Work

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

Year: 2026

Type: article

Abstract: 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.

Source: iScience

DOI: 10.1016/j.isci.2026.115829

Authors: Junpeng Zhou, Keyao Zhu, Xiaoqian Huang, Jinqi Yuan, Yumei Li

Institutions: Soochow University, Peking University, Tsinghua University

Language: English

Topic: Cancer Genomics and Diagnostics

Subfield: Cancer Research

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,240