A bioinformatics framework for RNA structure mining, motif discovery and polyadenylation analysis

The RNA molecules play various important roles in the cell and their functionality depends not only on the sequence information but to a large extent on their structure. The development of computational and predictive approaches to study RNA molecules is extremely valuable. In this research, a tool named RADAR was developed that provides a multitude of functionality for RNA data analysis and research. It aligns structure annotated RNA sequences so that both the sequence as well as structure information is taken into consideration. This tool is capable of performing pair-wise structure alignment, multiple structure alignment, database search and clustering. In addition, it provides two salient features: (i) constrained alignment of RNA secondary structures, and (ii) prediction of consensus structure for a set of RNA sequences. This tool is also hosted on the web and can be freely accessed and the software can be downloaded from http://datalab.njitedu/biodata/rna/RSmatch/server.htm . The RADAR software has been applied to various datasets (genomes of various mammals, viruses and parasites) and our experimental results show that this approach is capable of detecting functionally important regions. As an application of RADAR, a systematic data mining approach was developed, termed GLEAN-UTR, to identify small stem loop RNA structure elements in the Untranslated regions (UTRs) that are conserved between human and mouse orthologs and exist in multiple genes with common Gene Ontology terms. This study resulted in 90 distinct RNA structure groups containing 748 structures, with 3\u27 Histone stem loop (HSL3) and Iron Response element (IRE) among the top hits. Further, the role played by structure in mRNA polyadenylation was investigated. Polyadenylation is an important step towards the maturation of almost all cellular mRNAs in eukaryotes. Studies have identified several cis-elements besides the widely known polyadenylation signal (PAS) element (AATAAA or ATTAAA or a close variant) which may have a role to play in poly(A) site identification. In this study the differences in structural stability of sequences surrounding poly(A) sites was investigated and it was found that for the genes containing single poly(A) site, the surrounding sequence is most stable as compared with the surrounding sequences for alternative poly(A) sites. This indicates that structure may be providing a evolutionary advantage for single poly(A) sites that prevents multiple poly(A) sites from arising. In addition the study found that the structural stability of the region surrounding a polyadenylation site correlates with its distance from the next gene. The shortest distance corresponding to a greater structural stability

Functional divergence of gene duplicates – a domain-centric view

Gene duplicates have been shown to evolve at different rates. Here we further investigate the mechanism and functional underpinning of this phenomenon by assessing asymmetric evolution specifically within functional domains of gene duplicates. Based on duplicate genes in five teleost fishes resulting from a whole genome duplication event, we first show that a Fisher Exact test based approach to detect asymmetry is more sensitive than the previously used Likelihood Ratio test. Using our Fisher Exact test, we found that the evolutionary rate asymmetry in the overall protein is largely explained by the asymmetric evolution within specific protein domains. Moreover, among cases of asymmetrically evolving domains, for the gene copy containing a fast evolving domain, the non-synonymous substitutions often cluster within the fast evolving domain. We found that rare substitutions were preferred within asymmetrically evolving domains suggestive of functional divergence. While overall ~32 % of the domains tested were found to be evolving asymmetrically, certain protein domains such as the Tyrosine and Ser/Thr Kinase domains had a much greater prevalence of asymmetric evolution. Finally, based on the spatial expression of Zebra fish duplicate proteins during development, we found that protein pairs containing asymmetrically evolving domains had a greater divergence in gene expression as compared to the duplicate proteins that did not exhibit asymmetric evolution. Taken together, our results suggest that the previously observed asymmetry in the overall duplicate protein evolution is largely due to divergence of specific domains of the protein, and coincides with divergence in spatial expression domains.https://doi.org/10.1186/1471-2148-12-12

Mining small RNA structure elements in untranslated regions of human and mouse mRNAs using structure-based alignment

<p>Abstract</p> <p>Background</p> <p>UnTranslated Regions (UTRs) of mRNAs contain regulatory elements for various aspects of mRNA metabolism, such as mRNA localization, translation, and mRNA stability. Several RNA stem-loop structures in UTRs have been experimentally identified, including the histone 3' UTR stem-loop structure (HSL3) and iron response element (IRE). These stem-loop structures are conserved among mammalian orthologs, and exist in a group of genes encoding proteins involved in the same biological pathways. It is not known to what extent RNA structures like these exist in all mammalian UTRs.</p> <p>Results</p> <p>In this paper we took a systematic approach, named GLEAN-UTR, to identify small stem-loop RNA structure elements in UTRs that are conserved between human and mouse orthologs and exist in multiple genes with common Gene Ontology terms. This approach resulted in 90 distinct RNA structure groups containing 748 structures, with HSL3 and IRE among the top hits based on conservation of structure.</p> <p>Conclusion</p> <p>Our result indicates that there may exist many conserved stem-loop structures in mammalian UTRs that are involved in coordinate post-transcriptional regulation of biological pathways.</p

RADAR: a web server for RNA data analysis and research

RADAR is a web server that provides a multitude of functionality for RNA data analysis and research. It can align structure-annotated RNA sequences so that both sequence and structure information are taken into consideration during the alignment process. This server is capable of performing pairwise structure alignment, multiple structure alignment, database search and clustering. In addition, RADAR provides two salient features: (i) constrained alignment of RNA secondary structures, and (ii) prediction of the consensus structure for a set of RNA sequences. RADAR will be able to assist scientists in performing many important RNA mining operations, including the understanding of the functionality of RNA sequences, the detection of RNA structural motifs and the clustering of RNA molecules, among others. The web server together with a software package for download is freely accessible at http://datalab.njit.edu/biodata/rna/RSmatch/server.htm and http://www.ccrnp.ncifcrf.gov/~bshapiro

PIVOT: platform for interactive analysis and visualization of transcriptomics data

Abstract Background Many R packages have been developed for transcriptome analysis but their use often requires familiarity with R and integrating results of different packages requires scripts to wrangle the datatypes. Furthermore, exploratory data analyses often generate multiple derived datasets such as data subsets or data transformations, which can be difficult to track. Results Here we present PIVOT, an R-based platform that wraps open source transcriptome analysis packages with a uniform user interface and graphical data management that allows non-programmers to interactively explore transcriptomics data. PIVOT supports more than 40 popular open source packages for transcriptome analysis and provides an extensive set of tools for statistical data manipulations. A graph-based visual interface is used to represent the links between derived datasets, allowing easy tracking of data versions. PIVOT further supports automatic report generation, publication-quality plots, and program/data state saving, such that all analysis can be saved, shared and reproduced. Conclusions PIVOT will allow researchers with broad background to easily access sophisticated transcriptome analysis tools and interactively explore transcriptome datasets