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Long noncoding RNAs are well studied for their regulatory actions through interaction with DNA regulating biological roles of DNA, RNA, or protein. However, direct binding of lncRNA with DNA is rarely demonstrated in experiments. The present protocol explains genome wide computational strategies to choose lncRNAs that can bind directly to the chromatin by forming highly stable DNA-DNA-RNA triplexes. The chapter also focuses on biophysical methods that can be used to validate the computationally derived lncRNA-gene targets in vitro.

Circular RNAs (circRNAs) are unique transcript isoforms characterized by back splicing of exon ends to form a covalently closed loop or circular conformation. These transcript isoforms are now known to be expressed in a variety of organisms across the kingdoms of life. Recent studies have shown the role of circRNAs in a number of diseases and increasing evidence points to their potential application as biomarkers in these diseases. We have created a comprehensive manually curated database of circular RNAs associated with diseases. This database is available at URL http://clingen.igib.res.in/circad/. The Database lists more than 1300 circRNAs associated with 150 diseases and mapping to 113 International Statistical Classification of Diseases (ICD) codes with evidence of association linked to published literature. The database is unique in many ways. Firstly, it provides ready-to-use primers to work with, in order to use circRNAs as biomarkers or to perform functional studies. It additionally lists the assay and PCR primer details including experimentally validated ones as a ready reference to researchers along with fold change and statistical significance. It also provides standard disease nomenclature as per the ICD codes. To the best of our knowledge, circad is the most comprehensive and updated database of disease associated circular RNAs., Availability: http://clingen.igib.res.in/circad/., (© The Author(s) 2020. Published by Oxford University Press.)

Long noncoding RNAs (lncRNAs) belong to a class of RNA transcripts that do not have the potential to code for proteins. LncRNAs were largely discovered in the transcriptomes of human and several model organisms, using next-generation sequencing (NGS) approaches, which have enabled a comprehensive genome scale annotation of transcripts. LncRNAs are known to have dynamic expression status and have the potential to orchestrate gene regulation at the epigenetic, transcriptional, and posttranscriptional levels. Here we describe the experimental methods involved in the discovery of lncRNAs from the transcriptome of a popular model organism zebrafish (Danio rerio). A structured and well-designed computational analysis pipeline subsequent to the RNA sequencing can be instrumental in revealing the diversity of the lncRNA transcripts. We describe one such computational pipeline used for the discovery of novel lncRNA transcripts in zebrafish. We also detail the validation of the putative novel lncRNA transcripts using qualitative and quantitative assays in zebrafish.

Circular RNAs are an emerging class of transcript isoforms created by unique back splicing of exons to form a closed covalent circular structure. While initially considered as product of aberrant splicing, recent evidence suggests unique functions and conservation across evolution. While circular RNAs could be largely attributed to have little or no potential to encode for proteins, recent evidence points to at least a small subset of circular RNAs which encode for peptides. Circular RNAs are also increasingly shown to be biomarkers for a number of diseases including neurological disorders and cancer. The advent of deep sequencing has enabled large-scale identification of circular RNAs in human and other genomes. A number of computational approaches have come up in recent years to query circular RNAs on a genome-wide scale from RNA-seq data. In this chapter, we describe the application and methodology of identifying circular RNAs using three popular computational tools: FindCirc, Segemehl, and CIRI along with approaches for experimental validation of the unique splice junctions.

Background: Only a handful of long noncoding RNAs have been functionally characterized. They are known to modulate regulation through interacting with other biomolecules in the cell: DNA, RNA and protein. Though there have been detailed investigations on lncRNA-miRNA and lncRNA-protein interactions, the interaction of lncRNAs with DNA have not been studied extensively. In the present study, we explore whether lncRNAs could modulate genomic regulation by interacting with DNA through the formation of highly stable DNA:DNA:RNA triplexes., Methods: We computationally screened 23,898 lncRNA transcripts as annotated by GENCODE, across the human genome for potential triplex forming sequence stretches (PTS). The PTS frequencies were compared across 5'UTR, CDS, 3'UTR, introns, promoter and 1000 bases downstream of the transcription termination sites. These regions were annotated by mapping to experimental regulatory regions, classes of repeat regions and transcription factors. We validated few putative triplex mediated interactions where lncRNA-gene pair interaction is via pyrimidine triplex motif using biophysical methods., Results: We identified 20,04,034 PTS sites to be enriched in promoter and intronic regions across human genome. Additional analysis of the association of PTS with core promoter elements revealed a systematic paucity of PTS in all regulatory regions, except TF binding sites. A total of 25 transcription factors were found to be associated with PTS. Using an interaction network, we showed that a subset of the triplex forming lncRNAs, have a positive association with gene promoters. We also demonstrated an in vitro interaction of one lncRNA candidate with its predicted gene target promoter regions., Conclusions: Our analysis shows that PTS are enriched in gene promoter and largely associated with simple repeats. The current study suggests a major role of a subset of lncRNAs in mediating chromatin organization modulation through CTCF and NSRF proteins.

