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RNA folding with hard and soft constraints

Overview of attention for article published in Algorithms for Molecular Biology, April 2016
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Title
RNA folding with hard and soft constraints
Published in
Algorithms for Molecular Biology, April 2016
DOI 10.1186/s13015-016-0070-z
Pubmed ID
Authors

Ronny Lorenz, Ivo L. Hofacker, Peter F. Stadler

Abstract

A large class of RNA secondary structure prediction programs uses an elaborate energy model grounded in extensive thermodynamic measurements and exact dynamic programming algorithms. External experimental evidence can be in principle be incorporated by means of hard constraints that restrict the search space or by means of soft constraints that distort the energy model. In particular recent advances in coupling chemical and enzymatic probing with sequencing techniques but also comparative approaches provide an increasing amount of experimental data to be combined with secondary structure prediction. Responding to the increasing needs for a versatile and user-friendly inclusion of external evidence into diverse flavors of RNA secondary structure prediction tools we implemented a generic layer of constraint handling into the ViennaRNA Package. It makes explicit use of the conceptual separation of the "folding grammar" defining the search space and the actual energy evaluation, which allows constraints to be interleaved in a natural way between recursion steps and evaluation of the standard energy function. The extension of the ViennaRNA Package provides a generic way to include diverse types of constraints into RNA folding algorithms. The computational overhead incurred is negligible in practice. A wide variety of application scenarios can be accommodated by the new framework, including the incorporation of structure probing data, non-standard base pairs and chemical modifications, as well as structure-dependent ligand binding.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 101 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 2%
Denmark 1 <1%
Unknown 98 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 25 25%
Researcher 17 17%
Student > Master 15 15%
Student > Bachelor 10 10%
Professor 5 5%
Other 7 7%
Unknown 22 22%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 36 36%
Agricultural and Biological Sciences 21 21%
Computer Science 8 8%
Chemistry 4 4%
Engineering 3 3%
Other 7 7%
Unknown 22 22%
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 23 April 2016.
All research outputs
#20,322,106
of 22,865,319 outputs
Outputs from Algorithms for Molecular Biology
#233
of 264 outputs
Outputs of similar age
#253,435
of 299,155 outputs
Outputs of similar age from Algorithms for Molecular Biology
#12
of 12 outputs
Altmetric has tracked 22,865,319 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 264 research outputs from this source. They receive a mean Attention Score of 3.2. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 12 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.