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Algorithms for reconstruction of chromosomal structures

Overview of attention for article published in BMC Bioinformatics, January 2016
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Title
Algorithms for reconstruction of chromosomal structures
Published in
BMC Bioinformatics, January 2016
DOI 10.1186/s12859-016-0878-z
Pubmed ID
Authors

Vassily Lyubetsky, Roman Gershgorin, Alexander Seliverstov, Konstantin Gorbunov

Abstract

One of the main aims of phylogenomics is the reconstruction of objects defined in the leaves along the whole phylogenetic tree to minimize the specified functional, which may also include the phylogenetic tree generation. Such objects can include nucleotide and amino acid sequences, chromosomal structures, etc. The structures can have any set of linear and circular chromosomes, variable gene composition and include any number of paralogs, as well as any weights of individual evolutionary operations to transform a chromosome structure. Many heuristic algorithms were proposed for this purpose, but there are just a few exact algorithms with low (linear, cubic or similar) polynomial computational complexity among them to our knowledge. The algorithms naturally start from the calculation of both the distance between two structures and the shortest sequence of operations transforming one structure into another. Such calculation per se is an NP-hard problem. A general model of chromosomal structure rearrangements is considered. Exact algorithms with almost linear or cubic polynomial complexities have been developed to solve the problems for the case of any chromosomal structure but with certain limitations on operation weights. The computer programs are tested on biological data for the problem of mitochondrial or plastid chromosomal structure reconstruction. To our knowledge, no computer programs are available for this model. Exactness of the proposed algorithms and such low polynomial complexities were proved. The reconstructed evolutionary trees of mitochondrial and plastid chromosomal structures as well as the ancestral states of the structures appear to be reasonable.

Twitter Demographics

The data shown below were collected from the profiles of 4 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 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 12 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 42%
Researcher 3 25%
Professor > Associate Professor 1 8%
Student > Master 1 8%
Unknown 2 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 5 42%
Agricultural and Biological Sciences 3 25%
Computer Science 2 17%
Unknown 2 17%

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 24 January 2016.
All research outputs
#7,020,453
of 11,293,566 outputs
Outputs from BMC Bioinformatics
#2,913
of 4,195 outputs
Outputs of similar age
#179,490
of 340,744 outputs
Outputs of similar age from BMC Bioinformatics
#104
of 144 outputs
Altmetric has tracked 11,293,566 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,195 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 21st percentile – i.e., 21% of its peers scored the same or lower than it.
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We're also able to compare this research output to 144 others from the same source and published within six weeks on either side of this one. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.