Computational RNomics:Structure identification and functional prediction of non-coding RNAs in silico

The eukaryotic genome contains varying numbers of non-coding RNA(ncRNA) genes."Computational RNomics" takes a multidisciplinary approach,like information science,to resolve the structure and function of ncRNAs.Here,we review the main issues in "Computational RNomics" of data storage and management,ncRNA gene identification and characterization,ncRNA target identification and functional prediction,and we summarize the main methods and current content of "computational RNomics".     

非编码RNA与RNA组学研究现状及发展态势

人类基因组计划的完成(2001年)宣告了后基因组时代的到来,也掀起新一轮的RNA研究热潮．作为后基因组时代的科学前沿,RNA组学近年来成为生命科学领域的研究热点,各种新型ncRNA的发现,让人们对遗传信息表达调控网络有了新的认识．结合RNA领域的最新研究进展,《 中国科学C辑：生命科学》 (Science in China Series C-Life Sciences)2009年第3期的8篇述评,从动植物小分子非编码 RNA、miRNA 与细胞分化发育、miRNA 与肿瘤发生及诊断治疗的靶点、核酶的结构与功能、遗传印记起源、miRNA 基因簇的进化等多个方面进行了综述,展现了ncRNA领域的研究现状和发展前景．     

Computational methods in noncoding RNA research

Non protein-coding RNAs (ncRNAs) are a research hotspot in bioinformatics. Recent discoveries have revealed new ncRNA families performing a variety of roles, from gene expression regulation to catalytic activities. It is also believed that other families are still to be unveiled. Computational methods developed for protein coding genes often fail when searching for ncRNAs. Noncoding RNAs functionality is often heavily dependent on their secondary structure, which makes gene discovery very different from protein coding RNA genes. This motivated the development of specific methods for ncRNA research. This article reviews the main approaches used to identify ncRNAs and predict secondary structure. During the execution of this work, AML was supported by CAPES fellowship.     

Automatic prediction of non-coding RNA genes in prokaryotes based on compositional statistics

Although non-coding RNA (ncRNA) genes do not encode proteins, they play vital roles in cells by producing functionally important RNAs. In this paper, we present a novel method for predicting ncRNA genes based on compositional features extracted directly from gene sequences. Our method consists of two Support Vector Machine (SVM) models--Codon model which uses codon usage features derived from ncRNA genes and protein-coding genes and Kmer model which utilizes features of nucleotide and dinucleotide frequency extracted respectively from ncRNA genes and randomly chosen genome sequences. The 10-fold cross-validation accuracy for the two models is found to be 92% and 91%, respectively. Thus, we could make an automatic prediction of ncRNA genes in one genome without manual filtration of protein-coding genes. After applying our method in Sulfolobus solfataricus genome, 25 prediction results have been generated according to 25 cut-off pairs. We have also applied the approach in E. coli and found our results comparable to those of previous studies. In general, our method enables automatic identification of ncRNA genes in newly sequenced prokaryotic genomes.     

RNomics: identification and function of small,non-messenger RNAs

In the past few years, our knowledge about small non-mRNAs (snmRNAs) has grown exponentially. Approaches including computational and experimental RNomics have led to a plethora of novel snmRNAs, especially small nucleolar RNAs (snoRNAs). Members of this RNA class guide modification of ribosomal and spliceosomal RNAs. Novel targets for snoRNAs were identified such as tRNAs and potentially mRNAs, and several snoRNAs were shown to be tissue-specifically expressed. In addition, previously unknown classes of snmRNAs have been discovered. MicroRNAs and small interfering RNAs of about 21-23 nt, were shown to regulate gene expression by binding to mRNAs via antisense elements. Regulation of gene expression is exerted by degradation of mRNAs or translational regulation. snmRNAs play a variety of roles during regulation of gene expression. Moreover, the function of some snmRNAs known for decades, has been finally elucidated. Many other RNAs were identified by RNomics studies lacking known sequence and structure motifs. Future challenges in the field of RNomics include identification of the novel snmRNA's biological roles in the cell.     

A sequence-based filtering method for ncRNA identification and its application to searching for riboswitch elements

MOTIVATION: Recent studies have uncovered an "RNA world", in which non coding RNA (ncRNA) sequences play a central role in the regulation of gene expression. Computational studies on ncRNA have been directed toward developing detection methods for ncRNAs. State-of-the-art methods for the problem, like covariance models, suffer from high computational cost, underscoring the need for efficient filtering approaches that can identify promising sequence segments and speedup the detection process. RESULTS: In this paper we make several contributions toward this goal. First, we formalize the concept of a filter and provide figures of merit that allow comparison between filters. Second, we design efficient sequence based filters that dominate the current state-of-the-art HMM filters. Third, we provide a new formulation of the covariance model that allows speeding up RNA alignment. We demonstrate the power of our approach on both synthetic data and real bacterial genomes. We then apply our algorithm to the detection of novel riboswitch elements from the whole bacterial and archaeal genomes. Our results point to a number of novel riboswitch candidates, and include genomes that were not previously known to contain riboswitches. AVAILABILITY: The program is available upon request from the authors.     

