StructureFold: genome-wide RNA secondary structure mapping and reconstruction in vivo

Yin Tang; Emil Bouvier; Chun Kit Kwok; Yiliang Ding; Anton Nekrutenko; Philip C Bevilacqua; Sarah M Assmann

doi:10.1093/bioinformatics/btv213

StructureFold: genome-wide RNA secondary structure mapping and reconstruction in vivo

Bioinformatics. 2015 Aug 15;31(16):2668-75. doi: 10.1093/bioinformatics/btv213. Epub 2015 Apr 16.

Authors

Yin Tang¹, Emil Bouvier², Chun Kit Kwok³, Yiliang Ding⁴, Anton Nekrutenko⁵, Philip C Bevilacqua⁶, Sarah M Assmann⁷

Affiliations

¹ Department of Biology, Center for RNA Molecular Biology, Bioinformatics and Genomics Graduate Program.
² Department of Biochemistry and Molecular Biology, Penn State University, University Park, PA 16802, USA, Galaxyproject.org, University Park, PA 16802, USA and Baltimore, MD 21218, USA.
³ Center for RNA Molecular Biology, Department of Chemistry and.
⁴ Department of Biology, Center for RNA Molecular Biology, Department of Chemistry and.
⁵ Bioinformatics and Genomics Graduate Program, Department of Biochemistry and Molecular Biology, Penn State University, University Park, PA 16802, USA, Galaxyproject.org, University Park, PA 16802, USA and Baltimore, MD 21218, USA.
⁶ Center for RNA Molecular Biology, Department of Chemistry and Plant Biology Graduate Program, Pennsylvania State University, University Park, Pennsylvania 16802, USA.
⁷ Department of Biology, Center for RNA Molecular Biology, Bioinformatics and Genomics Graduate Program, Plant Biology Graduate Program, Pennsylvania State University, University Park, Pennsylvania 16802, USA.

Abstract

Motivation: RNAs fold into complex structures that are integral to the diverse mechanisms underlying RNA regulation of gene expression. Recent development of transcriptome-wide RNA structure profiling through the application of structure-probing enzymes or chemicals combined with high-throughput sequencing has opened a new field that greatly expands the amount of in vitro and in vivo RNA structural information available. The resultant datasets provide the opportunity to investigate RNA structural information on a global scale. However, the analysis of high-throughput RNA structure profiling data requires considerable computational effort and expertise.

Results: We present a new platform, StructureFold, that provides an integrated computational solution designed specifically for large-scale RNA structure mapping and reconstruction across any transcriptome. StructureFold automates the processing and analysis of raw high-throughput RNA structure profiling data, allowing the seamless incorporation of wet-bench structural information from chemical probes and/or ribonucleases to restrain RNA secondary structure prediction via the RNAstructure and ViennaRNA package algorithms. StructureFold performs reads mapping and alignment, normalization and reactivity derivation, and RNA structure prediction in a single user-friendly web interface or via local installation. The variation in transcript abundance and length that prevails in living cells and consequently causes variation in the counts of structure-probing events between transcripts is accounted for. Accordingly, StructureFold is applicable to RNA structural profiling data obtained in vivo as well as to in vitro or in silico datasets. StructureFold is deployed via the Galaxy platform.

Availability and implementation: StructureFold is freely available as a component of Galaxy available at: https://usegalaxy.org/.

Contact: yxt148@psu.edu or sma3@psu.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Publication types

Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Algorithms*
Arabidopsis / genetics*
Computational Biology / methods
Genome, Plant / genetics*
Nucleic Acid Conformation*
RNA / chemistry*
RNA, Plant / genetics*
RNA, Ribosomal, 18S / genetics*
Regulatory Sequences, Ribonucleic Acid / genetics
Sequence Analysis, RNA / methods*
Transcriptome

Substances

RNA, Plant
RNA, Ribosomal, 18S
Regulatory Sequences, Ribonucleic Acid
RNA

Grants and funding

R01 HG004909/HG/NHGRI NIH HHS/United States