Transcriptome-guided annotation and functional classification of long non-coding RNAs in Arabidopsis thaliana

Scientific Reports, Journal Year: 2022, Volume and Issue: 12(1)

Published: Aug. 18, 2022

Abstract Long non-coding RNAs (lncRNAs) are a prominent class of eukaryotic regulatory genes. Despite the numerous available transcriptomic datasets, annotation plant lncRNAs remains based on dated annotations that have been historically carried over. We present substantially improved Arabidopsis thaliana lncRNAs, generated by integrating 224 transcriptomes in multiple tissues, conditions, and developmental stages. annotate 6764 lncRNA genes, including 3772 novel. characterize their tissue expression patterns find 1425 co-expressed with coding enriched functional categories such as chloroplast organization, photosynthesis, RNA regulation, transcription, root development. This transcription-guided constitutes valuable resource for studying biological processes they may regulate.

Language: Английский

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A survey of computational methods and databases for lncRNA-miRNA interaction prediction

IEEE/ACM Transactions on Computational Biology and Bioinformatics, Journal Year: 2023, Volume and Issue: 20(5), P. 2810 - 2826

Published: April 4, 2023

Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) are two prevalent in current research. They play critical regulatory roles the life processes of animals plants. Studies have shown that lncRNAs can interact with miRNAs to participate post-transcriptional processes, mainly involved regulating cancer development, metastatic progression, drug resistance. Additionally, these interactions significant effects on plant growth, responses biotic abiotic stresses. Deciphering potential relationships between may provide new insights into our understanding biological functions miRNAs, pathogenesis complex diseases. In contrast, gathering information lncRNA-miRNA (LMIs) through experiments is expensive time-consuming. With accumulation multi-omics data, computational models extremely attractive systematically exploring LMIs. To best knowledge, this first comprehensive review methods for identifying Specifically, we summarized available public databases predicting animal Second, comprehensively reviewed LMIs classified them categories, including network-based sequence-based methods. Third, analyzed standard evaluation metrics used LMI prediction. Finally, pointed out some problems study discuss future research directions. Relevant latest advances prediction a GitHub repository https://github.com/sheng-n/lncRNA-miRNA-interaction-methods, we'll keep it updated.

Language: Английский

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JustRNA: a database of plant long noncoding RNA expression profiles and functional network

Journal of Experimental Botany, Journal Year: 2023, Volume and Issue: 74(17), P. 4949 - 4958

Published: June 2, 2023

Abstract Long noncoding RNAs (lncRNAs) are regulatory involved in numerous biological processes. Many plant lncRNAs have been identified, but their mechanisms remain largely unknown. A resource that enables the investigation of lncRNA activity under various conditions is required because co-expression between and protein-coding genes may reveal effects lncRNAs. This study developed JustRNA, an expression profiling for The platform currently contains 1 088 565 annotations 80 species. In addition, it includes 3692 RNA-seq samples derived from 825 six model plants. Functional network reconstruction provides insight into roles Genomic association analysis microRNA target prediction can be employed to depict potential interactions with nearby microRNAs, respectively. Subsequent strengthen confidence among genes. Chromatin immunoprecipitation sequencing data transcription factors histone modifications were integrated JustRNA identify transcriptional regulation several researchers valuable a free accessed at http://JustRNA.itps.ncku.edu.tw.

Language: Английский

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Plant long non-coding RNAs: identification and analysis to unveil their physiological functions

Frontiers in Plant Science, Journal Year: 2023, Volume and Issue: 14

Published: Oct. 26, 2023

Eukaryotic genomes encode thousands of RNA molecules; however, only a minimal fraction is translated into proteins. Among the non-coding elements, long RNAs (lncRNAs) play important roles in diverse biological processes. LncRNAs are associated mainly with regulation expression genome; nonetheless, their study has just scratched surface. This somewhat due to lack widespread conservation at sequence level, addition relatively low and highly tissue-specific patterns, which makes exploration challenging, especially plant where few these molecules have been described completely. Recently published high-quality crop plants, along new computational tools, considered promising resources for studying plants. review briefly summarizes characteristics lncRNAs, presence conservation, different protocols find limitations protocols. Likewise, it describes physiological phenomena. We believe that lncRNAs can help design strategies reduce negative effect biotic abiotic stresses on yield plants and, future, create fruits vegetables improved nutritional content, higher amounts compounds positive effects human health, better organoleptic characteristics, longer postharvest shelf life.

Language: Английский

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Plant-LncPipe: a computational pipeline providing significant improvement in plant lncRNA identification

Horticulture Research, Journal Year: 2024, Volume and Issue: 11(4)

Published: Feb. 8, 2024

Long non-coding RNAs (lncRNAs) play essential roles in various biological processes, such as chromatin remodeling, post-transcriptional regulation, and epigenetic modifications. Despite their critical functions regulating plant growth, root development, seed dormancy, the identification of lncRNAs remains a challenge due to scarcity specific extensively tested methods. Most mainstream machine learning-based methods used for lncRNA were initially developed using human or other animal datasets, accuracy effectiveness predicting have not been fully evaluated exploited. To overcome this limitation, we retrained several models, including CPAT, PLEK, LncFinder, datasets compared performance with prediction tools CPC2, CNCI, RNAplonc, LncADeep. Retraining these models significantly improved performance, two LncFinder-plant CPAT-plant, alongside ensemble, emerged most suitable identification. This underscores importance model retraining tackling challenges associated Finally, pipeline (Plant-LncPipe) that incorporates an ensemble best-performing covers entire data analysis process, reads mapping, transcript assembly, identification, classification, origin, efficient plants. The pipeline, Plant-LncPipe, is available at: https://github.com/xuechantian/Plant-LncRNA-pipline.

Language: Английский

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