Evolutionary Modeling and Prediction of Non-Coding RNAs in Drosophila

We performed benchmarks of phylogenetic grammar-based ncRNA gene prediction, experimenting with eight different models of structural evolution and two different programs for genome alignment. We evaluated our models using alignments of twelve Drosophila genomes. We find that ncRNA prediction performance can vary greatly between different gene predictors and subfamilies of ncRNA gene. Our estimates for false positive rates are based on simulations which preserve local islands of conservation; using these simulations, we predict a higher rate of false positives than previous computational ncRNA screens have reported. Using one of the tested prediction grammars, we provide an updated set of ncRNA predictions for D. melanogaster and compare them to previously-published predictions and experimental data. Many of our predictions show correlations with protein-coding genes. We found significant depletion of intergenic predictions near the 3′ end of coding regions and furthermore depletion of predictions in the first intron of protein-coding genes. Some of our predictions are colocated with larger putative unannotated genes: for example, 17 of our predictions showing homology to the RFAM family snoR28 appear in a tandem array on the X chromosome; the 4.5 Kbp spanned by the predicted tandem array is contained within a FlyBase-annotated cDNA.     

LocARNA-P: accurate boundary prediction and improved detection of structural RNAs

Current genomic screens for noncoding RNAs (ncRNAs) predict a large number of genomic regions containing potential structural ncRNAs. The analysis of these data requires highly accurate prediction of ncRNA boundaries and discrimination of promising candidate ncRNAs from weak predictions. Existing methods struggle with these goals because they rely on sequence-based multiple sequence alignments, which regularly misalign RNA structure and therefore do not support identification of structural similarities. To overcome this limitation, we compute columnwise and global reliabilities of alignments based on sequence and structure similarity; we refer to these structure-based alignment reliabilities as STARs. The columnwise STARs of alignments, or STAR profiles, provide a versatile tool for the manual and automatic analysis of ncRNAs. In particular, we improve the boundary prediction of the widely used ncRNA gene finder RNAz by a factor of 3 from a median deviation of 47 to 13 nt. Post-processing RNAz predictions, LocARNA-P's STAR score allows much stronger discrimination between true- and false-positive predictions than RNAz's own evaluation. The improved accuracy, in this scenario increased from AUC 0.71 to AUC 0.87, significantly reduces the cost of successive analysis steps. The ready-to-use software tool LocARNA-P produces structure-based multiple RNA alignments with associated columnwise STARs and predicts ncRNA boundaries. We provide additional results, a web server for LocARNA/LocARNA-P, and the software package, including documentation and a pipeline for refining screens for structural ncRNA, at http://www.bioinf.uni-freiburg.de/Supplements/LocARNA-P/.     

Identification of ncRNAs as potential therapeutic targets in multiple sclerosis through differential ncRNA – mRNA network analysis

Background

Several studies have revealed a potential role for both small nucleolar RNAs (snoRNAs) and microRNAs (miRNAs) in the physiopathology of relapsing-remitting multiple sclerosis (RRMS). This potential implication has been mainly described through differential expression studies. However, it has been suggested that, in order to extract additional information from large-scale expression experiments, differential expression studies must be complemented with differential network studies. Thus, the present work is aimed at the identification of potential therapeutic ncRNA targets for RRMS through differential network analysis of ncRNA – mRNA coexpression networks. ncRNA – mRNA coexpression networks have been constructed from both selected ncRNA (specifically miRNAs, snoRNAs and sdRNAs) and mRNA large-scale expression data obtained from 22 patients in relapse, the same 22 patients in remission and 22 healthy controls. Condition-specific (relapse, remission and healthy) networks have been built and compared to identify the parts of the system most affected by perturbation and aid the identification of potential therapeutic targets among the ncRNAs.

Results

All the coexpression networks we built present a scale-free topology and many snoRNAs are shown to have a prominent role in their architecture. The differential network analysis (relapse vs. remission vs. controls’ networks) has revealed that, although both network topology and the majority of the genes are maintained, few ncRNA – mRNA links appear in more than one network. We have selected as potential therapeutic targets the ncRNAs that appear in the disease-specific network and were found to be differentially expressed in a previous study.

Conclusions

Our results suggest that the diseased state of RRMS has a strong impact on the ncRNA – mRNA network of peripheral blood leukocytes, as a massive rewiring of the network happens between conditions. Our findings also indicate that the role snoRNAs have in targeted gene silencing is a widespread phenomenon. Finally, among the potential therapeutic target ncRNAs, SNORA40 seems to be the most promising candidate.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1396-5) contains supplementary material, which is available to authorized users.     

Diversity of Streptomyces spp. in Eastern Himalayan region - computational RNomics approach to phylogeny

Isolation and characterization of actinomycetes from soil samples from altitudinal gradient of North-East India were investigated for computational RNomics based phylogeny. A total of 52 diverse isolates of Streptomyces from the soil samples were isolated on four different media and from these 6 isolates were selected on the basis of cultural characteristics, microscopic and biochemical studies. Sequencing of 16S rDNA of the selected isolates identified them to belong to six different species of Streptomyces. The molecular morphometric and physico-kinetic analysis of 16S rRNA sequences were performed to predict the diversity of the genus. The computational RNomics study revealed the significance of the structural RNA based phylogenetic analysis in a relatively diverse group of Streptomyces.