Evidence map›Paper›PMID 27188311›Full record

ArticleBMC bioinformatics2016

A comprehensive database of high-throughput sequencing-based RNA secondary structure probing data (Structure Surfer).

Nathan D Berkowitz, Ian M Silverman, Daniel M Childress, Hilal Kazan, Li-San Wang, Brian D Gregory

Abstract read
In one paragraph

Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Genome-Wide RNA Secondary Structure Prediction.Methods in molecular biology (Clifton, N.J.) · 2023
    Article
  2. Article
  3. NPlant direct · 2020
    Article
  4. Human RNA cap1 methyltransferase CMTr1 cooperates with RNA helicase DHX15 to modify RNAs with highly structured 5' termini.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2018
    Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. 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

6 authors.

Nathan D BerkowitzDepartment of Biology, University of Pennsylvania, 433 S. University Ave., Philadelphia, PA, 19104, USA.
Ian M SilvermanDepartment of Biology, University of Pennsylvania, 433 S. University Ave., Philadelphia, PA, 19104, USA.
Daniel M ChildressDepartment of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Hilal KazanDepartment of Computer Engineering, Antalya International University, Antalya, Turkey.
Li-San WangGenomics and Computational Biology Graduate Group, Philadelphia, USA.
Brian D GregoryDepartment of Biology, University of Pennsylvania, 433 S. University Ave., Philadelphia, PA, 19104, USA. bdgregor@sas.upenn.edu.

Funding

THE NIA GENETICS OF ALZHEIMER'S DISEASE DATA STORAGE SITEU24AG041689 · NIA · UNIVERSITY OF PENNSYLVANIA · PI LI-SAN WANG · 2012 to 2026
$42.3M
PREDOCTORAL TRAINING PROGRAM IN GENETICST32GM008216 · NIGMS · UNIVERSITY OF PENNSYLVANIA · PI EPSTEIN, DOUGLAS J · 1987 to 2023
$8.3M
Computational genome-wide RNA profiling using next-generation sequencingR01GM099962 · NIGMS · UNIVERSITY OF PENNSYLVANIA · PI WANG, LI-SAN · 2012 to 2016
$1.5M
NIA NIH HHS U24 AG041689NIGMS NIH HHS R01 GM099962NIGMS NIH HHS T32 GM008216
6 · The paper itself

Abstract

backgroundRNA molecules fold into complex three-dimensional shapes, guided by the pattern of hydrogen bonding between nucleotides. This pattern of base pairing, known as RNA secondary structure, is critical to their cellular function. Recently several diverse methods have been developed to assay RNA secondary structure on a transcriptome-wide scale using high-throughput sequencing. Each approach has its own strengths and caveats, however there is no widely available tool for visualizing and comparing the results from these varied methods.

methodsTo address this, we have developed Structure Surfer, a database and visualization tool for inspecting RNA secondary structure in six transcriptome-wide data sets from human and mouse ( http://tesla.pcbi.upenn.edu/strucuturesurfer/ ). The data sets were generated using four different high-throughput sequencing based methods. Each one was analyzed with a scoring pipeline specific to its experimental design. Users of Structure Surfer have the ability to query individual loci as well as detect trends across multiple sites.

resultsHere, we describe the included data sets and their differences. We illustrate the database's function by examining known structural elements and we explore example use cases in which combined data is used to detect structural trends.

conclusionsIn total, Structure Surfer provides an easy-to-use database and visualization interface for allowing users to interrogate the currently available transcriptome-wide RNA secondary structure information for mammals.

Indexed as

Databases, FactualTranscriptomeAnimalsHigh-Throughput Nucleotide SequencingHumansMiceNucleic Acid ConformationRNASequence Analysis, RNARNA

Identifiers

PMID27188311
PMCPMC4869249

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.