Genome-wide searching with base-pairing kernel functions for noncoding RNAs: computational and expression analysis of snoRNA families in Caenorhabditis elegans

A max-margin training of RNA secondary structure prediction integrated with the thermodynamic model

A popular approach for predicting RNA secondary structure is the thermodynamic nearest-neighbor model that finds a thermodynamically most stable secondary structure with minimum free energy (MFE). For further improvement, an alternative approach that is based on machine learning techniques has been developed. The machine learning-based approach can employ a fine-grained model that includes much richer feature representations with the ability to fit the training data. Although a machine learning-based fine-grained model achieved extremely high performance in prediction accuracy, a possibility of the risk of overfitting for such a model has been reported. In this paper, we propose a novel algorithm for RNA secondary structure prediction that integrates the thermodynamic approach and the machine learning-based weighted approach. Our fine-grained model combines the experimentally determined thermodynamic parameters with a large number of scoring parameters for detailed contexts of features that are trained by the structured support vector machine (SSVM) with the [Formula: see text] regularization to avoid overfitting. Our benchmark shows that our algorithm achieves the best prediction accuracy compared with existing methods, and heavy overfitting cannot be observed. The implementation of our algorithm is available at https://github.com/keio-bioinformatics/mxfold .

SHARAKU: an algorithm for aligning and clustering read mapping profiles of deep sequencing in non-coding RNA processing

MOTIVATION

Deep sequencing of the transcripts of regulatory non-coding RNA generates footprints of post-transcriptional processes. After obtaining sequence reads, the short reads are mapped to a reference genome, and specific mapping patterns can be detected called read mapping profiles, which are distinct from random non-functional degradation patterns. These patterns reflect the maturation processes that lead to the production of shorter RNA sequences. Recent next-generation sequencing studies have revealed not only the typical maturation process of miRNAs but also the various processing mechanisms of small RNAs derived from tRNAs and snoRNAs.

RESULTS

We developed an algorithm termed SHARAKU to align two read mapping profiles of next-generation sequencing outputs for non-coding RNAs. In contrast with previous work, SHARAKU incorporates the primary and secondary sequence structures into an alignment of read mapping profiles to allow for the detection of common processing patterns. Using a benchmark simulated dataset, SHARAKU exhibited superior performance to previous methods for correctly clustering the read mapping profiles with respect to 5'-end processing and 3'-end processing from degradation patterns and in detecting similar processing patterns in deriving the shorter RNAs. Further, using experimental data of small RNA sequencing for the common marmoset brain, SHARAKU succeeded in identifying the significant clusters of read mapping profiles for similar processing patterns of small derived RNA families expressed in the brain.

AVAILABILITY AND IMPLEMENTATION

The source code of our program SHARAKU is available at http://www.dna.bio.keio.ac.jp/sharaku/, and the simulated dataset used in this work is available at the same link. Accession code: The sequence data from the whole RNA transcripts in the hippocampus of the left brain used in this work is available from the DNA DataBank of Japan (DDBJ) Sequence Read Archive (DRA) under the accession number DRA004502.

CONTACT

yasu@bio.keio.ac.jp

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Genome-wide analysis of small nucleolar RNAs of Leishmania major reveals a rich repertoire of RNAs involved in modification and processing of rRNA

Trypanosomatids are protozoan parasites and the causative agent of infamous infectious diseases. These organisms regulate their gene expression mainly at the post-transcriptional level and possess characteristic RNA processing mechanisms. In this study, we analyzed the complete repertoire of Leishmania major small nucleolar (snoRNA) RNAs by performing RNA-seq analysis on RNAs that were affinity-purified using the C/D snoRNA core protein, SNU13, and the H/ACA core protein, NHP2. This study revealed a large collection of C/D and H/ACA snoRNAs, organized in gene clusters generally containing both snoRNA types. Abundant snoRNAs were identified and predicted to guide trypanosome-specific rRNA cleavages. The repertoire of snoRNAs was compared to that of the closely related Trypanosoma brucei, and 80% of both C/D and H/ACA molecules were found to have functional homologues. The comparative analyses elucidated how snoRNAs evolved to generate molecules with analogous functions in both species. Interestingly, H/ACA RNAs have great flexibility in their ability to guide modifications, and several of the RNA species can guide more than one modification, compensating for the presence of single hairpin H/ACA snoRNA in these organisms. Placing the predicted modifications on the rRNA secondary structure revealed hypermodification regions mostly in domains which are modified in other eukaryotes, in addition to trypanosome-specific modifications.

