Yuan Hao, Mancuso Christopher A, Johnson Kayla, Braasch Ingo, Krishnan Arjun
Genetics and Genome Science Program; Ecology, Evolution, and Behavior Program, Michigan State University.
Department of Biostatistics & Informatics, University of Colorado Anschutz Medical Campus.
ArXiv. 2024 Aug 16:arXiv:2408.08503v1.
Research organisms provide invaluable insights into human biology and diseases, serving as essential tools for functional experiments, disease modeling, and drug testing. However, evolutionary divergence between humans and research organisms hinders effective knowledge transfer across species. Here, we review state-of-the-art methods for computationally transferring knowledge across species, primarily focusing on methods that utilize transcriptome data and/or molecular networks. We introduce the term "agnology" to describe the functional equivalence of molecular components regardless of evolutionary origin, as this concept is becoming pervasive in integrative data-driven models where the role of evolutionary origin can become unclear. Our review addresses four key areas of information and knowledge transfer across species: (1) transferring disease and gene annotation knowledge, (2) identifying agnologous molecular components, (3) inferring equivalent perturbed genes or gene sets, and (4) identifying agnologous cell types. We conclude with an outlook on future directions and several key challenges that remain in cross-species knowledge transfer.
实验生物为深入了解人类生物学和疾病提供了宝贵的见解，是功能实验、疾病建模和药物测试的重要工具。然而，人类与实验生物之间的进化差异阻碍了跨物种的有效知识转移。在此，我们综述了用于跨物种进行知识计算转移的最新方法，主要关注利用转录组数据和/或分子网络的方法。我们引入 “同源学” 一词来描述分子成分的功能等效性，而不考虑其进化起源，因为这一概念在整合数据驱动的模型中变得越来越普遍，在这些模型中进化起源的作用可能变得不明确。我们的综述涉及跨物种信息和知识转移的四个关键领域：（1）转移疾病和基因注释知识，（2）识别同源分子成分，（3）推断等效的受干扰基因或基因集，以及（4）识别同源细胞类型。我们最后展望了未来的方向以及跨物种知识转移中仍然存在的几个关键挑战。
