Deeksha Bhartiya

Systematic Transcriptome Wide Analysis of lncRNA-miRNA Interactions

Background Long noncoding RNAs (lncRNAs) are a recently discovered class of non-protein coding RNAs, which have now increasingly been shown to be involved in a wide variety of biological processes as regulatory molecules. The functional role of many of the members of this class has been an enigma, except a few of them like Malat and HOTAIR. Little is known regarding the regulatory interactions between noncoding RNA classes. Recent reports have suggested that lncRNAs could potentially interact with other classes of non-coding RNAs including microRNAs (miRNAs) and modulate their regulatory role through interactions. We hypothesized that lncRNAs could participate as a layer of regulatory interactions with miRNAs. The availability of genome-scale datasets for Argonaute targets across human transcriptome has prompted us to reconstruct a genome-scale network of interactions between miRNAs and lncRNAs. Results We used well characterized experimental Photoactivatable-Ribonucleoside-Enhanced Crosslinking and Immunoprecipitation (PAR-CLIP) datasets and the recent genome-wide annotations for lncRNAs in public domain to construct a comprehensive transcriptome-wide map of miRNA regulatory elements. Comparative analysis revealed that in addition to targeting protein-coding transcripts, miRNAs could also potentially target lncRNAs, thus participating in a novel layer of regulatory interactions between noncoding RNA classes. Furthermore, we have modeled one example of miRNA-lncRNA interaction using a zebrafish model. We have also found that the miRNA regulatory elements have a positional preference, clustering towards the mid regions and 3′ ends of the long noncoding transcripts. We also further reconstruct a genome-wide map of miRNA interactions with lncRNAs as well as messenger RNAs. Conclusions This analysis suggests widespread regulatory interactions between noncoding RNAs classes and suggests a novel functional role for lncRNAs. We also present the first transcriptome scale study on miRNA-lncRNA interactions and the first report of a genome-scale reconstruction of a noncoding RNA regulatory interactome involving lncRNAs.

lncRNome: a comprehensive knowledgebase of human long noncoding RNAs

The advent of high-throughput genome scale technologies has enabled us to unravel a large amount of the previously unknown transcriptionally active regions of the genome. Recent genome-wide studies have provided annotations of a large repertoire of various classes of noncoding transcripts. Long noncoding RNAs (lncRNAs) form a major proportion of these novel annotated noncoding transcripts, and presently known to be involved in a number of functionally distinct biological processes. Over 18 000 transcripts are presently annotated as lncRNA, and encompass previously annotated classes of noncoding transcripts including large intergenic noncoding RNA, antisense RNA and processed pseudogenes. There is a significant gap in the resources providing a stable annotation, cross-referencing and biologically relevant information. lncRNome has been envisioned with the aim of filling this gap by integrating annotations on a wide variety of biologically significant information into a comprehensive knowledgebase. To the best of our knowledge, lncRNome is one of the largest and most comprehensive resources for lncRNAs. Database URL: http://genome.igib.res.in/lncRNome

Conceptual approaches for lncRNA drug discovery and future strategies

Introduction: Long non-coding RNAs (lncRNAs) are a recently discovered class of non-coding functional RNA which has attracted immense research interest. The growing corpus of literature in the field provides ample evidence to suggest the important role of lncRNAs as regulators in a wide spectrum of biological processes. Recent evidence also suggests the role of lncRNAs in the pathophysiology of disease processes. Areas covered: The authors discuss a conceptual framework for understanding lncRNA-mediated regulation as a function of its interaction with other biomolecules in the cell. They summarize the mechanisms of the known functions of lncRNAs in light of this conceptual framework, and suggest how this insight could help in discovering novel targets for drug discovery. They also argue how certain emerging technologies could be of immense utility, both in discovering potential therapeutic targets as well as in further therapeutic development. Expert opinion: The authors propose how the field could immensely benefit from methodologies and technologies from six emerging fields in molecular and computational biology. They also suggest a futuristic area of lncRNAs design as a potential offshoot of synthetic biology, which would be an attractive field, both for discovery of targets as well as a therapeutic strategy.

miRvar: A Comprehensive Database for Genomic Variations in microRNAs

microRNAs are a recently discovered and well studied class of small noncoding functional RNAs. The regulatory role of microRNAs (miRNAs) has been well studied in a wide variety of biological processes but there have been no systematic effort to understand and analyze the genetic variations in miRNA loci and study its functional consequences. We have comprehensively curated genetic variations in miRNA loci in the human genome and established a computational pipeline to assess potential functional consequences of these variants along with methods for systematic curation and reporting of variations in these loci. The data is made available on the Leiden Open (source) Variation Database (LOVD) platform at http://genome.igib.res.in/mirlovd to provide ease of aggregation and analysis and is open for community curation efforts.

Genomic variations in non-coding RNAs: Structure, function and regulation.

The last decade has seen tremendous improvements in the understanding of human variations and their association with human traits and diseases. The availability of high-resolution map of the human transcriptome and the discovery of a large number of non-protein coding RNA genes has created a paradigm shift in the understanding of functional variations in non-coding RNAs. Several groups in recent years have reported functional variations and trait or disease associated variations mapping to non-coding RNAs including microRNAs, small nucleolar RNAs and long non-coding RNAs. The understanding of the functional consequences of variations in non-coding RNAs has been largely restricted by the limitations in understanding the functionalities of the non-coding RNAs. In this short review, we outline the current state-of-the-art of the field with emphasis on providing a conceptual outline as on how variations could modulate changes in the sequence, structure, and thereby the functionality of non-coding RNAs.

