 440,040 outputs
Outputs of similar age from Algorithms for Molecular Biology
#8
of 8 outputs
Altmetric has tracked 23,577,761 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% 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 is in the 25th percentile – i.e., 25% 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 440,040 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one.