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CusVarDB: A tool for building customized sample-specific variant protein database from next-generation sequencing datasets
Cancer genome sequencing studies have revealed a number of variants in coding regions of several genes. Some of these coding variants play an important role in activating specific pathways that drive proliferation. Coding variants present on cancer cell surfaces by the major histocompatibility compl...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
F1000 Research Limited
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7684676/ https://www.ncbi.nlm.nih.gov/pubmed/33274046 http://dx.doi.org/10.12688/f1000research.23214.2 |
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author | Kasaragod, Sandeep Mohanty, Varshasnata Tyagi, Ankur Behera, Santosh Kumar Patil, Arun H. Pinto, Sneha M. Prasad, T. S. Keshava Modi, Prashant Kumar Gowda, Harsha |
author_facet | Kasaragod, Sandeep Mohanty, Varshasnata Tyagi, Ankur Behera, Santosh Kumar Patil, Arun H. Pinto, Sneha M. Prasad, T. S. Keshava Modi, Prashant Kumar Gowda, Harsha |
author_sort | Kasaragod, Sandeep |
collection | PubMed |
description | Cancer genome sequencing studies have revealed a number of variants in coding regions of several genes. Some of these coding variants play an important role in activating specific pathways that drive proliferation. Coding variants present on cancer cell surfaces by the major histocompatibility complex serve as neo-antigens and result in immune activation. The success of immune therapy in patients is attributed to neo-antigen load on cancer cell surfaces. However, which coding variants are expressed at the protein level can’t be predicted based on genomic data. Complementing genomic data with proteomic data can potentially reveal coding variants that are expressed at the protein level. However, identification of variant peptides using mass spectrometry data is still a challenging task due to the lack of an appropriate tool that integrates genomic and proteomic data analysis pipelines. To overcome this problem, and for the ease of the biologists, we have developed a graphical user interface (GUI)-based tool called CusVarDB. We integrated variant calling pipeline to generate sample-specific variant protein database from next-generation sequencing datasets. We validated the tool with triple negative breast cancer cell line datasets and identified 423, 408, 386 and 361 variant peptides from BT474, MDMAB157, MFM223 and HCC38 datasets, respectively. |
format | Online Article Text |
id | pubmed-7684676 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | F1000 Research Limited |
record_format | MEDLINE/PubMed |
spelling | pubmed-76846762020-12-02 CusVarDB: A tool for building customized sample-specific variant protein database from next-generation sequencing datasets Kasaragod, Sandeep Mohanty, Varshasnata Tyagi, Ankur Behera, Santosh Kumar Patil, Arun H. Pinto, Sneha M. Prasad, T. S. Keshava Modi, Prashant Kumar Gowda, Harsha F1000Res Software Tool Article Cancer genome sequencing studies have revealed a number of variants in coding regions of several genes. Some of these coding variants play an important role in activating specific pathways that drive proliferation. Coding variants present on cancer cell surfaces by the major histocompatibility complex serve as neo-antigens and result in immune activation. The success of immune therapy in patients is attributed to neo-antigen load on cancer cell surfaces. However, which coding variants are expressed at the protein level can’t be predicted based on genomic data. Complementing genomic data with proteomic data can potentially reveal coding variants that are expressed at the protein level. However, identification of variant peptides using mass spectrometry data is still a challenging task due to the lack of an appropriate tool that integrates genomic and proteomic data analysis pipelines. To overcome this problem, and for the ease of the biologists, we have developed a graphical user interface (GUI)-based tool called CusVarDB. We integrated variant calling pipeline to generate sample-specific variant protein database from next-generation sequencing datasets. We validated the tool with triple negative breast cancer cell line datasets and identified 423, 408, 386 and 361 variant peptides from BT474, MDMAB157, MFM223 and HCC38 datasets, respectively. F1000 Research Limited 2020-11-16 /pmc/articles/PMC7684676/ /pubmed/33274046 http://dx.doi.org/10.12688/f1000research.23214.2 Text en Copyright: © 2020 Kasaragod S et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Software Tool Article Kasaragod, Sandeep Mohanty, Varshasnata Tyagi, Ankur Behera, Santosh Kumar Patil, Arun H. Pinto, Sneha M. Prasad, T. S. Keshava Modi, Prashant Kumar Gowda, Harsha CusVarDB: A tool for building customized sample-specific variant protein database from next-generation sequencing datasets |
title | CusVarDB: A tool for building customized sample-specific variant protein database from next-generation sequencing datasets |
title_full | CusVarDB: A tool for building customized sample-specific variant protein database from next-generation sequencing datasets |
title_fullStr | CusVarDB: A tool for building customized sample-specific variant protein database from next-generation sequencing datasets |
title_full_unstemmed | CusVarDB: A tool for building customized sample-specific variant protein database from next-generation sequencing datasets |
title_short | CusVarDB: A tool for building customized sample-specific variant protein database from next-generation sequencing datasets |
title_sort | cusvardb: a tool for building customized sample-specific variant protein database from next-generation sequencing datasets |
topic | Software Tool Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7684676/ https://www.ncbi.nlm.nih.gov/pubmed/33274046 http://dx.doi.org/10.12688/f1000research.23214.2 |
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