AT Vivek , Namrata Sahu , Garima Kalakoti, Shailesh Kumar
{"title":"ANNInter:一个探索拟南芥ncRNA-ncRNA相互作用的平台。","authors":"AT Vivek , Namrata Sahu , Garima Kalakoti, Shailesh Kumar","doi":"10.1016/j.compbiolchem.2024.108328","DOIUrl":null,"url":null,"abstract":"<div><div>Eukaryotic transcriptomes are remarkably complex, encompassing not only protein-coding RNAs but also an expanding repertoire of noncoding RNAs (ncRNAs). In plants, ncRNA-ncRNA interactions (NNIs) have emerged as pivotal regulators of gene expression, orchestrating development and adaptive responses to stress. Despite their critical roles, the functional significance of NNIs remains poorly understood, largely due to a lack of comprehensive resources. Here, we present ANNInter, a comprehensive platform that integrates computational predictions with experimental datasets to systematically identify and analyze NNIs. The current version catalogs over 90,000 interactions spanning eight categories of sRNA-to-longer ncRNAs, each extensively annotated with interaction types, identification methods, and functional descriptions. The integrated schema and advanced visualization framework in ANNInter enable users to explore intricate interaction networks, providing system-wide insights into ncRNA-mediated regulation. These interaction data provide unparalleled opportunities to uncover the regulatory roles of NNIs in key biological processes such as growth regulation, stress adaptation, and cellular signaling. By providing an extensive, curated repository of computational and degradome-based interaction data, ANNInter will provide a platform to the study of ncRNA biology, elucidating the complex mechanisms of NNIs and supporting the concept of competing endogenous RNAs (ceRNAs) in gene regulation. The platform is freely accessible at <span><span>https://www.nipgr.ac.in/ANNInter/</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"115 ","pages":"Article 108328"},"PeriodicalIF":2.6000,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"ANNInter: A platform to explore ncRNA-ncRNA interactome of Arabidopsis thaliana\",\"authors\":\"AT Vivek , Namrata Sahu , Garima Kalakoti, Shailesh Kumar\",\"doi\":\"10.1016/j.compbiolchem.2024.108328\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Eukaryotic transcriptomes are remarkably complex, encompassing not only protein-coding RNAs but also an expanding repertoire of noncoding RNAs (ncRNAs). In plants, ncRNA-ncRNA interactions (NNIs) have emerged as pivotal regulators of gene expression, orchestrating development and adaptive responses to stress. Despite their critical roles, the functional significance of NNIs remains poorly understood, largely due to a lack of comprehensive resources. Here, we present ANNInter, a comprehensive platform that integrates computational predictions with experimental datasets to systematically identify and analyze NNIs. The current version catalogs over 90,000 interactions spanning eight categories of sRNA-to-longer ncRNAs, each extensively annotated with interaction types, identification methods, and functional descriptions. The integrated schema and advanced visualization framework in ANNInter enable users to explore intricate interaction networks, providing system-wide insights into ncRNA-mediated regulation. These interaction data provide unparalleled opportunities to uncover the regulatory roles of NNIs in key biological processes such as growth regulation, stress adaptation, and cellular signaling. By providing an extensive, curated repository of computational and degradome-based interaction data, ANNInter will provide a platform to the study of ncRNA biology, elucidating the complex mechanisms of NNIs and supporting the concept of competing endogenous RNAs (ceRNAs) in gene regulation. 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ANNInter: A platform to explore ncRNA-ncRNA interactome of Arabidopsis thaliana
Eukaryotic transcriptomes are remarkably complex, encompassing not only protein-coding RNAs but also an expanding repertoire of noncoding RNAs (ncRNAs). In plants, ncRNA-ncRNA interactions (NNIs) have emerged as pivotal regulators of gene expression, orchestrating development and adaptive responses to stress. Despite their critical roles, the functional significance of NNIs remains poorly understood, largely due to a lack of comprehensive resources. Here, we present ANNInter, a comprehensive platform that integrates computational predictions with experimental datasets to systematically identify and analyze NNIs. The current version catalogs over 90,000 interactions spanning eight categories of sRNA-to-longer ncRNAs, each extensively annotated with interaction types, identification methods, and functional descriptions. The integrated schema and advanced visualization framework in ANNInter enable users to explore intricate interaction networks, providing system-wide insights into ncRNA-mediated regulation. These interaction data provide unparalleled opportunities to uncover the regulatory roles of NNIs in key biological processes such as growth regulation, stress adaptation, and cellular signaling. By providing an extensive, curated repository of computational and degradome-based interaction data, ANNInter will provide a platform to the study of ncRNA biology, elucidating the complex mechanisms of NNIs and supporting the concept of competing endogenous RNAs (ceRNAs) in gene regulation. The platform is freely accessible at https://www.nipgr.ac.in/ANNInter/.
期刊介绍:
Computational Biology and Chemistry publishes original research papers and review articles in all areas of computational life sciences. High quality research contributions with a major computational component in the areas of nucleic acid and protein sequence research, molecular evolution, molecular genetics (functional genomics and proteomics), theory and practice of either biology-specific or chemical-biology-specific modeling, and structural biology of nucleic acids and proteins are particularly welcome. Exceptionally high quality research work in bioinformatics, systems biology, ecology, computational pharmacology, metabolism, biomedical engineering, epidemiology, and statistical genetics will also be considered.
Given their inherent uncertainty, protein modeling and molecular docking studies should be thoroughly validated. In the absence of experimental results for validation, the use of molecular dynamics simulations along with detailed free energy calculations, for example, should be used as complementary techniques to support the major conclusions. Submissions of premature modeling exercises without additional biological insights will not be considered.
Review articles will generally be commissioned by the editors and should not be submitted to the journal without explicit invitation. However prospective authors are welcome to send a brief (one to three pages) synopsis, which will be evaluated by the editors.