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IC4R-2.0: Rice Genome Reannotation Using Massive RNA-seq Data
Genome reannotation aims for complete and accurate characterization of gene models and thus is of critical significance for in-depth exploration of gene function. Although the availability of massive RNA-seq data provides great opportunities for gene model refinement, few efforts have been made to a...
Autores principales: | , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7646092/ https://www.ncbi.nlm.nih.gov/pubmed/32683045 http://dx.doi.org/10.1016/j.gpb.2018.12.011 |
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author | Sang, Jian Zou, Dong Wang, Zhennan Wang, Fan Zhang, Yuansheng Xia, Lin Li, Zhaohua Ma, Lina Li, Mengwei Xu, Bingxiang Liu, Xiaonan Wu, Shuangyang Liu, Lin Niu, Guangyi Li, Man Luo, Yingfeng Hu, Songnian Hao, Lili Zhang, Zhang |
author_facet | Sang, Jian Zou, Dong Wang, Zhennan Wang, Fan Zhang, Yuansheng Xia, Lin Li, Zhaohua Ma, Lina Li, Mengwei Xu, Bingxiang Liu, Xiaonan Wu, Shuangyang Liu, Lin Niu, Guangyi Li, Man Luo, Yingfeng Hu, Songnian Hao, Lili Zhang, Zhang |
author_sort | Sang, Jian |
collection | PubMed |
description | Genome reannotation aims for complete and accurate characterization of gene models and thus is of critical significance for in-depth exploration of gene function. Although the availability of massive RNA-seq data provides great opportunities for gene model refinement, few efforts have been made to adopt these precious data in rice genome reannotation. Here we reannotate the rice (Oryza sativa L. ssp. japonica) genome based on integration of large-scale RNA-seq data and release a new annotation system IC4R-2.0. In general, IC4R-2.0 significantly improves the completeness of gene structure, identifies a number of novel genes, and integrates a variety of functional annotations. Furthermore, long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) are systematically characterized in the rice genome. Performance evaluation shows that compared to previous annotation systems, IC4R-2.0 achieves higher integrity and quality, primarily attributable to massive RNA-seq data applied in genome annotation. Consequently, we incorporate the improved annotations into the Information Commons for Rice (IC4R), a database integrating multiple omics data of rice, and accordingly update IC4R by providing more user-friendly web interfaces and implementing a series of practical online tools. Together, the updated IC4R, which is equipped with the improved annotations, bears great promise for comparative and functional genomic studies in rice and other monocotyledonous species. The IC4R-2.0 annotation system and related resources are freely accessible at http://ic4r.org/. |
format | Online Article Text |
id | pubmed-7646092 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-76460922020-11-13 IC4R-2.0: Rice Genome Reannotation Using Massive RNA-seq Data Sang, Jian Zou, Dong Wang, Zhennan Wang, Fan Zhang, Yuansheng Xia, Lin Li, Zhaohua Ma, Lina Li, Mengwei Xu, Bingxiang Liu, Xiaonan Wu, Shuangyang Liu, Lin Niu, Guangyi Li, Man Luo, Yingfeng Hu, Songnian Hao, Lili Zhang, Zhang Genomics Proteomics Bioinformatics Database Genome reannotation aims for complete and accurate characterization of gene models and thus is of critical significance for in-depth exploration of gene function. Although the availability of massive RNA-seq data provides great opportunities for gene model refinement, few efforts have been made to adopt these precious data in rice genome reannotation. Here we reannotate the rice (Oryza sativa L. ssp. japonica) genome based on integration of large-scale RNA-seq data and release a new annotation system IC4R-2.0. In general, IC4R-2.0 significantly improves the completeness of gene structure, identifies a number of novel genes, and integrates a variety of functional annotations. Furthermore, long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) are systematically characterized in the rice genome. Performance evaluation shows that compared to previous annotation systems, IC4R-2.0 achieves higher integrity and quality, primarily attributable to massive RNA-seq data applied in genome annotation. Consequently, we incorporate the improved annotations into the Information Commons for Rice (IC4R), a database integrating multiple omics data of rice, and accordingly update IC4R by providing more user-friendly web interfaces and implementing a series of practical online tools. Together, the updated IC4R, which is equipped with the improved annotations, bears great promise for comparative and functional genomic studies in rice and other monocotyledonous species. The IC4R-2.0 annotation system and related resources are freely accessible at http://ic4r.org/. Elsevier 2020-04 2020-07-16 /pmc/articles/PMC7646092/ /pubmed/32683045 http://dx.doi.org/10.1016/j.gpb.2018.12.011 Text en © 2020 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Database Sang, Jian Zou, Dong Wang, Zhennan Wang, Fan Zhang, Yuansheng Xia, Lin Li, Zhaohua Ma, Lina Li, Mengwei Xu, Bingxiang Liu, Xiaonan Wu, Shuangyang Liu, Lin Niu, Guangyi Li, Man Luo, Yingfeng Hu, Songnian Hao, Lili Zhang, Zhang IC4R-2.0: Rice Genome Reannotation Using Massive RNA-seq Data |
title | IC4R-2.0: Rice Genome Reannotation Using Massive RNA-seq Data |
title_full | IC4R-2.0: Rice Genome Reannotation Using Massive RNA-seq Data |
title_fullStr | IC4R-2.0: Rice Genome Reannotation Using Massive RNA-seq Data |
title_full_unstemmed | IC4R-2.0: Rice Genome Reannotation Using Massive RNA-seq Data |
title_short | IC4R-2.0: Rice Genome Reannotation Using Massive RNA-seq Data |
title_sort | ic4r-2.0: rice genome reannotation using massive rna-seq data |
topic | Database |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7646092/ https://www.ncbi.nlm.nih.gov/pubmed/32683045 http://dx.doi.org/10.1016/j.gpb.2018.12.011 |
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