7
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      RiceMetaSys: Drought-miR, a one-stop solution for drought responsive miRNAs-mRNA module in rice

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          MicroRNAs are key players involved in stress responses in plants and reports are available on the role of miRNAs in drought stress response in rice. This work reports the development of a database, RiceMetaSys: Drought-miR, based on the meta-analysis of publicly available sRNA datasets. From 28 drought stress-specific sRNA datasets, we identified 216 drought-responsive miRNAs (DRMs). The major features of the database include genotype-, tissue- and miRNA ID-specific search options and comparison of genotypes to identify common miRNAs. Co-localization of the DRMs with the known quantitative trait loci (QTLs), i.e., meta-QTL regions governing drought tolerance in rice pertaining to different drought adaptive traits, narrowed down this to 37 promising DRMs. To identify the high confidence target genes of DRMs under drought stress, degradome datasets and web resource on drought-responsive genes (RiceMetaSys: DRG) were used. Out of the 216 unique DRMs, only 193 had targets with high stringent parameters. Out of the 1081 target genes identified by Degradome datasets, 730 showed differential expression under drought stress in at least one accession. To retrieve complete information on the target genes, the database has been linked with RiceMetaSys: DRG. Further, we updated the RiceMetaSys: DRGv1 developed earlier with the addition of DRGs identified from RNA-seq datasets from five rice genotypes. We also identified 759 putative novel miRNAs and their target genes employing stringent criteria. Novel miRNA search has all the search options of known miRNAs and additionally, it gives information on their in silico validation features. Simple sequence repeat markers for both the miRNAs and their target genes have also been designed and made available in the database. Network analysis of the target genes identified 60 hub genes which primarily act through abscisic acid pathway and jasmonic acid pathway. Co-localization of the hub genes with the meta-QTL regions governing drought tolerance narrowed down this to 16 most promising DRGs.

          Database URL: http://14.139.229.201/RiceMetaSys_miRNA

          Updated database of RiceMetaSys URL: http://14.139.229.201/RiceMetaSysA/Drought/

          Related collections

          Most cited references57

          • Record: found
          • Abstract: found
          • Article: not found

          Cytoscape: a software environment for integrated models of biomolecular interaction networks.

          Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

            Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              BEDTools: a flexible suite of utilities for comparing genomic features

              Motivation: Testing for correlations between different sets of genomic features is a fundamental task in genomics research. However, searching for overlaps between features with existing web-based methods is complicated by the massive datasets that are routinely produced with current sequencing technologies. Fast and flexible tools are therefore required to ask complex questions of these data in an efficient manner. Results: This article introduces a new software suite for the comparison, manipulation and annotation of genomic features in Browser Extensible Data (BED) and General Feature Format (GFF) format. BEDTools also supports the comparison of sequence alignments in BAM format to both BED and GFF features. The tools are extremely efficient and allow the user to compare large datasets (e.g. next-generation sequencing data) with both public and custom genome annotation tracks. BEDTools can be combined with one another as well as with standard UNIX commands, thus facilitating routine genomics tasks as well as pipelines that can quickly answer intricate questions of large genomic datasets. Availability and implementation: BEDTools was written in C++. Source code and a comprehensive user manual are freely available at http://code.google.com/p/bedtools Contact: aaronquinlan@gmail.com; imh4y@virginia.edu Supplementary information: Supplementary data are available at Bioinformatics online.
                Bookmark

                Author and article information

                Contributors
                Journal
                Database (Oxford)
                Database (Oxford)
                databa
                Database: The Journal of Biological Databases and Curation
                Oxford University Press (UK )
                1758-0463
                2024
                21 August 2024
                21 August 2024
                : 2024
                : baae076
                Affiliations
                ICAR-National Institute for Plant Biotechnology , Pusa Campus, New Delhi 110012, India
                The Graduate School, ICAR-Indian Agricultural Research Institute , Pusa Campus, New Delhi 110012, India
                ICAR-National Institute for Plant Biotechnology , Pusa Campus, New Delhi 110012, India
                AKMU, ICAR-Indian Agricultural Research Institute , Pusa Campus, New Delhi 110012, India
                ICAR-National Institute for Plant Biotechnology , Pusa Campus, New Delhi 110012, India
                ICAR-National Institute for Plant Biotechnology , Pusa Campus, New Delhi 110012, India
                The Graduate School, ICAR-Indian Agricultural Research Institute , Pusa Campus, New Delhi 110012, India
                ICAR-Indian Agricultural Statistics Research Institute , Pusa Campus, New Delhi 110012, India
                ICAR-Indian Agricultural Statistics Research Institute , Pusa Campus, New Delhi 110012, India
                ICAR-Indian Agricultural Statistics Research Institute , Pusa Campus, New Delhi 110012, India
                ICAR-National Institute for Plant Biotechnology , Pusa Campus, New Delhi 110012, India
                ICAR-National Institute for Plant Biotechnology , Pusa Campus, New Delhi 110012, India
                Author notes
                *Corresponding author. ICAR-National Institute for Plant Biotechnology, New Delhi, Delhi 110012, India. E-mail: amithamithra.nrcpb@ 123456gmail.com ; amitha.sevanthi@ 123456icar.gov.in
                Author information
                https://orcid.org/0000-0002-8941-2014
                https://orcid.org/0000-0002-8447-9148
                Article
                baae076
                10.1093/database/baae076
                11338179
                39167719
                4ff996e7-0569-44af-bf84-c8ce09fff320
                © The Author(s) 2024. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 02 February 2024
                : 27 May 2024
                : 08 August 2024
                : 08 July 2024
                : 21 August 2024
                Page count
                Pages: 12
                Funding
                Funded by: Centre for Agricultural Bioinformatics, DOI 10.13039/501100020703;
                Award ID: 1006456
                Funded by: Centre for Agricultural Bioinformatics, DOI 10.13039/501100020703;
                Award ID: 1006456
                Categories
                Original Article
                AcademicSubjects/SCI00960

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

                Comments

                Comment on this article