cfDNA UniFlow: a unified preprocessing pipeline for cell-free DNA data from liquid biopsies

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• Cell-free DNA (cfDNA) is a crucial biomarker in liquid biopsies for research and diagnostics.
• Current cfDNA processing lacks a unified framework, hindering data integration and advanced analyses.
• Genetic and epigenetic information is often extracted from cfDNA in various clinical settings.

Purpose of the Study:

• To introduce cfDNA UniFlow, a standardized and automated workflow for cfDNA sample processing.
• To provide a scalable solution for cfDNA analysis from individual computers to cluster environments.
• To facilitate the universal application of innovative analysis strategies and data set integration.

Main Methods:

• Developed cfDNA UniFlow using Snakemake for scalable processing of cfDNA samples.
• Integrated methods for raw sequencing data processing, error correction, filtering, and quality control.
• Incorporated advanced techniques for copy number alteration detection and GC-bias correction.

Main Results:

• cfDNA UniFlow standardizes cfDNA processing, including bias correction and signal extraction.
• The workflow aggregates results and metrics into a unified report for downstream analysis.
• Provides methods for extracting, normalizing, and visualizing coverage signals in case-control studies.

Conclusions:

• An automated pipeline for liquid biopsy cfDNA processing is provided, enhancing research and clinical applications.
• The workflow's scalability and extensibility lay the groundwork for future cfDNA studies.
• Source code and documentation are publicly available on GitHub for broad accessibility.