Research Institute of Women's Health, Sookmyung Women's University, Seoul 04310, Republic of Korea.
Research Institute of Women's Health, Sookmyung Women's University, Seoul 04310, Republic of Korea; Department of Biological Sciences, Sookmyung Women's University, Seoul 04310, Republic of Korea.
Biochim Biophys Acta Rev Cancer. 2024 Jan;1879(1):189030. doi: 10.1016/j.bbcan.2023.189030. Epub 2023 Nov 25.
The availability of a large amount of multiomics data enables data-driven discovery studies on cancers. High-throughput data on mutations, gene/protein expression, immune scores (tumor-infiltrating cells), drug screening, and RNAi (shRNAs and CRISPRs) screening are major integrated components of patient samples and cell line datasets. Improvements in data access and user interfaces make it easy for general scientists to carry out their data mining practices on integrated multiomics data platforms without computational expertise. Here, we summarize the extent of data integration and functionality of several portals and software that provide integrated multiomics data mining platforms for all cancer studies. Recent progress includes programming interfaces (APIs) for customized data mining. Precalculated datasets assist noncomputational users in quickly browsing data associations. Furthermore, stand-alone software provides fast calculations and smart functions, guiding optimal sampling and filtering options for the easy discovery of significant data associations. These efforts improve the utility of cancer omics big data for noncomputational users at all levels of cancer research. In the present review, we aim to provide analytical information guiding general scientists to find and utilize data mining tools for their research.
大量多组学数据的可用性使得能够针对癌症进行数据驱动的发现研究。突变、基因/蛋白质表达、免疫评分（肿瘤浸润细胞）、药物筛选和 RNAi（shRNA 和 CRISPR）筛选的高通量数据是患者样本和细胞系数据集的主要集成组成部分。数据访问和用户界面的改进使得普通科学家无需计算专业知识即可轻松地在集成的多组学数据平台上进行数据挖掘实践。在这里，我们总结了几个提供用于所有癌症研究的集成多组学数据挖掘平台的门户和软件的数据集成程度和功能。最近的进展包括用于自定义数据挖掘的编程接口 (API)。预计算数据集可帮助非计算用户快速浏览数据关联。此外，独立软件提供快速计算和智能功能，指导最佳采样和过滤选项，以轻松发现有意义的数据关联。这些努力提高了癌症组学大数据对各级癌症研究的非计算用户的实用性。在本综述中，我们旨在提供分析信息，指导普通科学家找到并利用数据挖掘工具来进行他们的研究。
