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LocARNAscan: Incorporating thermodynamic stability in sequence and structure-based RNA homology search

Overview of attention for article published in Algorithms for Molecular Biology, April 2013
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1 tweeter
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Citations

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33 Mendeley
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Title
LocARNAscan: Incorporating thermodynamic stability in sequence and structure-based RNA homology search
Published in
Algorithms for Molecular Biology, April 2013
DOI 10.1186/1748-7188-8-14
Pubmed ID
Authors

Sebastian Will, Michael F Siebauer, Steffen Heyne, Jan Engelhardt, Peter F Stadler, Kristin Reiche, Rolf Backofen

Abstract

The search for distant homologs has become an import issue in genome annotation. A particular difficulty is posed by divergent homologs that have lost recognizable sequence similarity. This same problem also arises in the recognition of novel members of large classes of RNAs such as snoRNAs or microRNAs that consist of families unrelated by common descent. Current homology search tools for structured RNAs are either based entirely on sequence similarity (such as blast or hmmer) or combine sequence and secondary structure. The most prominent example of the latter class of tools is Infernal. Alternatives are descriptor-based methods. In most practical applications published to-date, however, the information contained in covariance models or manually prescribed search patterns is dominated by sequence information. Here we ask two related questions: (1) Is secondary structure alone informative for homology search and the detection of novel members of RNA classes? (2) To what extent is the thermodynamic propensity of the target sequence to fold into the correct secondary structure helpful for this task?

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Denmark 2 6%
France 1 3%
Unknown 30 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 39%
Researcher 6 18%
Professor > Associate Professor 3 9%
Professor 2 6%
Student > Bachelor 2 6%
Other 3 9%
Unknown 4 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 13 39%
Computer Science 7 21%
Biochemistry, Genetics and Molecular Biology 5 15%
Immunology and Microbiology 2 6%
Engineering 1 3%
Other 0 0%
Unknown 5 15%

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 27 June 2013.
All research outputs
#9,036,312
of 11,293,566 outputs
Outputs from Algorithms for Molecular Biology
#117
of 177 outputs
Outputs of similar age
#88,212
of 128,470 outputs
Outputs of similar age from Algorithms for Molecular Biology
#4
of 5 outputs
Altmetric has tracked 11,293,566 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 177 research outputs from this source. They receive a mean Attention Score of 2.8. This one is in the 18th percentile – i.e., 18% of its peers scored the same or lower than it.
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