snOPY: a small nucleolar RNA orthological gene database

Background

Small nucleolar RNAs (snoRNAs) are a class of non-coding RNAs that guide the modification of specific nucleotides in ribosomal RNAs (rRNAs) and small nuclear RNAs (snRNAs). Although most non-coding RNAs undergo post-transcriptional modifications prior to maturation, the functional significance of these modifications remains unknown. Here, we introduce the snoRNA orthological gene database (snOPY) as a tool for studying RNA modifications.

Findings

snOPY provides comprehensive information about snoRNAs, snoRNA gene loci, and target RNAs. It also contains data for orthologues from various species, which enables users to analyze the evolution of snoRNA genes. In total, 13,770 snoRNA genes, 10,345 snoRNA gene loci, and 133 target RNAs have been registered. Users can search and access the data efficiently using a simple web interface with a series of internal links. snOPY is freely available on the web at http://snoopy.med.miyazaki-u.ac.jp.

Conclusions

snOPY is the database that provides information about the small nucleolar RNAs and their orthologues. It will help users to study RNA modifications and snoRNA gene evolution.

Fast and accurate clustering of noncoding RNAs using ensembles of sequence alignments and secondary structures

Background

Clustering of unannotated transcripts is an important task to identify novel families of noncoding RNAs (ncRNAs). Several hierarchical clustering methods have been developed using similarity measures based on the scores of structural alignment. However, the high computational cost of exact structural alignment requires these methods to employ approximate algorithms. Such heuristics degrade the quality of clustering results, especially when the similarity among family members is not detectable at the primary sequence level.

Results

We describe a new similarity measure for the hierarchical clustering of ncRNAs. The idea is that the reliability of approximate algorithms can be improved by utilizing the information of suboptimal solutions in their dynamic programming frameworks. We approximate structural alignment in a more simplified manner than the existing methods. Instead, our method utilizes all possible sequence alignments and all possible secondary structures, whereas the existing methods only use one optimal sequence alignment and one optimal secondary structure. We demonstrate that this strategy can achieve the best balance between the computational cost and the quality of the clustering. In particular, our method can keep its high performance even when the sequence identity of family members is less than 60%.

Conclusions

Our method enables fast and accurate clustering of ncRNAs. The software is available for download at http://bpla-kernel.dna.bio.keio.ac.jp/clustering/.

Robust and accurate prediction of noncoding RNAs from aligned sequences

BACKGROUND

Computational prediction of noncoding RNAs (ncRNAs) is an important task in the post-genomic era. One common approach is to utilize the profile information contained in alignment data rather than single sequences. However, this strategy involves the possibility that the quality of input alignments can influence the performance of prediction methods. Therefore, the evaluation of the robustness against alignment errors is necessary as well as the development of accurate prediction methods.

RESULTS

We describe a new method, called Profile BPLA kernel, which predicts ncRNAs from alignment data in combination with support vector machines (SVMs). Profile BPLA kernel is an extension of base-pairing profile local alignment (BPLA) kernel which we previously developed for the prediction from single sequences. By utilizing the profile information of alignment data, the proposed kernel can achieve better accuracy than the original BPLA kernel. We show that Profile BPLA kernel outperforms the existing prediction methods which also utilize the profile information using the high-quality structural alignment dataset. In addition to these standard benchmark tests, we extensively evaluate the robustness of Profile BPLA kernel against errors in input alignments. We consider two different types of error: first, that all sequences in an alignment are actually ncRNAs but are aligned ignoring their secondary structures; second, that an alignment contains unrelated sequences which are not ncRNAs but still aligned. In both cases, the effects on the performance of Profile BPLA kernel are surprisingly small. Especially for the latter case, we demonstrate that Profile BPLA kernel is more robust compared to the existing prediction methods.

CONCLUSIONS

Profile BPLA kernel provides a promising way for identifying ncRNAs from alignment data. It is more accurate than the existing prediction methods, and can keep its performance under the practical situations in which the quality of input alignments is not necessarily high.