Claus L. Andersen

This paper introduces BBQ, a tool that improves rare somatic variant calling by modeling sample-specific error rates using overlapping reads, reducing false positives compared to existing tools.

PyDREAMS improves detection of low-frequency ctDNA by modeling SNV and indel errors in cfDNA sequencing data from colorectal cancer patients.

This paper improves ctDNA detection by combining mutation detection with fragmentomic features, achieving higher AUC scores than mutation burden alone.

The paper introduces ctDNAmer, a reference-free tool for detecting ctDNA from WGS data by analyzing k-mers, showing improved tumor fraction estimation and detection compared to traditional methods.

This paper introduces MRD-EDGE, a machine learning platform for ultrasensitive ctDNA detection in plasma WGS, showing improved sensitivity and broader applicability for MRD monitoring.

UMIseq detects circulating tumor DNA in colorectal cancer patients with high sensitivity and specificity, even at low allele frequencies.

DREAMS improves variant calling and circulating tumor DNA detection by modeling sequencing errors at the read level using deep learning.

This paper uses non-negative matrix factorization to analyze cfDNA fragment length distributions, showing it can infer tumor load, detect cancer, and identify open chromatin-associated signatures.