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A systematic approach to RNA-associated motif discovery

Overview of attention for article published in BMC Genomics, February 2018
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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 (71st percentile)

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Title
A systematic approach to RNA-associated motif discovery
Published in
BMC Genomics, February 2018
DOI 10.1186/s12864-018-4528-x
Pubmed ID
Authors

Tian Gao, Jiang Shu, Juan Cui

Abstract

Sequencing-based large screening of RNA-protein and RNA-RNA interactions has enabled the mechanistic study of post-transcriptional RNA processing and sorting, including exosome-mediated RNA secretion. The downstream analysis of RNA binding sites has encouraged the investigation of novel sequence motifs, which resulted in exceptional new challenges for identifying motifs from very short sequences (e.g., small non-coding RNAs or truncated messenger RNAs), where conventional methods tend to be ineffective. To address these challenges, we propose a novel motif-finding method and validate it on a wide range of RNA applications. We first perform motif analysis on microRNAs and longer RNA fragments from various cellular and exosomal sources, and then validate our prediction through literature search and experimental test. For example, a 4 bp-long motif, GUUG, was detected to be responsible for microRNA loading in exosomes involved in human colon cancer (SW620). Additional performance comparisons in various case studies have shown that this new approach outperforms several existing state-of-the-art methods in detecting motifs with exceptional high coverage and explicitness. In this work, we have demonstrated the promising performance of a new motif discovery approach that is particularly effective in current RNA applications. Important discoveries resulting from this work include the identification of possible RNA-loading motifs in a variety of exosomes, as well as novel insights in sequence features of RNA cargos, i.e., short non-coding RNAs and messenger RNAs may share similar loading mechanism into exosomes. This method has been implemented and deployed as a new webserver named MDS2which is accessible at http://sbbi-panda.unl.edu/MDS2/ , along with a standalone package available for download at https://github.com/sbbi/MDS2 .

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 61 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 61 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 20%
Student > Master 12 20%
Researcher 9 15%
Student > Bachelor 8 13%
Student > Doctoral Student 2 3%
Other 5 8%
Unknown 13 21%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 25 41%
Agricultural and Biological Sciences 11 18%
Engineering 4 7%
Medicine and Dentistry 2 3%
Pharmacology, Toxicology and Pharmaceutical Science 1 2%
Other 4 7%
Unknown 14 23%
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 01 May 2018.
All research outputs
#6,453,639
of 23,577,761 outputs
Outputs from BMC Genomics
#2,757
of 10,800 outputs
Outputs of similar age
#133,336
of 449,019 outputs
Outputs of similar age from BMC Genomics
#57
of 201 outputs
Altmetric has tracked 23,577,761 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 10,800 research outputs from this source. They receive a mean Attention Score of 4.7. This one has gotten more attention than average, scoring higher than 74% 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 449,019 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 201 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 71% of its contemporaries.