CytoGPS: A web-enabled karyotype analysis tool for cytogenetics

Zachary B. Abrams, Lin Zhang, Lynne V. Abruzzo, Nyla A. Heerema, Suli Li, Tom Dillon, Ricky Rodriguez, Kevin R. Coombes, Philip R.O. Payne, Jonathan Wren

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish
Pages (from-to)5365-5366
Number of pages2
JournalBioinformatics
Volume35
Issue number24
DOIs
StatePublished - Dec 15 2019

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    Abrams, Z. B., Zhang, L., Abruzzo, L. V., Heerema, N. A., Li, S., Dillon, T., Rodriguez, R., Coombes, K. R., Payne, P. R. O., & Wren, J. (2019). CytoGPS: A web-enabled karyotype analysis tool for cytogenetics. Bioinformatics, 35(24), 5365-5366. https://doi.org/10.1093/bioinformatics/btz520