The ubiquitous role of microRNAs (miRNAs) in a number of pathological processes has suggested that they could act as potential drug targets. RNA-binding small molecules offer an attractive means for modulating miRNA function. The availability of bioassay data sets for a variety of biological assays and molecules in public domain provides a new opportunity toward utilizing them to create models and further utilize them for in silico virtual screening approaches to prioritize or assign potential functions for small molecules. Here, we describe a computational strategy based on machine learning for creation of predictive models from high-throughput biological screens for virtual screening of small molecules with the potential to inhibit microRNAs. Such models could be potentially used for computational prioritization of small molecules before performing high-throughput biological assay.

Background: The dynamic and differential regulation and expression of genes is majorly governed by the complex interactions of a subset of biomolecules in the cell operating at multiple levels starting from genome organisation to protein post-translational regulation. The regulatory layer contributed by the epigenetic layer has been one of the favourite areas of interest recently. This layer of regulation as we know today largely comprises of DNA modifications, histone modifications and noncoding RNA regulation and the interplay between each of these major components. Epigenetic regulation has been recently shown to be central to development of a number of disease processes. The availability of datasets of high-throughput screens for molecules for biological properties offer a new opportunity to develop computational methodologies which would enable in-silico screening of large molecular libraries., Methods: In the present study, we have used data from high throughput screens for the inhibitors of epigenetic modifiers. Computational predictive models were constructed based on the molecular descriptors. Machine learning algorithms for supervised training, Naive Bayes and Random Forest, were used to generate predictive models for the small molecule inhibitors of histone methyl-transferases and demethylases. Random forest, with the accuracy of 80%, was identified as the most accurate classifier. Further we complemented the study with substructure search approach filtering out the probable pharmacophores from the active molecules leading to drug molecules., Results: We show that effective use of appropriate computational algorithms could be used to learn molecular and structural correlates of biological activities of small molecules. The computational models developed could be potentially used to screen and identify potential new biological activities of molecules from large molecular libraries and prioritise them for in-depth biological assays. To the best of our knowledge, this is the first and most comprehensive computational analysis towards understanding activities of small molecules inhibitors of epigenetic modifiers., Competing Interests: The authors have declared that no competing interests exist.

There is emergence of multidrug-resistant Salmonella enterica serotype typhi in pandemic proportions throughout the world, and therefore, there is a necessity to speed up the discovery of novel molecules having different modes of action and also less influenced by the resistance formation that would be used as drug for the treatment of salmonellosis particularly typhoid fever. The PhoP regulon is well studied and has now been shown to be a critical regulator of number of gene expressions which are required for intracellular survival of S. enterica and pathophysiology of disease like typhoid. The evident roles of two-component PhoP-/PhoQ-regulated products in salmonella virulence have motivated attempts to target them therapeutically. Although the discovery process of biologically active compounds for the treatment of typhoid relies on hit-finding procedure, using high-throughput screening technology alone is very expensive, as well as time consuming when performed on large scales. With the recent advancement in combinatorial chemistry and contemporary technique for compounds synthesis, there are more and more compounds available which give ample growth of diverse compound library, but the time and endeavor required to screen these unfocused massive and diverse library have been slightly reduced in the past years. Hence, there is demand to improve the high-quality hits and success rate for high-throughput screening that required focused and biased compound library toward the particular target. Therefore, we still need an advantageous and expedient method to prioritize the molecules that will be utilized for biological screens, which saves time and is also inexpensive. In this concept, in silico methods like machine learning are widely applicable technique used to build computational model for high-throughput virtual screens to prioritize molecules for advance study. Furthermore, in computational analysis, we extended our study to identify the common enriched structural entities among the biologically active compound toward finding out the privileged scaffold.

