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CytoGPS: a web-enabled karyotype analysis tool for cytogenetics
SUMMARY: Karyotype data are the most common form of genetic data that is regularly used clinically. They are collected as part of the standard of care in many diseases, particularly in pediatric and cancer medicine contexts. Karyotypes are represented in a unique text-based format, with a syntax def...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6954647/ https://www.ncbi.nlm.nih.gov/pubmed/31263896 http://dx.doi.org/10.1093/bioinformatics/btz520 |
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author | Abrams, Zachary B Zhang, Lin Abruzzo, Lynne V Heerema, Nyla A Li, Suli Dillon, Tom Rodriguez, Ricky Coombes, Kevin R Payne, Philip R O |
author_facet | Abrams, Zachary B Zhang, Lin Abruzzo, Lynne V Heerema, Nyla A Li, Suli Dillon, Tom Rodriguez, Ricky Coombes, Kevin R Payne, Philip R O |
author_sort | Abrams, Zachary B |
collection | PubMed |
description | SUMMARY: Karyotype data are the most common form of genetic data that is regularly used clinically. They are collected as part of the standard of care in many diseases, particularly in pediatric and cancer medicine contexts. Karyotypes are represented in a unique text-based format, with a syntax defined by the International System for human Cytogenetic Nomenclature (ISCN). While human-readable, ISCN is not intrinsically machine-readable. This limitation has prevented the full use of complex karyotype data in discovery science use cases. To enhance the utility and value of karyotype data, we developed a tool named CytoGPS. CytoGPS first parses ISCN karyotypes into a machine-readable format. It then converts the ISCN karyotype into a binary Loss-Gain-Fusion (LGF) model, which represents all cytogenetic abnormalities as combinations of loss, gain, or fusion events, in a format that is analyzable using modern computational methods. Such data is then made available for comprehensive ‘downstream’ analyses that previously were not feasible. AVAILABILITY AND IMPLEMENTATION: Freely available at http://cytogps.org. |
format | Online Article Text |
id | pubmed-6954647 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-69546472020-01-16 CytoGPS: a web-enabled karyotype analysis tool for cytogenetics Abrams, Zachary B Zhang, Lin Abruzzo, Lynne V Heerema, Nyla A Li, Suli Dillon, Tom Rodriguez, Ricky Coombes, Kevin R Payne, Philip R O Bioinformatics Applications Notes SUMMARY: Karyotype data are the most common form of genetic data that is regularly used clinically. They are collected as part of the standard of care in many diseases, particularly in pediatric and cancer medicine contexts. Karyotypes are represented in a unique text-based format, with a syntax defined by the International System for human Cytogenetic Nomenclature (ISCN). While human-readable, ISCN is not intrinsically machine-readable. This limitation has prevented the full use of complex karyotype data in discovery science use cases. To enhance the utility and value of karyotype data, we developed a tool named CytoGPS. CytoGPS first parses ISCN karyotypes into a machine-readable format. It then converts the ISCN karyotype into a binary Loss-Gain-Fusion (LGF) model, which represents all cytogenetic abnormalities as combinations of loss, gain, or fusion events, in a format that is analyzable using modern computational methods. Such data is then made available for comprehensive ‘downstream’ analyses that previously were not feasible. AVAILABILITY AND IMPLEMENTATION: Freely available at http://cytogps.org. Oxford University Press 2019-12-15 2019-07-02 /pmc/articles/PMC6954647/ /pubmed/31263896 http://dx.doi.org/10.1093/bioinformatics/btz520 Text en © The Author(s) 2019. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Applications Notes Abrams, Zachary B Zhang, Lin Abruzzo, Lynne V Heerema, Nyla A Li, Suli Dillon, Tom Rodriguez, Ricky Coombes, Kevin R Payne, Philip R O CytoGPS: a web-enabled karyotype analysis tool for cytogenetics |
title | CytoGPS: a web-enabled karyotype analysis tool for cytogenetics |
title_full | CytoGPS: a web-enabled karyotype analysis tool for cytogenetics |
title_fullStr | CytoGPS: a web-enabled karyotype analysis tool for cytogenetics |
title_full_unstemmed | CytoGPS: a web-enabled karyotype analysis tool for cytogenetics |
title_short | CytoGPS: a web-enabled karyotype analysis tool for cytogenetics |
title_sort | cytogps: a web-enabled karyotype analysis tool for cytogenetics |
topic | Applications Notes |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6954647/ https://www.ncbi.nlm.nih.gov/pubmed/31263896 http://dx.doi.org/10.1093/bioinformatics/btz520 |
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