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.

中文翻译：

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从组学数据中识别癌症生物标志物的计算资源

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不同类型的组学数据，这可能会改善癌症患者的现状。这些数据在有效管理癌症和改善患者预后方面具有巨大的应用价值。本综述总结了各种计算资源和工具，其中包含与癌症相关的几种类型的组学数据。资源的主要分类包括——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、预后和预测性生物标志物工具，以及使用多组学数据的数据分析算法。该审查主要侧重于提供有关开源多组学工具和数据存储库的全面信息，因为它们具有更广泛的适用性、经济效益和可用性。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极大地造福于研究人员和临床医生，尤其是那些在生物信息学方面没有良好背景并且缺乏足够的数据分析技能来解释当今产生​​的大量癌症特异性数据中的某些东西的人。