A standardized framework for robust fragmentomic feature extraction from cell-free DNA sequencing data

A standardized framework for robust fragmentomic feature extraction from cell-free DNA sequencing data

Abstract Fragmentomics features of cell-free DNA represent promising non-invasive biomarkers for cancer diagnosis. A lack of systematic evaluation of biases in feature quantification hinders the adoption of such applications. We compare features derived from whole-genome sequencing of ten healthy do...

Full description

Saved in:
Bibliographic Details
Main Authors: Haichao Wang, Paulius D. Mennea, Yu Kiu Elkie Chan, Zhao Cheng, Maria C. Neofytou, Arif Anwer Surani, Aadhitthya Vijayaraghavan, Emma-Jane Ditter, Richard Bowers, Matthew D. Eldridge, Dmitry S. Shcherbo, Christopher G. Smith, Florian Markowetz, Wendy N. Cooper, Tommy Kaplan, Nitzan Rosenfeld, Hui Zhao
Format: Article
Language:English
Published: BMC 2025-05-01
Series:Genome Biology
Subjects:
CfDNA
Fragmentomics
Cancer genomics
Feature extraction
Online Access:https://doi.org/10.1186/s13059-025-03607-5
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Abstract Fragmentomics features of cell-free DNA represent promising non-invasive biomarkers for cancer diagnosis. A lack of systematic evaluation of biases in feature quantification hinders the adoption of such applications. We compare features derived from whole-genome sequencing of ten healthy donors using nine library kits and ten data-processing routes and validated in 1182 plasma samples from published studies. Our results clarify the variations from library preparation and feature quantification methods. We design the Trim Align Pipeline and cfDNAPro R package as unified interfaces for data pre-processing, feature extraction, and visualization to standardize multi-modal feature engineering and integration for machine learning.
ISSN:1474-760X

Similar Items