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Leibowitz, D.

Publications and source records attributed to Leibowitz, D..

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Exploring per-base quality scores as a surrogate marker of cell-free DNA fragmentome

Per-base quality scores are widely treated as technical metadata in next-generation sequencing. Here, we show that in rigorously controlled whole-genome sequencing of cell-free DNA, quality profiles may encode fragmentomic signals that enable classification of cancer samples against matched controls. Analyzing four independent batches (23 cancer samples: pancreatic and breast; 22 matched controls) sequenced in a within-lane regime and further normalized per flow-cell tile to reduce technical confounders, we demonstrate through unsupervised analysis that boundary-enriched dynamics captured in these quality scores consistently separate cancer from control samples. A leave-one-batch-out classifier trained on quality-derived scores achieved a pooled area under the curve of 0.81. Furthermore, we show that the quality-derived metric correlates with short-fragment enrichment and tumor-associated 5-end motifs, performing comparably to established, motif-based orthogonal methods. These results provide initial evidence that quality scores could serve as a low-cost, alignment-free biomarker for cfDNA-based cancer detection. Key PointsO_LIPBQS in rigorously controlled cfDNA whole-genome sequencing contain biologically informative fragmentomic signal rather than only technical noise C_LIO_LIBoundary-enriched quality dynamics distinguish cancer samples from matched controls across independent sequencing batches C_LIO_LIA leave-one-batch-out classifier based on PBQS-derived features achieved a pooled AUC of 0.81 across 23 cancer and 22 control samples C_LIO_LIThe PBQS-derived score correlates with short-fragment enrichment and tumor-associated 5' end motifs, supporting its value as a lightweight orthogonal biomarker for cfDNA cancer. C_LI Biographical NoteProf. Noam Shomron heads the Functional Genomics Laboratory at Tel Aviv Universitys Medical School, where his group studies genomics and bioinformatics with a focus on sequencing technologies and translational medicine.

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