Computational approaches towards understanding human long non-coding RNA biology

Long non-coding RNAs (lncRNAs) form the largest class of non-protein coding genes in the human genome. While a small subset of well-characterized lncRNAs has demonstrated their significant role in diverse biological functions like chromatin modifications, post-transcriptional regulation, imprinting etc., the functional significance of a vast majority of them still remains an enigma. Increasing evidence of the implications of lncRNAs in various diseases including cancer and major developmental processes has further enhanced the need to gain mechanistic insights into the lncRNA functions. Here, we present a comprehensive review of the various computational approaches and tools available for the identification and annotation of long non-coding RNAs. We also discuss a conceptual roadmap to systematically explore the functional properties of the lncRNAs using computational approaches.

Systematic analysis and functional annotation of variations in the genome of an Indian individual

Whole genome sequencing of personal genomes has revealed a large repertoire of genomic variations and has provided a rich template for identification of common and rare variants in genomes in addition to understanding the genetic basis of diseases. The widespread application of personal genome sequencing in clinical settings for predictive and preventive medicine has been limited due to the lack of comprehensive computational analysis pipelines. We have used next‐generation sequencing technology to sequence the whole genome of a self‐declared healthy male of Indian origin. We have generated around 28X of the reference human genome with over 99% coverage. Analysis revealed over 3 million single nucleotide variations and about 490,000 small insertion–deletion events including several novel variants. Using this dataset as a template, we designed a comprehensive computational analysis pipeline for the systematic analysis and annotation of functionally relevant variants in the genome. This study follows a systematic and intuitive data analysis workflow to annotate genome variations and its potential functional effects. Moreover, we integrate predictive analysis of pharmacogenomic traits with emphasis on drugs for which pharmacogenomic testing has been recommended. This study thus provides the template for genome‐scale analysis of personal genomes for personalized medicine. Hum Mutat 33:1133–1140, 2012.

TBrowse: an integrative genomics map of Mycobacterium tuberculosis.

Tuberculosis is one of the major infectious diseases causing morbidity and mortality in the developing world. Genome-wide experiments on Mycobacterium tuberculosis particularly H37Rv and many other strains has revealed a wealth of information on the pathogen. This has been complemented by computational methods for the analysis of genomic sequence. This genome-level information is scattered in individual databases and supplementary material of publications and is not easily amenable to integrative analysis and visualization. TBrowse is an attempt to create a starting resource for integrative analysis of the M. tuberculosis genome. This comprehensive database contains more than half a million data-points of genomic data systematically culled from online resources and publications and is organized into hundred tracks. The resource is built based on the Generic Model Organism Database Genome Browser, thus making it readily interoperable with other genome browser installations. TBrowse is enabled with tools for programmatic data access and interoperability with other similar resources through Distributed Annotation System. In addition the resource is interfaced with sequence analysis servers maintained by the National Center for Biotechnology Information and the University of California Santa Cruz. The resource is available at http://tbrowse.osdd.net.

Distinct Patterns of Genetic Variations in Potential Functional Elements in Long Noncoding RNAs

Non‐protein‐coding RNAs have increasingly been shown to be an important class of regulatory RNAs having significant roles in regulation of gene expression. The long noncoding RNA (lncRNA) gene family presently constitutes a large number of noncoding RNA (ncRNA) loci almost equaling the number of protein‐coding genes. Nevertheless, the biological roles and mechanisms of the majority of lncRNAs are poorly understood, with exceptions of a very few well‐studied candidates. The availability of genome‐scale variation datasets, and increasing number of variant loci from genome‐wide association studies falling in lncRNA loci have motivated us to understand the patterns of genomic variations in lncRNA loci, their potential functional correlates, and selection in populations. In the present study, we have performed a comprehensive analysis of genomic variations in lncRNA loci. We analyzed for patterns and distributions of genomic variations with respect to potential functional domains in lncRNAs. The analysis reveals a distinct distribution of variations in subclasses of long ncRNAs and in potential functional domains of lncRNAs. We further examined signals of selections and allele frequencies of these prioritized set of lncRNAs. To the best of our knowledge, this is the first and comprehensive large‐scale analysis of genetic variations in long ncRNAs.

The Zebrafish GenomeWiki: a crowdsourcing approach to connect the long tail for zebrafish gene annotation.

A large repertoire of gene-centric data has been generated in the field of zebrafish biology. Although the bulk of these data are available in the public domain, most of them are not readily accessible or available in nonstandard formats. One major challenge is to unify and integrate these widely scattered data sources. We tested the hypothesis that active community participation could be a viable option to address this challenge. We present here our approach to create standards for assimilation and sharing of information and a system of open standards for database intercommunication. We have attempted to address this challenge by creating a community-centric solution for zebrafish gene annotation. The Zebrafish GenomeWiki is a ‘wiki’-based resource, which aims to provide an altruistic shared environment for collective annotation of the zebrafish genes. The Zebrafish GenomeWiki has features that enable users to comment, annotate, edit and rate this gene-centric information. The credits for contributions can be tracked through a transparent microattribution system. In contrast to other wikis, the Zebrafish GenomeWiki is a ‘structured wiki’ or rather a ‘semantic wiki’. The Zebrafish GenomeWiki implements a semantically linked data structure, which in the future would be amenable to semantic search. Database URL: http://genome.igib.res.in/twiki