Parallel computation of genome-scale RNA secondary structure to detect structural constraints on human genome

Abstract

BackgroundRNA 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.ResultsOur 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.ConclusionsWe 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1067-9) contains supplementary material, which is available to authorized users.

Discovery of a pre-mRNA structural scaffold as a contributor to the mammalian splicing code

“…4A) indicates that elements outside of 5′ exon or 5′SS also participate in defining the context for U1 snRNP binding. A previous genome-wide bioinformatic analysis of about 350,000 5′SS indicates that 5′SS are highly structured genome-wide (29). Furthermore, in the accompanying manuscript we observed that a βglobin pre-mRNA with hybridized 5′ exon with its 5′SS on a single-stranded segment (EH3+4+ED1+2 mutant of β-globin) is still unable to assemble spliceosome even though the 5′SS resemble the consensus sequence and considered strong.…”

RintC: fast and accuracy-aware decomposition of distributions of RNA secondary structures with extended logsumexp

“…The algorithms were implemented by the authors, except for parameter file reading, which is based on ParasoR's implementation. (11) E. Computation time. To demonstrate computational efficiency, the computation time of the proposed method using the S151 Rfam Dataset (20) was measured.…”

“…We introduced maximum-span constraint to RintW in order to reduce the computational costs. While Turner energy parameters have been determined by experiments using short RNA, the computational analysis tends to be unstable when RNA can form long range base-pairs (11). Our method lost little by excluding long range base-pairs.…”

reactIDR: Evaluation of the statistical reproducibility of high-throughput structural analyses for a robust RNA reactivity classification

“…Moreover, even for the noisy genome-wide analysis of the PARS data, IDR-based classification was also successful in associating the reproducibility of read enrichment and the strength of stem probability obtained in silico (Kawaguchi and Kiryu, 2016) (shown in Supplementary Figure 8). Taken together, using reactIDR case and control read enrichment criteria, we were able to infer the accessibility of each nucleotide with higher precision than that possible when using a raw read count, by employing the data on the reproducibility and reference structure.…”

“…Many previous studies have succeeded in inferring possible secondary structures or accessibility from both experimental and computational perspectives (Puton et al, 2013). Due to the difficulties in the experimental determination of RNA secondary structure prior to the development of the next-generation sequencing, a number of computational methods was developed to predict the secondary structure from sequences or sequence alignment data (Zuker et al, 1989) These methods were shown to be highly accurate in the small RNA analyses, but the time and computer memory requirements remain too high to be applicable at genome-wide level (Bernhart et al, 2006;Kawaguchi and Kiryu, 2016).…”