A comprehensive toolkit for analyzing cell-free DNA genomic sequencing data in liquid biopsy

Liquid biopsy via plasma cell-free DNA (cfDNA) is transforming precision medicine by enabling non-invasive insights into the genomic and epigenomic landscapes of disease, especially cancer. However, the increasing diversity of cfDNA-derived features and machine learning applications has outpaced the availability of unified, versatile computational tools. To bridge this gap, we present cfDNAanalyzer-a user-friendly toolkit for streamlined cfDNA analysis, integrating feature extraction, selection, and machine learning model construction. Designed mainly for researchers and clinicians with limited bioinformatics expertise, it supports multimodal integration, interpretable output, and automated preprocessing. Benchmarking against existing toolkits demonstrated that cfDNAanalyzer provides broader feature coverage, efficient runtime, and comparable or improved predictive accuracy across shared feature types. We demonstrate its utility using real-world cfDNA datasets, uncovering its capability to discover biologically meaningful signals and improve diagnostic performance through feature integration. By standardizing and accelerating cfDNA analysis, cfDNAanalyzer enables reproducible biomarker discovery and advances translational liquid biopsy research.