title: Parallel computation of genome-scale RNA secondary structure to detect structural constraints on human genome.
abstract: BACKGROUND
  RNA secondary structure around splice sites is known to assist normal splicing by promoting spliceosome recognition. However, analyzing the structural properties of entire intronic regions or pre-mRNA sequences has been difficult hitherto, owing to serious experimental and computational limitations, such as low read coverage and numerical problems.
  
  
  RESULTS
  Our novel software, "ParasoR", is designed to run on a computer cluster and enables the exact computation of various structural features of long RNA sequences under the constraint of maximal base-pairing distance. ParasoR divides dynamic programming (DP) matrices into smaller pieces, such that each piece can be computed by a separate computer node without losing the connectivity information between the pieces. ParasoR directly computes the ratios of DP variables to avoid the reduction of numerical precision caused by the cancellation of a large number of Boltzmann factors. The structural preferences of mRNAs computed by ParasoR shows a high concordance with those determined by high-throughput sequencing analyses. Using ParasoR, we investigated the global structural preferences of transcribed regions in the human genome. A genome-wide folding simulation indicated that transcribed regions are significantly more structural than intergenic regions after removing repeat sequences and k-mer frequency bias. In particular, we observed a highly significant preference for base pairing over entire intronic regions as compared to their antisense sequences, as well as to intergenic regions. A comparison between pre-mRNAs and mRNAs showed that coding regions become more accessible after splicing, indicating constraints for translational efficiency. Such changes are correlated with gene expression levels, as well as GC content, and are enriched among genes associated with cytoskeleton and kinase functions.
  
  
  CONCLUSIONS
  We have shown that ParasoR is very useful for analyzing the structural properties of long RNA sequences such as mRNAs, pre-mRNAs, and long non-coding RNAs whose lengths can be more than a million bases in the human genome. In our analyses, transcribed regions including introns are indicated to be subject to various types of structural constraints that cannot be explained from simple sequence composition biases. ParasoR is freely available at https://github.com/carushi/ParasoR .
authors.affiliation: Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, 277-8561, Japan. kawaguchi-rs@cb.k.u-tokyo.ac.jp.
authors.forename: Risa
authors.initials: R
authors.lastname: Kawaguchi
authors.affiliation: Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, 277-8561, Japan.
authors.forename: Hisanori
authors.initials: H
authors.lastname: Kiryu
mesh_terms: D000818:Animals; D019540:Area Under Curve; D001483:Base Sequence; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D063990:Gene Ontology; D015894:Genome, Human; D006801:Humans; D051379:Mice; D008840:Microfilament Proteins / Q000378:metabolism; D009690:Nucleic Acid Conformation; D057216:Propensity Score; D012313:RNA / Q000737:chemistry* / Q000235:genetics*; D012322:RNA Precursors / Q000235:genetics / Q000378:metabolism; D012326:RNA Splicing / Q000235:genetics; D012333:RNA, Messenger / Q000235:genetics / Q000378:metabolism; D015203:Reproducibility of Results; D012984:Software; D014158:Transcription, Genetic
chemical_list: D008840:Microfilament Proteins; D012322:RNA Precursors; D012333:RNA, Messenger; D012313:RNA
keywords: Intron; PARS; Parallel computation; RNA secondary structure prediction; Splicing