Veith Thomas, Beck Richard, Tagal Vural, Li Tao, Alahmari Saeed, Cole Jackson, Hannaby Daniel, Kyei John, Yu Xiaoqing, Maksin Konstantin, Schultz Andrew, Lee HoJoon, ElNaqa Issam, Eschrich Steven, Ji Hanlee P, Diaz Aaron, Andor Noemi
Department of Integrated Mathematical Oncology, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
Department of Computer Science, Najran University, Najran 66462, Saudi Arabia.
bioRxiv. 2025 Jul 3:2025.05.07.652202. doi: 10.1101/2025.05.07.652202.
Understanding how genetic and phenotypic diversity emerges and evolves within cancer cell populations is a fundamental challenge in cancer biology. CLONEID is a novel framework designed to organize and analyze clone-specific measures as structured time-series data. By integrating and monitoring genotypic and phenotypic experimental data over time, CLONEID facilitates hypothesis-driven and hypothesis-generating research in cancer biology. This article outlines the development, utility, and applications of CLONEID, emphasizing its role in overcoming challenges in data reproducibility, mathematical modeling, and multi-modal data integration. A webportal to the CLONEID database is available at dev.cloneid.org.
了解癌细胞群体中遗传和表型多样性如何出现和演变是癌症生物学中的一项基本挑战。CLONEID是一个新颖的框架,旨在将克隆特异性测量组织和分析为结构化的时间序列数据。通过整合和监测一段时间内的基因型和表型实验数据,CLONEID有助于癌症生物学中基于假设的研究以及产生假设的研究。本文概述了CLONEID的开发、用途和应用,强调了其在克服数据可重复性、数学建模和多模态数据整合方面挑战中的作用。可通过dev.cloneid.org访问CLONEID数据库的网络门户。