Supplementary Material for: Computational Prediction of the Global Functional Genomic Landscape: Applications, Methods, and Challenges

Technological advances have led to an explosive growth of high-throughput functional genomic data. Exploiting the correlation among different data types, it is possible to predict one functional genomic data type from other data types. Prediction tools are valuable in understanding the relationship among different functional genomic signals. They also provide a cost-efficient solution to inferring the unknown functional genomic profiles when experimental data are unavailable due to resource or technological constraints. The predicted data may be used for generating hypotheses, prioritizing targets, interpreting disease variants, facilitating data integration, quality control, and many other purposes. This article reviews various applications of prediction methods in functional genomics, discusses analytical challenges, and highlights some common and effective strategies used to develop prediction methods for functional genomic data.

技术进步推动了高通量功能基因组学数据（high-throughput functional genomic data）的爆发式增长。借助不同数据类型间的关联关系，我们能够基于其他功能基因组数据类型预测目标功能基因组数据类型。预测工具对于解析不同功能基因组信号（functional genomic signals）间的关联具有重要价值；当因资源或技术限制无法获取实验数据时，这类工具还能为未知功能基因组谱（functional genomic profiles）的推断提供经济高效的解决方案。预测得到的数据可用于生成研究假说、筛选优先研究靶点、解读疾病变异、助力数据整合与质量控制等诸多场景。本文综述了预测方法在功能基因组学（functional genomics）中的各类应用，探讨了相关分析挑战，并重点介绍了用于开发功能基因组数据预测方法的若干通用且高效的策略。