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RNA motif search with data-driven element ordering

Overview of attention for article published in BMC Bioinformatics, May 2016
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  • Good Attention Score compared to outputs of the same age (70th percentile)
  • Good Attention Score compared to outputs of the same age and source (65th percentile)

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4 X users
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1 Wikipedia page

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23 Mendeley
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Title
RNA motif search with data-driven element ordering
Published in
BMC Bioinformatics, May 2016
DOI 10.1186/s12859-016-1074-x
Pubmed ID
Authors

Ladislav Rampášek, Randi M. Jimenez, Andrej Lupták, Tomáš Vinař, Broňa Brejová

Abstract

In this paper, we study the problem of RNA motif search in long genomic sequences. This approach uses a combination of sequence and structure constraints to uncover new distant homologs of known functional RNAs. The problem is NP-hard and is traditionally solved by backtracking algorithms. We have designed a new algorithm for RNA motif search and implemented a new motif search tool RNArobo. The tool enhances the RNAbob descriptor language, allowing insertions in helices, which enables better characterization of ribozymes and aptamers. A typical RNA motif consists of multiple elements and the running time of the algorithm is highly dependent on their ordering. By approaching the element ordering problem in a principled way, we demonstrate more than 100-fold speedup of the search for complex motifs compared to previously published tools. We have developed a new method for RNA motif search that allows for a significant speedup of the search of complex motifs that include pseudoknots. Such speed improvements are crucial at a time when the rate of DNA sequencing outpaces growth in computing. RNArobo is available at http://compbio.fmph.uniba.sk/rnarobo .

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X Demographics

The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 23 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 22%
Student > Master 4 17%
Student > Ph. D. Student 4 17%
Other 3 13%
Student > Doctoral Student 2 9%
Other 2 9%
Unknown 3 13%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 12 52%
Agricultural and Biological Sciences 2 9%
Computer Science 2 9%
Chemistry 2 9%
Neuroscience 1 4%
Other 1 4%
Unknown 3 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 09 January 2022.
All research outputs
#6,156,892
of 22,842,950 outputs
Outputs from BMC Bioinformatics
#2,324
of 7,289 outputs
Outputs of similar age
#97,451
of 334,137 outputs
Outputs of similar age from BMC Bioinformatics
#34
of 100 outputs
Altmetric has tracked 22,842,950 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 7,289 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 67% of its peers.
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 334,137 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.
We're also able to compare this research output to 100 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 65% of its contemporaries.