easyMF: A Web Platform for Matrix Factorization-based Biological Discovery from Large-scale Transcriptome Data

With the development of high-throughput experimental technologies, large-scale RNA sequencing (RNA-Seq) data have been and continue to be produced, but have led to challenges in extracting relevant biological knowledge hidden in the produced high-dimensional gene expression matrices. Here, we present easyMF, a user-friendly web platform that aims to facilitate biological discovery from large-scale transcriptome data through matrix factorization (MF). The easyMF platform enables users with little bioinformatics experience to streamline transcriptome analysis from raw reads to gene expression and to decompose expression matrix from thousands of genes to a handful of metagenes. easyMF also offers a series of functional modules for metagene-based exploratory analysis with an emphasis on functional gene discovery. As a modular, containerized and open-source platform, easyMF can be customized to satisfy users’ specific demands and deployed as a web server for broad applications. easyMF is freely available at https://github.com/cma2015/easyMF. We demonstrated the application of easyMF with four case studies using 940 RNA sequencing datasets from maize (<em>Zea mays </em>L<em>.</em>).随着高通量实验技术的发展，大规模RNA测序（RNA-Seq）数据已产生并仍在持续产出，但从其中挖掘隐藏于高维基因表达矩阵内的相关生物学知识仍面临诸多挑战。在此，我们介绍easyMF——一款用户友好型网络平台，旨在通过矩阵分解（MF）从大规模转录组数据中助力生物学发现。easyMF平台可帮助缺乏生物信息学经验的用户简化从原始测序读段到基因表达量的转录组分析全流程，并可将数千个基因的表达矩阵分解为少量元基因（metagene）。easyMF还提供一系列基于元基因的探索性分析功能模块，重点聚焦功能基因挖掘。作为一款模块化、容器化且开源的平台，easyMF可针对用户的特定需求进行定制，并可作为网络服务器部署以适配广泛的应用场景。easyMF可在https://github.com/cma2015/easyMF免费获取。我们利用玉米（Zea mays L.）的940套RNA测序数据集开展了四项案例研究，以此演示了easyMF的实际应用。