Zolotareva Olga, Kleine Maren
Bielefeld University, Faculty of Technology and Center for Biotechnology, International Research Training Group "Computational Methods for the Analysis of the Diversity and Dynamics of Genomes" and Genome Informatics, Universitätsstraße 25, Bielefeld, Germany.
Bielefeld University, Faculty of Technology, Bioinformatics/Medical Informatics Department, Universitätsstraße 25, Bielefeld, Germany.
J Integr Bioinform. 2019 Sep 9;16(4):20180069. doi: 10.1515/jib-2018-0069.
Modern high-throughput experiments provide us with numerous potential associations between genes and diseases. Experimental validation of all the discovered associations, let alone all the possible interactions between them, is time-consuming and expensive. To facilitate the discovery of causative genes, various approaches for prioritization of genes according to their relevance for a given disease have been developed. In this article, we explain the gene prioritization problem and provide an overview of computational tools for gene prioritization. Among about a hundred of published gene prioritization tools, we select and briefly describe 14 most up-to-date and user-friendly. Also, we discuss the advantages and disadvantages of existing tools, challenges of their validation, and the directions for future research.
现代高通量实验为我们提供了基因与疾病之间众多潜在的关联。对所有已发现的关联进行实验验证，更不用说它们之间所有可能的相互作用，既耗时又昂贵。为了促进致病基因的发现，已经开发了各种根据基因与特定疾病的相关性对基因进行优先级排序的方法。在本文中，我们解释了基因优先级排序问题，并概述了用于基因优先级排序的计算工具。在大约一百种已发表的基因优先级排序工具中，我们选择并简要描述了14种最新且用户友好的工具。此外，我们还讨论了现有工具的优缺点、验证它们所面临的挑战以及未来研究的方向。