The human mitochondrial genome has been reported to have a very high mutation rate as compared with the nuclear genome. A large number of mitochondrial mutations show significant phenotypic association and are involved in a broad spectrum of diseases. In recent years, there has been a remarkable progress in the understanding of mitochondrial genetics. The availability of next-generation sequencing (NGS) technologies have not only reduced sequencing cost by orders of magnitude but has also provided us good quality mitochondrial genome sequences with high coverage, thereby enabling decoding of a number of human mitochondrial diseases. In this study, we report a computational and experimental pipeline to decipher the human mitochondrial DNA variations and examine them for their clinical correlation. As a proof of principle, we also present a clinical study of a patient with Leigh disease and confirmed maternal inheritance of the causative allele. The pipeline is made available as a user-friendly online tool to annotate variants and find haplogroup, disease association, and heteroplasmic sites. The "mit-o-matic" computational pipeline represents a comprehensive cloud-based tool for clinical evaluation of mitochondrial genomic variations from NGS datasets. The tool is freely available at http://genome.igib.res.in/mitomatic/., (© 2015 WILEY PERIODICALS, INC.)

Human mitochondrial DNA (mtDNA) encodes a set of 37 genes which are essential structural and functional components of the electron transport chain. Variations in these genes have been implicated in a broad spectrum of diseases and are extensively reported in literature and various databases. In this study, we describe MitoLSDB, an integrated platform to catalogue disease association studies on mtDNA (http://mitolsdb.igib.res.in). The main goal of MitoLSDB is to provide a central platform for direct submissions of novel variants that can be curated by the Mitochondrial Research Community. MitoLSDB provides access to standardized and annotated data from literature and databases encompassing information from 5231 individuals, 675 populations and 27 phenotypes. This platform is developed using the Leiden Open (source) Variation Database (LOVD) software. MitoLSDB houses information on all 37 genes in each population amounting to 132397 variants, 5147 unique variants. For each variant its genomic location as per the Revised Cambridge Reference Sequence, codon and amino acid change for variations in protein-coding regions, frequency, disease/phenotype, population, reference and remarks are also listed. MitoLSDB curators have also reported errors documented in literature which includes 94 phantom mutations, 10 NUMTs, six documentation errors and one artefactual recombination. MitoLSDB is the largest repository of mtDNA variants systematically standardized and presented using the LOVD platform. We believe that this is a good starting resource to curate mtDNA variants and will facilitate direct submissions enhancing data coverage, annotation in context of pathogenesis and quality control by ensuring non-redundancy in reporting novel disease associated variants.

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.

The advancements in genomics technologies and the amenability to large-scale computational analysis have contributed immensely to the understanding of the zebrafish genome, its organization, and its functional correlates. Translating genomics information into biological meaning would require integration and amenability of data and tools. FishMap is a community resource for genomic datasets on zebrafish created with a vision to provide relevant and readily available information to zebrafish researchers. The present update of FishMap has kept up with the availability of the latest zebrafish genome assembly (Zv8). In this update, particular emphasis has been given to noncoding RNAs and noncoding RNA-mediated regulation in addition to genomic regulatory motifs, which are emerging areas of vertebrate biology. FishMap Zv8 update also features a sequence mapping and analysis server. Consistent with its commitment to make the information freely available to the community, FishMap features options to share data between compatible resources in addition to making it amenable to programmatic access. FishMap Zv8 update is available at http://fishmap2.igib.res.in.

MicroRNAs (small approximately 22 nucleotide long non-coding endogenous RNAs) have recently attracted immense attention as critical regulators of gene expression in multi-cellular eukaryotes, especially in humans. Recent studies have proved that viruses also express microRNAs, which are thought to contribute to the intricate mechanisms of host-pathogen interactions. Computational predictions have greatly accelerated the discovery of microRNAs. However, most of these widely used tools are dependent on structural features and sequence conservation which limits their use in discovering novel virus expressed microRNAs and non-conserved eukaryotic microRNAs. In this work an efficient prediction method is developed based on the hypothesis that sequence and structure features which discriminate between host microRNA precursor hairpins and pseudo microRNAs are shared by viral microRNA as they depend on host machinery for the processing of microRNA precursors. The proposed method has been found to be more efficient than recently reported ab-initio methods for predicting viral microRNAs and microRNAs expressed by mammals.

