Evidence map›Paper›PMID 36705896›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2023

Linear-Time Algorithms for RNA Structure Prediction.

He Zhang, Liang Zhang, Kaibo Liu, Sizhen Li, David H Mathews, Liang Huang

Abstract read
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In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
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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.

He ZhangBaidu Research USA, Sunnyvale, CA, USA.
Liang ZhangBaidu Research USA, Sunnyvale, CA, USA.
Kaibo LiuBaidu Research USA, Sunnyvale, CA, USA.
Sizhen LiSchool of Electrical Engineering & Computer Science, Oregon State University, Corvallis, OR, USA.
David H MathewsDept. of Biochemistry & Biophysics, Center for RNA Biology, Rochester, NY, USA.
Liang HuangSchool of Electrical Engineering & Computer Science, Oregon State University, Corvallis, OR, USA.

Funding

RNA Structure Modeling Using Physics and Sequence ComparisonR35GM145283 · NIGMS · UNIVERSITY OF ROCHESTER · PI DAVID H. MATHEWS · 2022 to 2026
$2.7M
NIGMS NIH HHS R35 GM145283
6 · The paper itself

Abstract

RNA secondary structure prediction is widely used to understand RNA function. Existing dynamic programming-based algorithms, both the classical minimum free energy (MFE) methods and partition function methods, suffer from a major limitation: their runtimes scale cubically with the RNA length, and this slowness limits their use in genome-wide applications. Inspired by incremental parsing for context-free grammars in computational linguistics, we designed linear-time heuristic algorithms, LinearFold and LinearPartition, to approximate the MFE structure, partition function and base pairing probabilities. These programs are orders of magnitude faster than Vienna RNAfold and CONTRAfold on long sequences. More interestingly, LinearFold and LinearPartition lead to more accurate predictions on the longest sequence families for which the structures are well established (16S and 23S Ribosomal RNAs), as well as improved accuracies for long-range base pairs (500 +  nucleotides apart). This chapter provides protocols for using LinearFold and LinearPartition for secondary structure prediction.

Indexed as

AlgorithmsRNABase PairingComputational BiologyEntropyHumansNucleic Acid ConformationSequence Analysis, RNARNABeam search approximationLinear-time heuristicMaximum expected accuracy structureMinimum free energy structurePartition functionRNA secondary structure prediction

Identifiers

PMID36705896

What OpenQuestion holds

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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.