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Innovative in Silico Approaches for Characterization of Genes and Proteins
Bioinformatics is an amalgamation of biology, mathematics and computer science. It is a science which gathers the information from biology in terms of molecules and applies the informatic techniques to the gathered information for understanding and organizing the data in a useful manner. With the he...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9159363/ https://www.ncbi.nlm.nih.gov/pubmed/35664302 http://dx.doi.org/10.3389/fgene.2022.865182 |
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author | Bhat, Gh. Rasool Sethi, Itty Rah, Bilal Kumar, Rakesh Afroze, Dil |
author_facet | Bhat, Gh. Rasool Sethi, Itty Rah, Bilal Kumar, Rakesh Afroze, Dil |
author_sort | Bhat, Gh. Rasool |
collection | PubMed |
description | Bioinformatics is an amalgamation of biology, mathematics and computer science. It is a science which gathers the information from biology in terms of molecules and applies the informatic techniques to the gathered information for understanding and organizing the data in a useful manner. With the help of bioinformatics, the experimental data generated is stored in several databases available online like nucleotide database, protein databases, GENBANK and others. The data stored in these databases is used as reference for experimental evaluation and validation. Till now several online tools have been developed to analyze the genomic, transcriptomic, proteomics, epigenomics and metabolomics data. Some of them include Human Splicing Finder (HSF), Exonic Splicing Enhancer Mutation taster, and others. A number of SNPs are observed in the non-coding, intronic regions and play a role in the regulation of genes, which may or may not directly impose an effect on the protein expression. Many mutations are thought to influence the splicing mechanism by affecting the existing splice sites or creating a new sites. To predict the effect of mutation (SNP) on splicing mechanism/signal, HSF was developed. Thus, the tool is helpful in predicting the effect of mutations on splicing signals and can provide data even for better understanding of the intronic mutations that can be further validated experimentally. Additionally, rapid advancement in proteomics have steered researchers to organize the study of protein structure, function, relationships, and dynamics in space and time. Thus the effective integration of all of these technological interventions will eventually lead to steering up of next-generation systems biology, which will provide valuable biological insights in the field of research, diagnostic, therapeutic and development of personalized medicine. |
format | Online Article Text |
id | pubmed-9159363 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-91593632022-06-02 Innovative in Silico Approaches for Characterization of Genes and Proteins Bhat, Gh. Rasool Sethi, Itty Rah, Bilal Kumar, Rakesh Afroze, Dil Front Genet Genetics Bioinformatics is an amalgamation of biology, mathematics and computer science. It is a science which gathers the information from biology in terms of molecules and applies the informatic techniques to the gathered information for understanding and organizing the data in a useful manner. With the help of bioinformatics, the experimental data generated is stored in several databases available online like nucleotide database, protein databases, GENBANK and others. The data stored in these databases is used as reference for experimental evaluation and validation. Till now several online tools have been developed to analyze the genomic, transcriptomic, proteomics, epigenomics and metabolomics data. Some of them include Human Splicing Finder (HSF), Exonic Splicing Enhancer Mutation taster, and others. A number of SNPs are observed in the non-coding, intronic regions and play a role in the regulation of genes, which may or may not directly impose an effect on the protein expression. Many mutations are thought to influence the splicing mechanism by affecting the existing splice sites or creating a new sites. To predict the effect of mutation (SNP) on splicing mechanism/signal, HSF was developed. Thus, the tool is helpful in predicting the effect of mutations on splicing signals and can provide data even for better understanding of the intronic mutations that can be further validated experimentally. Additionally, rapid advancement in proteomics have steered researchers to organize the study of protein structure, function, relationships, and dynamics in space and time. Thus the effective integration of all of these technological interventions will eventually lead to steering up of next-generation systems biology, which will provide valuable biological insights in the field of research, diagnostic, therapeutic and development of personalized medicine. Frontiers Media S.A. 2022-05-18 /pmc/articles/PMC9159363/ /pubmed/35664302 http://dx.doi.org/10.3389/fgene.2022.865182 Text en Copyright © 2022 Bhat, Sethi, Rah, Kumar and Afroze. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics Bhat, Gh. Rasool Sethi, Itty Rah, Bilal Kumar, Rakesh Afroze, Dil Innovative in Silico Approaches for Characterization of Genes and Proteins |
title | Innovative in Silico Approaches for Characterization of Genes and Proteins |
title_full | Innovative in Silico Approaches for Characterization of Genes and Proteins |
title_fullStr | Innovative in Silico Approaches for Characterization of Genes and Proteins |
title_full_unstemmed | Innovative in Silico Approaches for Characterization of Genes and Proteins |
title_short | Innovative in Silico Approaches for Characterization of Genes and Proteins |
title_sort | innovative in silico approaches for characterization of genes and proteins |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9159363/ https://www.ncbi.nlm.nih.gov/pubmed/35664302 http://dx.doi.org/10.3389/fgene.2022.865182 |
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