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Reconstruction of ancestral RNA sequences under multiple structural constraints

Overview of attention for article published in BMC Genomics, November 2016
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Title
Reconstruction of ancestral RNA sequences under multiple structural constraints
Published in
BMC Genomics, November 2016
DOI 10.1186/s12864-016-3105-4
Pubmed ID
Authors

Olivier Tremblay-Savard, Vladimir Reinharz, Jérôme Waldispühl

Abstract

Secondary structures form the scaffold of multiple sequence alignment of non-coding RNA (ncRNA) families. An accurate reconstruction of ancestral ncRNAs must use this structural signal. However, the inference of ancestors of a single ncRNA family with a single consensus structure may bias the results towards sequences with high affinity to this structure, which are far from the true ancestors. In this paper, we introduce achARNement, a maximum parsimony approach that, given two alignments of homologous ncRNA families with consensus secondary structures and a phylogenetic tree, simultaneously calculates ancestral RNA sequences for these two families. We test our methodology on simulated data sets, and show that achARNement outperforms classical maximum parsimony approaches in terms of accuracy, but also reduces by several orders of magnitude the number of candidate sequences. To conclude this study, we apply our algorithms on the Glm clan and the FinP-traJ clan from the Rfam database. Our results show that our methods reconstruct small sets of high-quality candidate ancestors with better agreement to the two target structures than with classical approaches. Our program is freely available at: http://csb.cs.mcgill.ca/acharnement .

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Mendeley readers

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 3 25%
Researcher 2 17%
Student > Master 2 17%
Student > Bachelor 1 8%
Other 1 8%
Other 0 0%
Unknown 3 25%
Readers by discipline Count As %
Computer Science 3 25%
Biochemistry, Genetics and Molecular Biology 2 17%
Agricultural and Biological Sciences 1 8%
Social Sciences 1 8%
Medicine and Dentistry 1 8%
Other 0 0%
Unknown 4 33%
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 19 November 2016.
All research outputs
#19,042,835
of 24,282,284 outputs
Outputs from BMC Genomics
#7,930
of 10,937 outputs
Outputs of similar age
#227,012
of 315,244 outputs
Outputs of similar age from BMC Genomics
#138
of 222 outputs
Altmetric has tracked 24,282,284 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,937 research outputs from this source. They receive a mean Attention Score of 4.8. This one is in the 27th percentile – i.e., 27% of its peers scored the same or lower than it.
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We're also able to compare this research output to 222 others from the same source and published within six weeks on either side of this one. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.