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SeqWiz: a modularized toolkit for next-generation protein sequence database management and analysis
BACKGROUND: Current proteomic technologies are fast-evolving to uncover the complex features of sequence processes, variations and modifications. Thus, protein sequence database and the corresponding softwares should also be improved to solve this issue. RESULTS: We developed a state-of-the-art tool...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10189941/ https://www.ncbi.nlm.nih.gov/pubmed/37194023 http://dx.doi.org/10.1186/s12859-023-05334-9 |
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author | Zhang, Ping Wang, Min Zhou, Tao Chen, Daozhen |
author_facet | Zhang, Ping Wang, Min Zhou, Tao Chen, Daozhen |
author_sort | Zhang, Ping |
collection | PubMed |
description | BACKGROUND: Current proteomic technologies are fast-evolving to uncover the complex features of sequence processes, variations and modifications. Thus, protein sequence database and the corresponding softwares should also be improved to solve this issue. RESULTS: We developed a state-of-the-art toolkit (SeqWiz) for constructing next-generation sequence databases and performing proteomic-centric sequence analyses. First, we proposed two derived data formats: SQPD (a well-structured and high-performance local sequence database based on SQLite), and SET (an associated list of selected entries based on JSON). The SQPD format follows the basic standards of the emerging PEFF format, which also aims to facilitate the search of complex proteoform. The SET format is designed for generating subsets with with high-efficiency. These formats are shown to greatly outperform the conventional FASTA or PEFF formats in time and resource consumption. Then, we mainly focused on the UniProt knowledgebase and developed a collection of open-source tools and basic modules for retrieving species-specific databases, formats conversion, sequence generation, sequence filter, and sequence analysis. These tools are implemented by using the Python language and licensed under the GNU General Public Licence V3. The source codes and distributions are freely available at GitHub (https://github.com/fountao/protwiz/tree/main/seqwiz). CONCLUSIONS: SeqWiz is designed to be a collection of modularized tools, which is friendly to both end-users for preparing easy-to-use sequence databases as well as bioinformaticians for performing downstream sequence analysis. Besides the novel formats, it also provides compatible functions for handling the traditional text based FASTA or PEFF formats. We believe that SeqWiz will promote the implementing of complementary proteomics for data renewal and proteoform analysis to achieve precision proteomics. Additionally, it can also drive the improvement of proteomic standardization and the development of next-generation proteomic softwares. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-023-05334-9. |
format | Online Article Text |
id | pubmed-10189941 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-101899412023-05-18 SeqWiz: a modularized toolkit for next-generation protein sequence database management and analysis Zhang, Ping Wang, Min Zhou, Tao Chen, Daozhen BMC Bioinformatics Software BACKGROUND: Current proteomic technologies are fast-evolving to uncover the complex features of sequence processes, variations and modifications. Thus, protein sequence database and the corresponding softwares should also be improved to solve this issue. RESULTS: We developed a state-of-the-art toolkit (SeqWiz) for constructing next-generation sequence databases and performing proteomic-centric sequence analyses. First, we proposed two derived data formats: SQPD (a well-structured and high-performance local sequence database based on SQLite), and SET (an associated list of selected entries based on JSON). The SQPD format follows the basic standards of the emerging PEFF format, which also aims to facilitate the search of complex proteoform. The SET format is designed for generating subsets with with high-efficiency. These formats are shown to greatly outperform the conventional FASTA or PEFF formats in time and resource consumption. Then, we mainly focused on the UniProt knowledgebase and developed a collection of open-source tools and basic modules for retrieving species-specific databases, formats conversion, sequence generation, sequence filter, and sequence analysis. These tools are implemented by using the Python language and licensed under the GNU General Public Licence V3. The source codes and distributions are freely available at GitHub (https://github.com/fountao/protwiz/tree/main/seqwiz). CONCLUSIONS: SeqWiz is designed to be a collection of modularized tools, which is friendly to both end-users for preparing easy-to-use sequence databases as well as bioinformaticians for performing downstream sequence analysis. Besides the novel formats, it also provides compatible functions for handling the traditional text based FASTA or PEFF formats. We believe that SeqWiz will promote the implementing of complementary proteomics for data renewal and proteoform analysis to achieve precision proteomics. Additionally, it can also drive the improvement of proteomic standardization and the development of next-generation proteomic softwares. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-023-05334-9. BioMed Central 2023-05-17 /pmc/articles/PMC10189941/ /pubmed/37194023 http://dx.doi.org/10.1186/s12859-023-05334-9 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Software Zhang, Ping Wang, Min Zhou, Tao Chen, Daozhen SeqWiz: a modularized toolkit for next-generation protein sequence database management and analysis |
title | SeqWiz: a modularized toolkit for next-generation protein sequence database management and analysis |
title_full | SeqWiz: a modularized toolkit for next-generation protein sequence database management and analysis |
title_fullStr | SeqWiz: a modularized toolkit for next-generation protein sequence database management and analysis |
title_full_unstemmed | SeqWiz: a modularized toolkit for next-generation protein sequence database management and analysis |
title_short | SeqWiz: a modularized toolkit for next-generation protein sequence database management and analysis |
title_sort | seqwiz: a modularized toolkit for next-generation protein sequence database management and analysis |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10189941/ https://www.ncbi.nlm.nih.gov/pubmed/37194023 http://dx.doi.org/10.1186/s12859-023-05334-9 |
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