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Analysis of rice nuclear-localized seed-expressed proteins and their database (RSNP-DB)
Nuclear proteins are primarily regulatory factors governing gene expression. Multiple factors determine the localization of a protein in the nucleus. An upright identification of nuclear proteins is way far from accuracy. We have attempted to combine information from subcellular prediction tools, ex...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7492263/ https://www.ncbi.nlm.nih.gov/pubmed/32934280 http://dx.doi.org/10.1038/s41598-020-70713-8 |
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author | Deveshwar, Priyanka Sharma, Shivam Prusty, Ankita Sinha, Neha Zargar, Sajad Majeed Karwal, Divya Parashar, Vishal Singh, Sanjeev Tyagi, Akhilesh Kumar |
author_facet | Deveshwar, Priyanka Sharma, Shivam Prusty, Ankita Sinha, Neha Zargar, Sajad Majeed Karwal, Divya Parashar, Vishal Singh, Sanjeev Tyagi, Akhilesh Kumar |
author_sort | Deveshwar, Priyanka |
collection | PubMed |
description | Nuclear proteins are primarily regulatory factors governing gene expression. Multiple factors determine the localization of a protein in the nucleus. An upright identification of nuclear proteins is way far from accuracy. We have attempted to combine information from subcellular prediction tools, experimental evidence, and nuclear proteome data to identify a reliable list of seed-expressed nuclear proteins in rice. Depending upon the number of prediction tools calling a protein nuclear, we could sort 19,441 seed expressed proteins into five categories. Of which, half of the seed-expressed proteins were called nuclear by at least one out of four prediction tools. Further, gene ontology (GO) enrichment and transcription factor composition analysis showed that 6116 seed-expressed proteins could be called nuclear with a greater assertion. Localization evidence from experimental data was available for 1360 proteins. Their analysis showed that a 92.04% accuracy of a nuclear call is valid for proteins predicted nuclear by at least three tools. Distribution of nuclear localization signals and nuclear export signals showed that the majority of category four members were nuclear resident proteins, whereas other categories have a low fraction of nuclear resident proteins and significantly higher constitution of shuttling proteins. We compiled all the above information for the seed-expressed genes in the form of a searchable database named Rice Seed Nuclear Protein DataBase (RSNP-DB) https://pmb.du.ac.in/rsnpdb. This information will be useful for comprehending the role of seed nuclear proteome in rice. |
format | Online Article Text |
id | pubmed-7492263 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-74922632020-09-16 Analysis of rice nuclear-localized seed-expressed proteins and their database (RSNP-DB) Deveshwar, Priyanka Sharma, Shivam Prusty, Ankita Sinha, Neha Zargar, Sajad Majeed Karwal, Divya Parashar, Vishal Singh, Sanjeev Tyagi, Akhilesh Kumar Sci Rep Article Nuclear proteins are primarily regulatory factors governing gene expression. Multiple factors determine the localization of a protein in the nucleus. An upright identification of nuclear proteins is way far from accuracy. We have attempted to combine information from subcellular prediction tools, experimental evidence, and nuclear proteome data to identify a reliable list of seed-expressed nuclear proteins in rice. Depending upon the number of prediction tools calling a protein nuclear, we could sort 19,441 seed expressed proteins into five categories. Of which, half of the seed-expressed proteins were called nuclear by at least one out of four prediction tools. Further, gene ontology (GO) enrichment and transcription factor composition analysis showed that 6116 seed-expressed proteins could be called nuclear with a greater assertion. Localization evidence from experimental data was available for 1360 proteins. Their analysis showed that a 92.04% accuracy of a nuclear call is valid for proteins predicted nuclear by at least three tools. Distribution of nuclear localization signals and nuclear export signals showed that the majority of category four members were nuclear resident proteins, whereas other categories have a low fraction of nuclear resident proteins and significantly higher constitution of shuttling proteins. We compiled all the above information for the seed-expressed genes in the form of a searchable database named Rice Seed Nuclear Protein DataBase (RSNP-DB) https://pmb.du.ac.in/rsnpdb. This information will be useful for comprehending the role of seed nuclear proteome in rice. Nature Publishing Group UK 2020-09-15 /pmc/articles/PMC7492263/ /pubmed/32934280 http://dx.doi.org/10.1038/s41598-020-70713-8 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Deveshwar, Priyanka Sharma, Shivam Prusty, Ankita Sinha, Neha Zargar, Sajad Majeed Karwal, Divya Parashar, Vishal Singh, Sanjeev Tyagi, Akhilesh Kumar Analysis of rice nuclear-localized seed-expressed proteins and their database (RSNP-DB) |
title | Analysis of rice nuclear-localized seed-expressed proteins and their database (RSNP-DB) |
title_full | Analysis of rice nuclear-localized seed-expressed proteins and their database (RSNP-DB) |
title_fullStr | Analysis of rice nuclear-localized seed-expressed proteins and their database (RSNP-DB) |
title_full_unstemmed | Analysis of rice nuclear-localized seed-expressed proteins and their database (RSNP-DB) |
title_short | Analysis of rice nuclear-localized seed-expressed proteins and their database (RSNP-DB) |
title_sort | analysis of rice nuclear-localized seed-expressed proteins and their database (rsnp-db) |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7492263/ https://www.ncbi.nlm.nih.gov/pubmed/32934280 http://dx.doi.org/10.1038/s41598-020-70713-8 |
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