Department of Computer Science, Georgia State University, Atlanta, GA, USA.
Department of Mathematics and Statistics, Georgia State University, Atlanta, GA, USA.
Methods Mol Biol. 2024;2812:39-46. doi: 10.1007/978-1-0716-3886-6_3.
In this chapter, we outline an approach to analyzing metatranscriptomic data, focusing on the assessment of differential enzyme expression and metabolic pathway activities using a novel bioinformatics software tool, EMPathways2. The analysis pipeline commences with raw data originating from a sequencer and concludes with an output of enzyme expressions and an estimate of metabolic pathway activities. The initial step involves aligning specific transcriptomes assembled from RNA-Seq data using Bowtie2 and acquiring gene expression data with IsoEM2. Subsequently, the pipeline proceeds to quality assessment and preprocessing of the input data, ensuring accurate estimates of enzymes and their differential regulation. Upon completion of the preprocessing stage, EMPathways2 is employed to decipher the intricate relationships between genes, enzymes, and pathways. An online repository containing sample data has been made available, alongside custom Python scripts designed to modify the output of the programs within the pipeline for diverse downstream analyses. This chapter highlights the technical aspects and practical applications of using EMPathways2, which facilitates the advancement of transcriptome data analysis and contributes to a deeper understanding of the complex regulatory mechanisms underlying living systems.
在本章中，我们概述了一种分析宏转录组数据的方法，重点介绍了使用新型生物信息学软件工具 EMPathways2 评估差异酶表达和代谢途径活性。该分析管道始于源自测序仪的原始数据，以酶表达和代谢途径活性的估计作为输出结束。该流程的第一步涉及使用 Bowtie2 对齐特定的转录组，这些转录组是从 RNA-Seq 数据组装而来的，并使用 IsoEM2 获取基因表达数据。然后，管道会对输入数据进行质量评估和预处理，以确保酶及其差异调控的准确估计。在预处理阶段完成后，EMPathways2 用于破译基因、酶和途径之间错综复杂的关系。我们提供了一个包含示例数据的在线存储库，以及设计用于修改管道内程序输出的自定义 Python 脚本，以便进行各种下游分析。本章重点介绍了使用 EMPathways2 的技术方面和实际应用，这有助于推进转录组数据分析，并有助于深入了解生命系统中复杂的调控机制。
