Biology subjects

Shlayem, R.

Publications and source records attributed to Shlayem, R..

3 recordsLinked to original sources

bioRxiv2026

Comparative analysis of Illumina and Ultima-Genomics sequencing for plasma cell-free small RNA profiling in pancreatic cancer

Plasma-derived cell-free small non-coding RNAs are promising non-invasive biomarkers for cancer detection and monitoring. However, variability in sequencing output limits standardization, and cross-platform performance for plasma small RNA profiling has not been systematically evaluated. Illumina short-read sequencing is the current standard, whereas the newcomer, Ultima-Genomics platform, has been less extensively studied for circulating small RNA in plasma. To directly compare platform performance, we sequenced plasma cell-free RNA from 39 patients with pancreatic cancer and 39 matched controls on both platforms. After filtering, Ultima-Genomics retained more mature microRNA reads, whereas Illumina achieved slightly higher enrichment efficiency and mapping rates. Despite these technical differences, both platforms produced concordant expression profiles, with strong cross-platform correlations for shared microRNAs and clear separation of cases and controls within each dataset. Differential expression analysis identified 14 significant microRNAs on both platforms with concordant directions of change, most of which are supported by pancreatic cancer databases. Pathway enrichment analysis highlighted signaling pathways implicated in pancreatic cancer, supporting the biological relevance of both shared and platform-specific signatures. These findings indicate that both Illumina and Ultima Genomics platforms are suitable for plasma small RNA profiling and capture biologically relevant signals in pancreatic cancer.

genomics↗

bioRxiv2026

Simulation-based Bayesian deep learning enables uncertainty-aware tumor fraction estimation in cell-free DNA

BackgroundEstimating tumor fraction from whole-genome cell-free DNA sequencing is critical for liquid biopsy, but is hampered by weak signals and baseline noise at low tumor fractions. Existing computational methods often require matched controls or large labeled datasets for training and lack uncertainty quantification. To address these gaps, we developed purNPE, a Bayesian deep-learning framework trained without labeled cancer cell-free DNA samples. Specifically, purNPE leverages a two-part generative model: one component simulates diverse tumor copy-number profiles based on evolutionary genealogies, while a second, data-driven component learns and replicates realistic sequencing background patterns from cancer-free cell-free DNA. By training a Neural Posterior Estimator on synthetic tumor profiles augmented with learned noise, purNPE performs amortized inference in milliseconds without needing a reference sample set at inference. ResultsIn a real-world pan-cancer cohort, purNPE achieved comparable performance with existing methods against orthogonal mutant-allele-fraction validation (MAE = 0.066). In silico and semi-synthetic experiments suggested analytical sensitivity around 1% tumor fraction under the evaluated conditions and showed strong classification accuracy in low tumor fractions (AUC = 0.98 for TF[≤] 3% versus controls). ConclusionsThis work provides a framework for using simulation-based inference to derive calibrated, uncertainty-aware TF estimates, offering a potential alternative to traditional data-dependent methods.

bioinformatics↗

bioRxiv2026

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.

bioinformatics↗