Brief Funct Genomics. 2021 Jul 17;20(4):213-222. doi: 10.1093/bfgp/elab021.
Cancer is one of the most prevailing, deadly and challenging diseases worldwide. The advancement in technology led to the generation of different types of omics data at each genome level that may potentially improve the current status of cancer patients. These data have tremendous applications in managing cancer effectively with improved outcome in patients. This review summarizes the various computational resources and tools housing several types of omics data related to cancer. Major categorization of resources includes-cancer-associated multiomics data repositories, visualization/analysis tools for omics data, machine learning-based diagnostic, prognostic, and predictive biomarker tools, and data analysis algorithms employing the multiomics data. The review primarily focuses on providing comprehensive information on the open-source multiomics tools and data repositories, owing to their broader applicability, economic-benefit and usability. Sections including the comparative analysis, tools applicability and possible future directions have also been discussed in detail. We hope that this information will significantly benefit the researchers and clinicians, especially those with no sound background in bioinformatics and who lack sufficient data analysis skills to interpret something from the plethora of cancer-specific data generated nowadays.
癌症是全球最普遍、最致命和最具挑战性的疾病之一。技术的进步导致在每个基因组水平上产生了不同类型的组学数据，这些数据可能会改善癌症患者的现状。这些数据在有效管理癌症方面具有巨大的应用价值，可以改善患者的预后。 本综述总结了与癌症相关的多种组学数据的各种计算资源和工具。资源的主要分类包括与癌症相关的多组学数据存储库、组学数据的可视化/分析工具、基于机器学习的诊断、预后和预测生物标志物工具，以及利用多组学数据的数据分析算法。由于其更广泛的适用性、经济效益和可用性，本综述主要侧重于提供有关开源多组学工具和数据存储库的综合信息。还详细讨论了比较分析、工具适用性和可能的未来方向等部分。我们希望这些信息将对研究人员和临床医生，特别是那些没有坚实的生物信息学背景且缺乏足够的数据分析技能来从当今生成的大量特定于癌症的数据中解读信息的人，产生重大影响。