Background: MicroRNAs (miRNAs) regulate several biological processes through post-transcriptional gene silencing. The efficiency of binding of miRNAs to target transcripts depends on the sequence as well as intramolecular structure of the transcript. Single Nucleotide Polymorphisms (SNPs) can contribute to alterations in the structure of regions flanking them, thereby influencing the accessibility for miRNA binding., Description: The entire human genome was analyzed for SNPs in and around predicted miRNA target sites. Polymorphisms within 200 nucleotides that could alter the intramolecular structure at the target site, thereby altering regulation were annotated. Collated information was ported in a MySQL database with a user-friendly interface accessible through the URL: (http://miracle.igib.res.in/dbSMR)., Conclusion: The database has a user-friendly interface where the information can be queried using either the gene name, microRNA name, polymorphism ID or transcript ID. Combination queries using 'AND' or 'OR' is also possible along with specifying the degree of change of intramolecular bonding with and without the polymorphism. Such a resource would enable researchers address questions like the role of regulatory SNPs in the 3' UTRs and population specific regulatory modulations in the context of microRNA targets.

: Earthworms show a wide spectrum of regenerative potential with certain species like Eisenia fetida capable of regenerating more than two-thirds of their body while other closely related species, such as Paranais litoralis seem to have lost this ability. Earthworms belong to the phylum Annelida, in which the genomes of the marine oligochaete Capitella telata and the freshwater leech Helobdella robusta have been sequenced and studied. Herein, we report the transcriptomic changes in Eisenia fetida (Indian isolate) during regeneration. Following injury, E. fetida regenerates the posterior segments in a time spanning several weeks. We analyzed gene expression changes both in the newly regenerating cells and in the adjacent tissue, at early (15days post amputation), intermediate (20days post amputation) and late (30 days post amputation) by RNAseq based de novo assembly and comparison of transcriptomes. We also generated a draft genome sequence of this terrestrial red worm using short reads and mate-pair reads. An in-depth analysis of the miRNome of the worm showed that many miRNA gene families have undergone extensive duplications. Sox4, a master regulator of TGF-beta mediated epithelial-mesenchymal transition was induced in the newly regenerated tissue. Genes for several proteins such as sialidases and neurotrophins were identified amongst the differentially expressed transcripts. The regeneration of the ventral nerve cord was also accompanied by the induction of nerve growth factor and neurofilament genes. We identified 315 novel differentially expressed transcripts in the transcriptome, that have no homolog in any other species. Surprisingly, 82% of these novel differentially expressed transcripts showed poor potential for coding proteins, suggesting that novel ncRNAs may play a critical role in regeneration of earthworm. [ABSTRACT FROM AUTHOR]

An increase in the expression of estrogen receptors (ER) and the expanded population of ER-positive cells are two common phenotypes of breast cancer. Detection of the aberrantly expressed ERα in breast cancer is carried out using ERα-antibodies and radiolabelled ligands to make decisions about cancer treatment and targeted therapy. Capitalizing on the beneficial advantages of aptamer over the conventional antibody or radiolabelled ligand, we have identified a DNA aptamer that selectively binds and facilitates the detection of ERα in human breast cancer tissue sections. The aptamer is identified using the high throughput sequencing assisted SELEX screening. Biophysical characterization confirms the binding and formation of a thermodynamically stable complex between the identified DNA aptamer (ERaptD4) and ERα (Ka = 1.55±0.298×108 M-1; ΔH = 4.32×104±801.1 cal/mol; ΔS = -108 cal/mol/deg). Interestingly, the specificity measurements suggest that the ERaptD4 internalizes into ERα-positive breast cancer cells in a target-selective manner and localizes specifically in the nuclear region. To harness these characteristics of ERaptD4 for detection of ERα expression in breast cancer samples, we performed the aptamer-assisted histochemical analysis of ERα in tissue samples from breast cancer patients. The results were validated by performing the immunohistochemistry on same samples with an ERα-antibody. We found that the two methods agree strongly in assay output (kappa value = 0.930, p-value <0.05 for strong ERα positive and the ERα negative samples; kappa value = 0.823, p-value <0.05 for the weak/moderate ER+ve samples, n = 20). Further, the aptamer stain the ERα-positive cells in breast tissues without cross-reacting to ERα-deficient fibroblasts, adipocytes, or the inflammatory cells. Our results demonstrate a significant consistency in the aptamer-assisted detection of ERα in strong ERα positive, moderate ERα positive and ERα negative breast cancer tissues. We anticipate that the ERaptD4 aptamer targeting ERα may potentially be used for an efficient grading of ERα expression in cancer tissues. [ABSTRACT FROM AUTHOR]

