aPEAch:表观基因组和转录组数据端到端分析自动化流水线

Biology Pub Date : 2024-07-02 DOI:10.3390/biology13070492
Panagiotis Xiropotamos, Foteini Papageorgiou, Haris Manousaki, Charalampos Sinnis, Charalabos Antonatos, Y. Vasilopoulos, Georgios K. Georgakilas
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引用次数: 0

摘要

随着新一代测序技术(NGS)的出现,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。所生成数据的数量及其分析的潜在复杂性凸显了对稳健易用的计算分析方法的需求,这种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 是一个自动化管道,可促进 DNA 和 RNA 测序分析(包括小 RNA 测序)的端到端分析,从评估输入样本文件的质量到利用生物数据中蕴含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,用户可以选择分析的路径和范围以及结果的表现形式。aPEAch 提供了各种样本指标,如质量控制报告、片段大小分布图和所有中间输出文件,使管道可以用不同的参数或算法重新执行,并提供可发表的可视化结果。此外,aPEAch 通过自动聚类优化和可视化,无缝整合了先进的无监督学习分析,从而为深入了解潜在的生物机制提供了宝贵的资料。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
aPEAch: Automated Pipeline for End-to-End Analysis of Epigenomic and Transcriptomic Data
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.
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