Evidence map›Paper›PMID 26585404›Full record

ReviewJournal of molecular biology2016

Progress and Current Challenges in Modeling Large RNAs.

Srinivas Somarowthu

Abstract readReview
In one paragraph

Review in Journal of molecular biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
1.5field-weighted citation impact, top 17% of its field
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

16 citing papers in PubMed, 26 citations in OpenAlex.

  1. Article
  2. Review
  3. Recent advances in RNA structurome.Science China. Life sciences · 2022
    Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Review
  14. Review
  15. Function by Structure: Spotlights on Xist Long Non-coding RNA.Frontiers in molecular biosciences · 2017
    Review
  16. Challenges in RNA Structural Modeling and Design.Journal of molecular biology · 2016
    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

1 author at 1 institution in 1 country.

Srinivas SomarowthuDepartment of Molecular, Cellular and Developmental Biology, Yale University, 219 Prospect Street, Kline Biology Tower, New Haven, CT 06511, USA. Electronic address: srinivas.somarowthu@yale.edu.
Yale University · US

Funding

STUCTURE AND FUNCTION OF GROUP II INTRONSR01GM050313 · NIGMS · YALE UNIVERSITY · PI PYLE, ANNA MARIE · 1994 to 2016
$4.6M
NIGMS NIH HHS R01 GM050313NIGMS NIH HHS R01GM50313
6 · The paper itself

Abstract

Recent breakthroughs in next-generation sequencing technologies have led to the discovery of several classes of non-coding RNAs (ncRNAs). It is now apparent that RNA molecules are not only just carriers of genetic information but also key players in many cellular processes. While there has been a rapid increase in the number of ncRNA sequences deposited in various databases over the past decade, the biological functions of these ncRNAs are largely not well understood. Similar to proteins, RNA molecules carry out a function by forming specific three-dimensional structures. Understanding the function of a particular RNA therefore requires a detailed knowledge of its structure. However, determining experimental structures of RNA is extremely challenging. In fact, RNA-only structures represent just 1% of the total structures deposited in the PDB. Thus, computational methods that predict three-dimensional RNA structures are in high demand. Computational models can provide valuable insights into structure-function relationships in ncRNAs and can aid in the development of functional hypotheses and experimental designs. In recent years, a set of diverse RNA structure prediction tools have become available, which differ in computational time, input data and accuracy. This review discusses the recent progress and challenges in RNA structure prediction methods.

Indexed as

Models, MolecularNucleic Acid ConformationComputational BiologyHumansRNA, UntranslatedRNA, Untranslatedgroup II intronshomology modelinglncRNAsRNA backbone refinementRNA structure prediction

Identifiers

PMID26585404
PMCPMC4789162
OpenAlexW2174630165

What OpenQuestion holds

Textmetadata
LicenceTDM
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.