Publications

Computational methods for annotation of plant regulatory non-coding RNAs using RNA-seq

Published in Briefings in Bioinformatics, 2021

Plant transcriptome encompasses numerous endogenous, regulatory non-coding RNAs (ncRNAs) that play a major biological role in regulating key physiological mechanisms. While studies have shown that ncRNAs are extremely diverse and ubiquitous, the functions of the vast majority of ncRNAs are still unknown. With ever-increasing ncRNAs under study, it is essential to identify, categorize and annotate these ncRNAs on a genome-wide scale. The use of high-throughput RNA sequencing (RNA-seq) technologies provides a broader picture of the non-coding component of transcriptome, enabling the comprehensive identification and annotation of all major ncRNAs across samples. However, the detection of known and emerging class of ncRNAs from RNA-seq data demands complex computational methods owing to their unique as well as similar characteristics. Here, we discuss major plant endogenous …

Recommended citation: Vivek, A.T. and Kumar, S., 2021. Computational methods for annotation of plant regulatory non-coding RNAs using RNA-seq. Briefings in Bioinformatics, 22(4), p.bbaa322. https://academic.oup.com/bib/article-abstract/22/4/bbaa322/6041165

AlnC: An extensive database of long non-coding RNAs in angiosperms

Published in Public Library of Science, 2021

Long non-coding RNAs (lncRNAs) are defined as transcripts of greater than 200 nucleotides that play a crucial role in various cellular processes such as the development, differentiation and gene regulation across all eukaryotes, including plant cells. Since the last decade, there has been a significant rise in our understanding of lncRNA molecular functions in plants, resulting in an exponential increase in lncRNA transcripts, while these went unannounced from the major Angiosperm plant species despite the availability of large-scale high throughput sequencing data in public repositories. We, therefore, developed a user-friendly, open-access web interface, AlnC (Angiosperm lncRNA Catalogue) for the exploration of lncRNAs in diverse Angiosperm plant species using recent 1000 plant (1KP) trancriptomes data. The current version of AlnC offers 10,855,598 annotated lncRNA transcripts across 682 Angiosperm plant species encompassing 809 tissues. To improve the user interface, we added features for browsing, searching, and downloading lncRNA data, interactive graphs, and an online BLAST service. Additionally, each lncRNA record is annotated with possible small open reading frames (sORFs) to facilitate the study of peptides encoded within lncRNAs. With this user-friendly interface, we anticipate that AlnC will provide a rich source of lncRNAs for small-and large-scale studies in a variety of flowering plants, as well as aid in the improvement of key characteristics in relevance to their economic importance. Database URL: http://www.nipgr.ac.in/AlnC

Recommended citation: Singh, A., Vivek, A.T. and Kumar, S., 2021. AlnC: An extensive database of long non-coding RNAs in angiosperms. Plos one, 16(4), p.e0247215. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0247215

From current knowledge to best practice: A primer on viral diagnostics using deep sequencing of virus-derived small interfering RNAs (vsiRNAs) in infected plants

Published in Methods, 2020

Plants have evolved many defense strategies for combating viral infections. One major surveillance strategy adopted by them is manipulating viral sequences to generate distinct small RNA products via Dicer-like enzymes (DCL), and thereby restricting virus multiplication through the RNA interference (RNAi) mechanism. The power of high-throughput sequencing technologies, with diverse computational tools to handle small RNA sequencing (sRNA-Seq) data, bestows unprecedented opportunities to answer fundamental questions in plant virology. Here, we present some basic concepts of virus-derived, small interfering RNA (vsiRNA) biogenesis in plants, optimization strategies, caveats, and best practices for efficient discovery and diagnosis of known as well as novel plant viruses/viroids using deep sequencing of small RNA (sRNA) pools.

Recommended citation: Your Name, You. (2015). "Paper Title Number 3." Journal 1. 1(3). https://www.sciencedirect.com/science/article/pii/S1046202319301744

In silico identification and characterization of microRNAs based on EST and GSS in orphan legume crop, Lens culinaris medik.(lentil)

Published in Agri Gene, 2018

MicroRNAs (miRNAs) are a class of endogenous non-coding, small RNAs that are associated with the regulation of gene expression in eukaryotes. In plants, few miRNAs are highly conserved, that may have the same ancestor in the early stages of evolution. This fact allows the detection of conserved miRNAs in various plant species, especially in those that lack genome sequence information. Though the draft genome of the orphan crop, Lens culinaris Medik. (Lentil) is published, its complete genome assembly is still underway. In this computational study, an EST and GSS based comparative genomics approach were conducted to identify miRNAs in Lentils. The adopted approach was on the basis of a search for sequence similarity followed by series of filtering steps to provide reliable and precise results, eliminating the false-positive predictions. This study reports 24 miRNAs from 10,190 ESTs and 715 GSSs in …

Recommended citation: Vivek, A.T., 2018. In silico identification and characterization of microRNAs based on EST and GSS in orphan legume crop, Lens culinaris medik.(lentil). Agri Gene, 8, pp.45-56. https://www.sciencedirect.com/science/article/abs/pii/S2352215118300114

PineElm_SSRdb: a microsatellite marker database identified from genomic, chloroplast, mitochondrial and EST sequences of pineapple (Ananas comosus (L.) Merrill)

Published in Hereditas, 2016

Simple Sequence Repeats or microsatellites are resourceful molecular genetic markers. There are only few reports of SSR identification and development in pineapple. Complete genome sequence of pineapple available in the public domain can be used to develop numerous novel SSRs. Therefore, an attempt was made to identify SSRs from genomic, chloroplast, mitochondrial and EST sequences of pineapple which will help in deciphering genetic makeup of its germplasm resources. A total of 359511 SSRs were identified in pineapple (356385 from genome sequence, 45 from chloroplast sequence, 249 in mitochondrial sequence and 2832 from EST sequences). The list of EST-SSR markers and their details are available in the database. PineElm_SSRdb is an open source database available for non-commercial academic purpose at http://app.bioelm.com/ with a mapping tool which can develop circular maps of selected marker set. This database will be of immense use to breeders, researchers and graduates working on Ananas spp. and to others working on cross-species transferability of markers, investigating diversity, mapping and DNA fingerprinting.

Recommended citation: Chaudhary, S., Mishra, B.K., Vivek, T., Magadum, S. and Yasin, J.K., 2016. PineElm_SSRdb: a microsatellite marker database identified from genomic, chloroplast, mitochondrial and EST sequences of pineapple (Ananas comosus (L.) Merrill). Hereditas, 153(1), pp.1-6. https://www.sciencedirect.com/science/article/abs/pii/S2352215118300114