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MutaXome: A Novel Database for Identified Somatic Variations of In silico Analyzed Cancer Exome Datasets

Advancements in the field of cancer research have enabled researchers and clinicians to access a massive amount of data to aid cancer patients and to add to the existing knowledge of research. However, despite the existence of reliable sources for extricating this data, it remains a challenge to acc...

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Autores principales: Padmavathi, P, Chandrashekar, K, Setlur, Anagha S, Niranjan, Vidya
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9109167/
https://www.ncbi.nlm.nih.gov/pubmed/35586731
http://dx.doi.org/10.1177/11769351221097593
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author Padmavathi, P
Chandrashekar, K
Setlur, Anagha S
Niranjan, Vidya
author_facet Padmavathi, P
Chandrashekar, K
Setlur, Anagha S
Niranjan, Vidya
author_sort Padmavathi, P
collection PubMed
description Advancements in the field of cancer research have enabled researchers and clinicians to access a massive amount of data to aid cancer patients and to add to the existing knowledge of research. However, despite the existence of reliable sources for extricating this data, it remains a challenge to accurately comprehend and draw conclusions based on the entirety of available information. Therefore, the current study aimed to design and develop a database for the identified variants of 5 different cancer types using 20 different cancer exomes. The exome data were retrieved from NCBI SRA and an NGS data clean-up protocol was implemented to obtain the best quality reads. The reads which passed the quality checks were then used for calling the variants which were then processed and filtered. This data was used to normalize and the normalized data generated was used for developing the database. MutaXome, which stands for mutations in cancer exome was designed in SQL, with the front end in bootstrap and HTML, and backend in PHP. The normalized data containing the variants inclusive of Single Nucleotide Polymorphisms (SNPs), were added into MutaXome, which contains detailed information regarding each type of identified variant. This database, available online via http://www.vidyalab.rf.gd/, serves as a knowledge base for cancer exome variations and holds much potential for enriching it by linking it to a decision support system as prospective studies.
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spelling pubmed-91091672022-05-17 MutaXome: A Novel Database for Identified Somatic Variations of In silico Analyzed Cancer Exome Datasets Padmavathi, P Chandrashekar, K Setlur, Anagha S Niranjan, Vidya Cancer Inform Original Research Advancements in the field of cancer research have enabled researchers and clinicians to access a massive amount of data to aid cancer patients and to add to the existing knowledge of research. However, despite the existence of reliable sources for extricating this data, it remains a challenge to accurately comprehend and draw conclusions based on the entirety of available information. Therefore, the current study aimed to design and develop a database for the identified variants of 5 different cancer types using 20 different cancer exomes. The exome data were retrieved from NCBI SRA and an NGS data clean-up protocol was implemented to obtain the best quality reads. The reads which passed the quality checks were then used for calling the variants which were then processed and filtered. This data was used to normalize and the normalized data generated was used for developing the database. MutaXome, which stands for mutations in cancer exome was designed in SQL, with the front end in bootstrap and HTML, and backend in PHP. The normalized data containing the variants inclusive of Single Nucleotide Polymorphisms (SNPs), were added into MutaXome, which contains detailed information regarding each type of identified variant. This database, available online via http://www.vidyalab.rf.gd/, serves as a knowledge base for cancer exome variations and holds much potential for enriching it by linking it to a decision support system as prospective studies. SAGE Publications 2022-05-13 /pmc/articles/PMC9109167/ /pubmed/35586731 http://dx.doi.org/10.1177/11769351221097593 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Original Research
Padmavathi, P
Chandrashekar, K
Setlur, Anagha S
Niranjan, Vidya
MutaXome: A Novel Database for Identified Somatic Variations of In silico Analyzed Cancer Exome Datasets
title MutaXome: A Novel Database for Identified Somatic Variations of In silico Analyzed Cancer Exome Datasets
title_full MutaXome: A Novel Database for Identified Somatic Variations of In silico Analyzed Cancer Exome Datasets
title_fullStr MutaXome: A Novel Database for Identified Somatic Variations of In silico Analyzed Cancer Exome Datasets
title_full_unstemmed MutaXome: A Novel Database for Identified Somatic Variations of In silico Analyzed Cancer Exome Datasets
title_short MutaXome: A Novel Database for Identified Somatic Variations of In silico Analyzed Cancer Exome Datasets
title_sort mutaxome: a novel database for identified somatic variations of in silico analyzed cancer exome datasets
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9109167/
https://www.ncbi.nlm.nih.gov/pubmed/35586731
http://dx.doi.org/10.1177/11769351221097593
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