Evidence map›Paper›PMID 25886980›Full record

ArticleBioinformatics (Oxford, England)2015

StructureFold: genome-wide RNA secondary structure mapping and reconstruction in vivo.

Yin Tang, Emil Bouvier, Chun Kit Kwok, Yiliang Ding, Anton Nekrutenko, Philip C Bevilacqua, Sarah M Assmann

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

26 citing papers in PubMed.

  1. In vivo structure probing of RNA inRNA (New York, N.Y.) · 2023
    Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Technique Development for Probing RNA Structure In Vivo and Genome-Wide.Cold Spring Harbor perspectives in biology · 2018
    Review
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
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  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Yin TangDepartment of Biology, Center for RNA Molecular Biology, Bioinformatics and Genomics Graduate Program.
Emil BouvierDepartment of Biochemistry and Molecular Biology, Penn State University, University Park, PA 16802, USA, Galaxyproject.org, University Park, PA 16802, USA and Baltimore, MD 21218, USA.
Chun Kit KwokCenter for RNA Molecular Biology, Department of Chemistry and.
Yiliang DingDepartment of Biology, Center for RNA Molecular Biology, Department of Chemistry and.
Anton NekrutenkoBioinformatics and Genomics Graduate Program, Department of Biochemistry and Molecular Biology, Penn State University, University Park, PA 16802, USA, Galaxyproject.org, University Park, PA 16802, USA and Baltimore, MD 21218, USA.
Philip C BevilacquaCenter for RNA Molecular Biology, Department of Chemistry and Plant Biology Graduate Program, Pennsylvania State University, University Park, Pennsylvania 16802, USA.
Sarah M AssmannDepartment of Biology, Center for RNA Molecular Biology, Bioinformatics and Genomics Graduate Program, Plant Biology Graduate Program, Pennsylvania State University, University Park, Pennsylvania 16802, USA.

Funding

An Efficient Lightweight Environment for Biomedical ComputationR01HG004909 · NHGRI · PENNSYLVANIA STATE UNIVERSITY, THE · PI NEKRUTENKO, ANTON · 2009 to 2011
$1.3M
NHGRI NIH HHS R01 HG004909
6 · The paper itself

Abstract

motivationRNAs fold into complex structures that are integral to the diverse mechanisms underlying RNA regulation of gene expression. Recent development of transcriptome-wide RNA structure profiling through the application of structure-probing enzymes or chemicals combined with high-throughput sequencing has opened a new field that greatly expands the amount of in vitro and in vivo RNA structural information available. The resultant datasets provide the opportunity to investigate RNA structural information on a global scale. However, the analysis of high-throughput RNA structure profiling data requires considerable computational effort and expertise.

resultsWe present a new platform, StructureFold, that provides an integrated computational solution designed specifically for large-scale RNA structure mapping and reconstruction across any transcriptome. StructureFold automates the processing and analysis of raw high-throughput RNA structure profiling data, allowing the seamless incorporation of wet-bench structural information from chemical probes and/or ribonucleases to restrain RNA secondary structure prediction via the RNAstructure and ViennaRNA package algorithms. StructureFold performs reads mapping and alignment, normalization and reactivity derivation, and RNA structure prediction in a single user-friendly web interface or via local installation. The variation in transcript abundance and length that prevails in living cells and consequently causes variation in the counts of structure-probing events between transcripts is accounted for. Accordingly, StructureFold is applicable to RNA structural profiling data obtained in vivo as well as to in vitro or in silico datasets. StructureFold is deployed via the Galaxy platform. AVAILABILITY AND IMPLEMENTATION: StructureFold is freely available as a component of Galaxy available at: https://usegalaxy.org/. CONTACT: yxt148@psu.edu or sma3@psu.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Indexed as

AlgorithmsNucleic Acid ConformationArabidopsisComputational BiologyGenome, PlantRegulatory Sequences, Ribonucleic AcidRNARNA, PlantRNA, Ribosomal, 18SSequence Analysis, RNATranscriptomeRegulatory Sequences, Ribonucleic AcidRNARNA, PlantRNA, Ribosomal, 18S

Identifiers

PMID25886980
PMCPMC6280868

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.