aPEAch: Automated Pipeline for End-to-End Analysis of Epigenomic and Transcriptomic Data.

Area of Science:

• Genomics
• Computational Biology
• Molecular Biology

Background:

• Next-generation sequencing (NGS) generates vast amounts of complex data for epigenome and transcriptional regulation studies.
• Analyzing this data requires robust, user-friendly computational methods to extract biological insights.

Purpose of the Study:

• To develop an automated, end-to-end computational pipeline for DNA and RNA sequencing data analysis.
• To streamline the process from raw data quality assessment to biological question answering.

Main Methods:

• The aPEAch pipeline is implemented in Python using a modular approach.
• It supports batch processing of samples with single or multiple replicates.
• Includes automated quality control, intermediate file generation, and publication-ready visualizations.

Main Results:

• aPEAch facilitates comprehensive analysis of DNA- and RNA-sequencing assays, including small RNA sequencing.
• The pipeline provides sample metrics, quality control reports, and fragment size distribution plots.
• Incorporates automated unsupervised learning for clustering optimization and visualization.

Conclusions:

• aPEAch offers an automated, reproducible, and flexible solution for genomic data analysis.
• It simplifies complex analyses, enabling researchers to gain valuable biological insights from sequencing data.
• The pipeline enhances the accessibility of advanced computational methods for epigenome and transcriptional regulation studies.