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Genome-wide measurement of RNA secondary structure in yeast.

ABSTRACT: The structures of RNA molecules are often important for their function and regulation, yet there are no experimental techniques for genome-scale measurement of RNA structure. Here we describe a novel strategy termed parallel analysis of RNA structure (PARS), which is based on deep sequencing fragments of RNAs that were treated with structure-specific enzymes, thus providing simultaneous in vitro profiling of the secondary structure of thousands of RNA species at single nucleotide resolution. We apply PARS to profile the secondary structure of the messenger RNAs (mRNAs) of the budding yeast Saccharomyces cerevisiae and obtain structural profiles for over 3,000 distinct transcripts. Analysis of these profiles reveals several RNA structural properties of yeast transcripts, including the existence of more secondary structure over coding regions compared with untranslated regions, a three-nucleotide periodicity of secondary structure across coding regions and an anti-correlation between the efficiency with which an mRNA is translated and the structure over its translation start site. PARS is readily applicable to other organisms and to profiling RNA structure in diverse conditions, thus enabling studies of the dynamics of secondary structure at a genomic scale.

SUBMITTER: Kertesz M

PROVIDER: S-EPMC3847670 | biostudies-literature | 2010 Sep

REPOSITORIES: biostudies-literature

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Genome-wide measurement of RNA secondary structure in yeast.

Kertesz Michael M Wan Yue Y Mazor Elad E Rinn John L JL Nutter Robert C RC Chang Howard Y HY Segal Eran E

Nature 20100901 7311

The structures of RNA molecules are often important for their function and regulation, yet there are no experimental techniques for genome-scale measurement of RNA structure. Here we describe a novel strategy termed parallel analysis of RNA structure (PARS), which is based on deep sequencing fragments of RNAs that were treated with structure-specific enzymes, thus providing simultaneous in vitro profiling of the secondary structure of thousands of RNA species at single nucleotide resolution. We ...[more]

PMID: 20811459

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Project description:The structures of RNA molecules are often important for their function and regulation, yet there are no experimental techniques for genome-scale measurement of RNA structure. Here, we describe a novel strategy termed Parallel Analysis of RNA Structure (PARS), which is based on deep sequencing fragments of RNAs that were treated with structure-specific enzymes, thus providing simultaneous in-vitro profiling of the secondary structure of thousands of RNA species at single nucleotide resolution. We apply PARS to profile the secondary structure of the mRNAs of the budding yeast S. cerevisiae and obtain structural profiles for over 3000 distinct transcripts. Analysis of these profiles reveals several RNA structural properties of yeast transcripts, including the existence of more secondary structure over coding regions compared to untranslated regions, a three-nucleotide periodicity of secondary structure across coding regions, and a relationship between the efficiency with which an mRNA is translated and the lack of structure over its translation start site. PARS is readily applicable to other organisms and to profiling RNA structure in diverse conditions, thus enabling studies of the dynamics of secondary structure at a genomic scale. RNA sample was treated with one of two structure-specific enzymes (RNase V1 or RNase S1). Four independent V1 experiments and three independent S1 experiments were carried out. Processed data file linked below. Data processing involves merging (or rather log-ratio-ing) the 7 lanes of SOLiD sequencing data against each other. Also linked below are the genome and transcriptome FASTA files used for mapping, and the annotation file having the format: gene_ID, chromosome, start, end, feature. Start and end are 1-based; feature is "Transcript" for the entire transcript (including introns), "Intron", "Exon", "5UTR" or "3UTR". Genome-wide measurement of RNA secondary structure in yeast, Kertesz et al., Nature Volume:467, Pages:103-107, Date published:(02 September 2010) http://www.nature.com/nature/journal/v467/n7311/abs/nature09322.html

Project description: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.

Project description:BackgroundWith the rapid emergence of RNA databases and newly identified non-coding RNAs, an efficient compression algorithm for RNA sequence and structural information is needed for the storage and analysis of such data. Although several algorithms for compressing DNA sequences have been proposed, none of them are suitable for the compression of RNA sequences with their secondary structures simultaneously. This kind of compression not only facilitates the maintenance of RNA data, but also supplies a novel way to measure the informational complexity of RNA structural data, raising the possibility of studying the relationship between the functional activities of RNA structures and their complexities, as well as various structural properties of RNA based on compression.ResultsRNACompress employs an efficient grammar-based model to compress RNA sequences and their secondary structures. The main goals of this algorithm are two fold: (1) present a robust and effective way for RNA structural data compression; (2) design a suitable model to represent RNA secondary structure as well as derive the informational complexity of the structural data based on compression. Our extensive tests have shown that RNACompress achieves a universally better compression ratio compared with other sequence-specific or common text-specific compression algorithms, such as Gencompress, winrar and gzip. Moreover, a test of the activities of distinct GTP-binding RNAs (aptamers) compared with their structural complexity shows that our defined informational complexity can be used to describe how complexity varies with activity. These results lead to an objective means of comparing the functional properties of heteropolymers from the information perspective.ConclusionA universal algorithm for the compression of RNA secondary structure as well as the evaluation of its informational complexity is discussed in this paper. We have developed RNACompress, as a useful tool for academic users. Extensive tests have shown that RNACompress is a universally efficient algorithm for the compression of RNA sequences with their secondary structures. RNACompress also serves as a good measurement of the informational complexity of RNA secondary structure, which can be used to study the functional activities of RNA molecules.

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Genome-wide measurement of RNA secondary structure in yeast.

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Genome-wide measurement of RNA secondary structure in yeast.

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