Campelo Dos Santos Andre Luiz, DeGiorgio Michael, Assis Raquel
Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431, United States.
Institute for Human Health and Disease Intervention, Florida Atlantic University, Boca Raton, FL 33431, United States.
Bioinform Adv. 2024 Jan 17;4(1):vbae002. doi: 10.1093/bioadv/vbae002. eCollection 2024.
Gene deletion is traditionally thought of as a nonadaptive process that removes functional redundancy from genomes, such that it generally receives less attention than duplication in evolutionary turnover studies. Yet, mounting evidence suggests that deletion may promote adaptation via the "less-is-more" evolutionary hypothesis, as it often targets genes harboring unique sequences, expression profiles, and molecular functions. Hence, predicting the relative prevalence of redundant and unique functions among genes targeted by deletion, as well as the parameters underlying their evolution, can shed light on the role of gene deletion in adaptation.
Here, we present CLOUDe, a suite of machine learning methods for predicting evolutionary targets of gene deletion events from expression data. Specifically, CLOUDe models expression evolution as an Ornstein-Uhlenbeck process, and uses multi-layer neural network, extreme gradient boosting, random forest, and support vector machine architectures to predict whether deleted genes are "redundant" or "unique", as well as several parameters underlying their evolution. We show that CLOUDe boasts high power and accuracy in differentiating between classes, and high accuracy and precision in estimating evolutionary parameters, with optimal performance achieved by its neural network architecture. Application of CLOUDe to empirical data from suggests that deletion primarily targets genes with unique functions, with further analysis showing these functions to be enriched for protein deubiquitination. Thus, CLOUDe represents a key advance in learning about the role of gene deletion in functional evolution and adaptation.
CLOUDe is freely available on GitHub (https://github.com/anddssan/CLOUDe).
传统上认为基因缺失是一个非适应性过程，它从基因组中去除功能冗余，因此在进化更替研究中，它通常比基因复制受到的关注更少。然而，越来越多的证据表明，基因缺失可能通过“少即是多”的进化假说促进适应性，因为它通常靶向具有独特序列、表达谱和分子功能的基因。因此，预测基因缺失靶向基因中冗余和独特功能的相对流行程度，以及它们进化的潜在参数，有助于阐明基因缺失在适应性中的作用。
在这里，我们展示了CLOUDe，这是一套用于从表达数据预测基因缺失事件进化靶点的机器学习方法。具体来说，CLOUDe将表达进化建模为奥恩斯坦 - 乌伦贝克过程，并使用多层神经网络、极端梯度提升、随机森林和支持向量机架构来预测缺失基因是“冗余的”还是“独特的”，以及它们进化的几个潜在参数。我们表明，CLOUDe在区分类别方面具有高功效和准确性，在估计进化参数方面具有高精度和精确性，其神经网络架构实现了最佳性能。将CLOUDe应用于来自[具体来源未给出]的实证数据表明，基因缺失主要靶向具有独特功能的基因，进一步分析表明这些功能在蛋白质去泛素化方面富集。因此，CLOUDe代表了在了解基因缺失在功能进化和适应性中的作用方面的一项关键进展。
CLOUDe可在GitHub（https://github.com/anddssan/CLOUDe）上免费获取。
