Literature DB >> 26086407

Genome-wide profiling of in vivo RNA structure at single-nucleotide resolution using structure-seq.

Yiliang Ding1, Chun Kit Kwok2, Yin Tang3, Philip C Bevilacqua4, Sarah M Assmann5.   

Abstract

Structure-seq is a high-throughput and quantitative method that provides genome-wide information on RNA structure at single-nucleotide resolution. Structure-seq can be performed both in vivo and in vitro to study RNA structure-function relationships, RNA regulation of gene expression and RNA processing. Structure-seq can be carried out by an experienced molecular biologist with a basic understanding of bioinformatics. Structure-seq begins with chemical RNA structure probing under single-hit kinetics conditions. Certain chemical modifications, e.g., methylation of the Watson-Crick face of unpaired adenine and cytosine residues by dimethyl sulfate, result in a stop in reverse transcription. Modified RNA is then subjected to reverse transcription using random hexamer primers, which minimizes 3' end bias; reverse transcription proceeds until it is blocked by a chemically modified residue. Resultant cDNAs are amplified by adapter-based PCR and subjected to high-throughput sequencing, subsequently allowing retrieval of the structural information on a genome-wide scale. In contrast to classical methods that provide information only on individual transcripts, a single structure-seq experiment provides information on tens of thousands of RNA structures in ∼1 month. Although the procedure described here is for Arabidopsis thaliana seedlings in vivo, structure-seq is widely applicable, thereby opening new avenues to explore RNA structure-function relationships in living organisms.

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Year:  2015        PMID: 26086407     DOI: 10.1038/nprot.2015.064

Source DB:  PubMed          Journal:  Nat Protoc        ISSN: 1750-2799            Impact factor:   13.491


  40 in total

1.  RNA structure from deep sequencing.

Authors:  Eric Westhof
Journal:  Nat Biotechnol       Date:  2015-09       Impact factor: 54.908

2.  Comparative and integrative analysis of RNA structural profiling data: current practices and emerging questions.

Authors:  Krishna Choudhary; Fei Deng; Sharon Aviran
Journal:  Quant Biol       Date:  2017-03-30

Review 3.  Measuring RNA structure transcriptome-wide with icSHAPE.

Authors:  Dalen Chan; Chao Feng; Robert C Spitale
Journal:  Methods       Date:  2017-03-20       Impact factor: 3.608

4.  Multiplex Aptamer Discovery through Apta-Seq and Its Application to ATP Aptamers Derived from Human-Genomic SELEX.

Authors:  Michael M Abdelsayed; Bao T Ho; Michael M K Vu; Julio Polanco; Robert C Spitale; Andrej Lupták
Journal:  ACS Chem Biol       Date:  2017-07-11       Impact factor: 5.100

Review 5.  Detecting RNA G-Quadruplexes (rG4s) in the Transcriptome.

Authors:  Chun Kit Kwok; Giovanni Marsico; Shankar Balasubramanian
Journal:  Cold Spring Harb Perspect Biol       Date:  2018-07-02       Impact factor: 10.005

Review 6.  Probing the function of long noncoding RNAs in the nucleus.

Authors:  Sajal Medha K Akkipeddi; Anthony J Velleca; Dawn M Carone
Journal:  Chromosome Res       Date:  2020-02-06       Impact factor: 5.239

7.  GraphClust2: Annotation and discovery of structured RNAs with scalable and accessible integrative clustering.

Authors:  Milad Miladi; Eteri Sokhoyan; Torsten Houwaart; Steffen Heyne; Fabrizio Costa; Björn Grüning; Rolf Backofen
Journal:  Gigascience       Date:  2019-12-01       Impact factor: 6.524

8.  Probing Transcriptome-Wide RNA Structural Changes Dependent on the DEAD-box Helicase Dbp2.

Authors:  Yu-Hsuan Lai; Elizabeth J Tran
Journal:  Methods Mol Biol       Date:  2021

9.  Bridging the gap between in vitro and in vivo RNA folding.

Authors:  Kathleen A Leamy; Sarah M Assmann; David H Mathews; Philip C Bevilacqua
Journal:  Q Rev Biophys       Date:  2016-06-24       Impact factor: 5.318

10.  The discovery potential of RNA processing profiles.

Authors:  Amadís Pagès; Ivan Dotu; Joan Pallarès-Albanell; Eulàlia Martí; Roderic Guigó; Eduardo Eyras
Journal:  Nucleic Acids Res       Date:  2018-02-16       Impact factor: 16.971

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