Xiropotamos Panagiotis, Papageorgiou Foteini, Manousaki Haris, Sinnis Charalampos, Antonatos Charalabos, Vasilopoulos Yiannis, Georgakilas Georgios K
Laboratory of Genetics, Section of Genetics, Cell Biology and Development, Department of Biology, University of Patras, 26504 Patras, Greece.
Information Management Systems Institute, ATHENA Research Center, 15125 Marousi, Greece.
Biology (Basel). 2024 Jul 2;13(7):492. doi: 10.3390/biology13070492.
With the advent of next-generation sequencing (NGS), experimental techniques that capture the biological significance of DNA loci or RNA molecules have emerged as fundamental tools for studying the epigenome and transcriptional regulation on a genome-wide scale. The volume of the generated data and the underlying complexity regarding their analysis highlight the need for robust and easy-to-use computational analytic methods that can streamline the process and provide valuable biological insights. Our solution, aPEAch, is an automated pipeline that facilitates the end-to-end analysis of both DNA- and RNA-sequencing assays, including small RNA sequencing, from assessing the quality of the input sample files to answering meaningful biological questions by exploiting the rich information embedded in biological data. Our method is implemented in Python, based on a modular approach that enables users to choose the path and extent of the analysis and the representations of the results. The pipeline can process samples with single or multiple replicates in batches, allowing the ease of use and reproducibility of the analysis across all samples. aPEAch provides a variety of sample metrics such as quality control reports, fragment size distribution plots, and all intermediate output files, enabling the pipeline to be re-executed with different parameters or algorithms, along with the publication-ready visualization of the results. Furthermore, aPEAch seamlessly incorporates advanced unsupervised learning analyses by automating clustering optimization and visualization, thus providing invaluable insight into the underlying biological mechanisms.
随着下一代测序（NGS）技术的出现，能够捕捉DNA位点或RNA分子生物学意义的实验技术已成为在全基因组范围内研究表观基因组和转录调控的基本工具。所生成数据的量以及分析其潜在复杂性凸显了对强大且易于使用的计算分析方法的需求，这些方法可以简化流程并提供有价值的生物学见解。我们的解决方案aPEAch是一个自动化流程，它有助于对DNA测序和RNA测序检测进行端到端分析，包括小RNA测序，从评估输入样本文件的质量到通过利用生物数据中嵌入的丰富信息回答有意义的生物学问题。我们的方法是用Python实现的，基于模块化方法，使用户能够选择分析的路径和范围以及结果的表示形式。该流程可以批量处理单个或多个重复样本，从而使所有样本的分析易于使用且具有可重复性。aPEAch提供各种样本指标，如质量控制报告、片段大小分布图以及所有中间输出文件，使该流程能够使用不同的参数或算法重新执行，并能对结果进行可用于发表的可视化展示。此外，aPEAch通过自动执行聚类优化和可视化无缝整合了先进的无监督学习分析，从而为潜在的生物学机制提供了宝贵的见解。
