Poh, Z. W. | bioRxiv Science

bioRxiv2025

A Computational Framework for Analysis of cfDNA Fragmentation Profiles

Circulating cell-free DNA (cfDNA) has emerged as a promising non-invasive medium for studying tumor molecular profiles. Non-random fragmentation patterns in plasma cfDNA, particularly around nucleosome-depleted regions (NDRs) near transcription start sites (TSS), have been shown to reflect epigenetic regulation and gene expression. In this study, coverage profiles of the NDR were utilized to derive an NDR score, which was subsequently used as a proxy for inferring gene expression. To reduce transcript-to-transcript variability and enhance the clarity of these expression-associated signals, we implement a method for GC-bias correction of cfDNA samples. A computational framework (NDRDiff) was then developed to enable comparative analyses of NDR score profiles across different sample groups. The GC-bias correction preserved the overall trend of the NDR signal while improving the separation of gene expression levels, as demonstrated by comparisons of healthy donor cfDNA samples with matched blood RNA-seq data. Validation on a simulated dataset showed that NDRDiff achieved an area under the precision-recall curve (AUPRC) of 0.916, outperforming a standard t-test (AUPRC of 0.777). When applied to a comparison of healthy donor cfDNA and metastatic colorectal cancer (mCRC) cfDNA, NDRDiff identified 531 differential NDR score (DNS) genes that facilitated clear separation between the two groups. These DNS genes were found to correlate with tumor fraction estimates (down-regulated DNS genes: Pearson R = 0.89, p < 0.05; up-regulated DNS genes: Pearson R = -0.88, p < 0.05) and included CLDN4, BIN2, and IRAG2, which exhibit strong associations with colorectal cancer or blood cell expression signatures. Gene set enrichment analysis further revealed enrichment of colon and other gastrointestinal tissue signatures. Collectively, these findings underscore the potential of NDR-based cfDNA analysis as a minimally invasive tool for monitoring tumor-related molecular features in cancer.

cancer biology↗

bioRxiv2024

GCfix: A Fast and Accurate Fragment Length-SpecificMethod for Correcting GC Bias in Cell-Free DNA

MotivationCell-free DNA (cfDNA) analysis has wide-ranging clinical applications due to its non-invasive nature. However, cfDNA fragmentomics and copy number analysis can be complicated by GC bias. There is a lack of GC correction software based on rigorous cfDNA GC bias analysis. Furthermore, there is no standardized metric for comparing GC bias correction methods across large sample sets, nor a rigorous experiment setup to demonstrate their effectiveness on cfDNA data at various coverage levels. ResultsWe present GCfix, a method for robust GC bias correction in cfDNA data across diverse coverages. Developed following an in-depth analysis of cfDNA GC bias at the region and fragment length levels, GCfix is both fast and accurate. It works on all reference genomes and generates correction factors, tagged BAM files, and corrected coverage tracks. We also introduce two orthogonal performance metrics for (1) comparing the fragment count density distribution of GC content between expected and corrected samples, and (2) evaluating coverage profile improvement post-correction. GCfix outperforms existing cfDNA GC bias correction methods on these metrics. AvailabilityGCfix software and code for reproducing the figures are publicly accessible on GitHub: https://github.com/Rafeed-bot/GCfix_Software.

genomics↗