Diverse microbiome communities drive biogeochemical processes and evolution of animals in their ecosystems. Many microbiome projects have demonstrated the power of using metagenomics to understand the structures and factors influencing the function of the microbiomes in their environments. In order to characterize the effects from microbiome composition for human health, diseases, and even ecosystems, one must first understand the relationship of microbes and their environment in different samples. Running machine learning model with metagenomic sequencing data is encouraged for this purpose, but it is not an easy task to make an appropriate machine learning model for all diverse metagenomic datasets. We introduce MegaR, an R Shiny package and web application, to build an unbiased machine learning model effortlessly with interactive visual analysis. The MegaR employs taxonomic profiles from either whole metagenome sequencing or 16S rRNA sequencing data to develop machine learning models and classify the samples into two or more categories. It provides various options for model fine tuning throughout the analysis pipeline such as data processing, multiple machine learning techniques, model validation, and unknown sample prediction that can be used to achieve the highest prediction accuracy possible for any given dataset while still maintaining a user-friendly experience. Metagenomic sample classification and phenotype prediction is important particularly when it applies to a diagnostic method for identifying and predicting microbe-related human diseases. MegaR provides various interactive visualizations for user to build an accurate machine-learning model without difficulty. Unknown sample prediction with a properly trained model using MegaR will enhance researchers to identify the sample property in a fast turnaround time.

中文翻译：

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MegaR：一个交互式 R 包，用于使用宏基因组图谱和机器学习进行快速样本分类和表型预测

多样化的微生物群落驱动着生态系统中动物的生物地球化学过程和进化。许多微生物组项目已经证明了使用宏基因组学来了解影响环境中微生物组功能的结构和因素的力量。为了表征微生物组组成对人类健康、疾病甚至生态系统的影响，我们必须首先了解不同样本中微生物与其环境的关系。为此，鼓励使用宏基因组测序数据运行机器学习模型，但为所有不同的宏基因组数据集建立适当的机器学习模型并不是一件容易的事。我们引入 MegaR，一个 R Shiny 包和 Web 应用程序，通过交互式视觉分析轻松构建无偏见的机器学习模型。MegaR 利用来自整个宏基因组测序或 16S rRNA 测序数据的分类图谱来开发机器学习模型，并将样本分为两个或多个类别。它为整个分析流程中的模型微调提供了各种选项，例如数据处理、多种机器学习技术、模型验证和未知样本预测，可用于为任何给定数据集实现最高的预测精度，同时仍保持用户友好的经历。宏基因组样本分类和表型预测尤其重要，当它应用于识别和预测与微生物相关的人类疾病的诊断方法时。MegaR 提供各种交互式可视化，让用户轻松构建准确的机器学习模型。使用 MegaR 通过经过适当训练的模型来预测未知样本将增强研究人员在快速周转时间内识别样本属性的能力。