The organization of structure and function of cardiac chambers in vertebrates is defined by chamber-specific distinct gene expression. This peculiarity and uniqueness of the genetic signatures demonstrates functional resolution attributed to the different chambers of the heart. Altered expression of the cardiac chamber genes can lead to individual chamber related dysfunctions and disease patho-physiologies. Information on transcriptional repertoire of cardiac compartments is important to understand the spectrum of chamber specific anomalies. We have carried out a genome wide transcriptome profiling study of the three cardiac chambers in the zebrafish heart using RNA sequencing. We have captured the gene expression patterns of 13,396 protein coding genes in the three cardiac chambers—atrium, ventricle and bulbus arteriosus. Of these, 7,260 known protein coding genes are highly expressed (≥10 FPKM) in the zebrafish heart. Thus, this study represents nearly an all-inclusive information on the zebrafish cardiac transcriptome. In this study, a total of 96 differentially expressed genes across the three cardiac chambers in zebrafish were identified. The atrium, ventricle and bulbus arteriosus displayed 20, 32 and 44 uniquely expressing genes respectively. We validated the expression of predicted chamber-restricted genes using independent semi-quantitative and qualitative experimental techniques. In addition, we identified 23 putative novel protein coding genes that are specifically restricted to the ventricle and not in the atrium or bulbus arteriosus. In our knowledge, these 23 novel genes have either not been investigated in detail or are sparsely studied. The transcriptome identified in this study includes 68 differentially expressing zebrafish cardiac chamber genes that have a human ortholog. We also carried out spatiotemporal gene expression profiling of the 96 differentially expressed genes throughout the three cardiac chambers in 11 developmental stages and 6 tissue types of zebrafish. We hypothesize that clustering the differentially expressed genes with both known and unknown functions will deliver detailed insights on fundamental gene networks that are important for the development and specification of the cardiac chambers. It is also postulated that this transcriptome atlas will help utilize zebrafish in a better way as a model for studying cardiac development and to explore functional role of gene networks in cardiac disease pathogenesis. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: With a higher throughput and lower cost in sequencing, second generation sequencing technology has immense potential for translation into clinical practice and in the realization of pharmacogenomics based patient care. The systematic analysis of whole genome sequences to assess patient to patient variability in pharmacokinetics and pharmacodynamics responses towards drugs would be the next step in future medicine in line with the vision of personalizing medicine. Methods: Genomic DNA obtained from a 55 years old, self-declared healthy, anonymous male of Malay descent was sequenced. The subject's mother died of lung cancer and the father had a history of schizophrenia and deceased at the age of 65 years old. A systematic, intuitive computational workflow/pipeline integrating custom algorithm in tandem with large datasets of variant annotations and gene functions for genetic variations with pharmacogenomics impact was developed. A comprehensive pathway map of drug transport, metabolism and action was used as a template to map non-synonymous variations with potential functional consequences. Principal Findings: Over 3 million known variations and 100,898 novel variations in the Malay genome were identified. Further in-depth pharmacogenetics analysis revealed a total of 607 unique variants in 563 proteins, with the eventual identification of 4 drug transport genes, 2 drug metabolizing enzyme genes and 33 target genes harboring deleterious SNVs involved in pharmacological pathways, which could have a potential role in clinical settings. Conclusions: The current study successfully unravels the potential of personal genome sequencing in understanding the functionally relevant variations with potential influence on drug transport, metabolism and differential therapeutic outcomes. These will be essential for realizing personalized medicine through the use of comprehensive computational pipeline for systematic data mining and analysis. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]