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Methods in molecular biology (Clifton, N.J.)
2023;2586 35-48.

Genome-Wide RNA Secondary Structure Prediction.

Risa Karakida Kawaguchi, Hisanori Kiryu
Author Information
  1. Risa Karakida Kawaguchi: Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA. rkawaguc@cshl.edu.
  2. Hisanori Kiryu: Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan.
PMID: 36705897 DOI: 10.1007/978-1-0716-2768-6_3

Abstract

The information of RNA secondary structure has been widely applied to the inference of RNA function. However, a classical prediction method is not feasible to long RNAs such as mRNA due to the problems of computational time and numerical errors. To overcome those problems, sliding window methods have been applied while their results are not directly comparable to global RNA structure prediction. In this chapter, we introduce ParasoR, a method designed for parallel computation of genome-wide RNA secondary structures. To enable genome-wide prediction, ParasoR distributes dynamic programming (DP) matrices required for structure prediction to multiple computational nodes. Using the database of not the original DP variable but the ratio of variables, ParasoR can locally compute the structure scores such as stem probability or accessibility on demand. A comprehensive analysis of local secondary structures by ParasoR is expected to be a promising way to detect the statistical constraints on long RNAs.

Keywords

Genome-wide Local structure Maximal span constraint Parallel computation RNA secondary structure

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MeSH Term

RNA
Algorithms
Nucleic Acid Conformation
Computational Biology
RNA, Messenger

Chemicals

RNA
RNA, Messenger
Journal Article
Article has an altmetric score of 4

Word Cloud

Created with Highcharts 10.0.0RNAstructuresecondarypredictionParasoRappliedmethodlongRNAsproblemscomputationalcomputationgenome-widestructuresDPinformationwidelyinferencefunctionHoweverclassicalfeasiblemRNAduetimenumericalerrorsovercomeslidingwindowmethodsresultsdirectlycomparableglobalchapterintroducedesignedparallelenable ParasoRdistributesdynamicprogrammingmatricesrequiredmultiplenodesUsingdatabaseoriginalvariableratiovariablescanlocallycomputescoresstemprobabilityaccessibilitydemandcomprehensiveanalysislocalexpectedpromisingwaydetectstatisticalconstraintsGenome-WideSecondaryStructurePredictionGenome-wideLocalMaximalspanconstraintParallel

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