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Reconstruction of ancestral gene orders using intermediate genomes

Overview of attention for article published in BMC Bioinformatics, October 2015
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Title
Reconstruction of ancestral gene orders using intermediate genomes
Published in
BMC Bioinformatics, October 2015
DOI 10.1186/1471-2105-16-s14-s3
Pubmed ID
Authors

Pedro Feijão

Abstract

The problem of reconstructing ancestral genomes in a given phylogenetic tree arises in many different comparative genomics fields. Here, we focus on reconstructing the gene order of ancestral genomes, a problem that has been largely studied in the past 20 years, especially with the increasing availability of whole genome DNA sequences. There are two main approaches to this problem: event-based methods, that try to find the ancestral genomes that minimize the number of rearrangement events in the tree; and homology-based, that look for conserved structures, such as adjacent genes in the extant genomes, to build the ancestral genomes. We propose algorithms that use the concept of intermediate genomes, arising in optimal pairwise rearrangement scenarios. We show that intermediate genomes have combinatorial properties that make them easy to reconstruct, and develop fast algorithms with better reconstructed ancestral genomes than current event-based methods. The proposed framework is also designed to accept extra information, such as results from homology-based approaches, giving rise to combined algorithms with better results than the original methods.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Finland 1 6%
Germany 1 6%
Unknown 14 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 31%
Student > Bachelor 3 19%
Student > Master 2 13%
Researcher 2 13%
Professor 1 6%
Other 2 13%
Unknown 1 6%
Readers by discipline Count As %
Computer Science 7 44%
Agricultural and Biological Sciences 4 25%
Biochemistry, Genetics and Molecular Biology 3 19%
Earth and Planetary Sciences 1 6%
Unknown 1 6%
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 15 October 2015.
All research outputs
#18,429,163
of 22,830,751 outputs
Outputs from BMC Bioinformatics
#6,320
of 7,287 outputs
Outputs of similar age
#198,033
of 275,399 outputs
Outputs of similar age from BMC Bioinformatics
#126
of 143 outputs
Altmetric has tracked 22,830,751 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,287 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 5th percentile – i.e., 5% 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 275,399 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 143 others from the same source and published within six weeks on either side of this one. This one is in the 5th percentile – i.e., 5% of its contemporaries scored the same or lower than it.