标题： MyESL：分子进化和系统发育分析中的稀疏学习 显示英文标题

标题： MyESL: Sparse learning in molecular evolution and phylogenetic analysis

摘要： 进化稀疏学习（ESL）使用监督机器学习方法，最小绝对收缩和选择算子（LASSO），来构建解释假设与序列比对中基因组特征（例如，位点）变化之间关系的模型。 ESL通过使用稀疏组LASSO，在基因组特征（例如，基因组位点）之间和内部引入稀疏性。 尽管有一些软件包可用于执行稀疏组LASSO，但我们发现它们不太适合处理和分析包含数百万个特征（如碱基）的基因组规模数据。 MyESL软件填补了用于进行ESL分析的开源软件的空白，提供了预处理输入假设和大型比对、使LASSO灵活且计算高效以及后处理输出模型以生成在功能或进化基因组学中有用的不同指标的功能。 MyESL可以将系统发育树和序列比对作为输入，并分别将其转换为数值响应和特征。 模型输出被处理成用户友好的文本和图形文件。 MyESL的计算核心用C++编写，这提供了具有或不具有组稀疏性的模型构建，而输入和模型输出的预处理和后处理则使用用Python编写的自定义函数完成。 它在系统发生基因组学中的一个应用展示了MyESL的实用性。 我们对经验基因组规模数据集的分析表明，MyESL可以在个人台式机上快速高效地构建进化模型，而其他计算包由于计算资源和时间的高昂要求而无法做到这一点。 MyESL适用于Linux上的Python环境，并作为独立应用程序分发给Windows和macOS。 它可以从https://github.com/kumarlabgit/MyESL获取。 显示英文摘要

摘要： Evolutionary sparse learning (ESL) uses a supervised machine learning approach, Least Absolute Shrinkage and Selection Operator (LASSO), to build models explaining the relationship between a hypothesis and the variation across genomic features (e.g., sites) in sequence alignments. ESL employs sparsity between and within the groups of genomic features (e.g., genomic loci) by using sparse-group LASSO. Although some software packages are available for performing sparse group LASSO, we found them less well-suited for processing and analyzing genome-scale data containing millions of features, such as bases. MyESL software fills the need for open-source software for conducting ESL analyses with facilities to pre-process the input hypotheses and large alignments, make LASSO flexible and computationally efficient, and post-process the output model to produce different metrics useful in functional or evolutionary genomics. MyESL can take phylogenetic trees and sequence alignments as input and transform them into numeric responses and features, respecetively. The model outputs are processed into user-friendly text and graphical files. The computational core of MyESL is written in C++, which offers model building with or without group sparsity, while the pre- and post-processing of inputs and model outputs is performed using customized functions written in Python. One of its applications in phylogenomics showcases the utility of MyESL. Our analysis of empirical genome-scale datasets shows that MyESL can build evolutionary models quickly and efficiently on a personal desktop, while other computational packages were unable due to their prohibitive requirements of computational resources and time. MyESL is available for Python environments on Linux and distributed as a standalone application for Windows and macOS. It is available from https://github.com/kumarlabgit/MyESL.