Journal: Genome Medicine

Article Title: Tumor-naïve ctDNA detection with deep learning-enhanced error suppression for sensitive mutation calling

doi: 10.1186/s13073-026-01694-y

Figure Lengend Snippet: Limitations of ultra deep sequencing for UMI based sequencing error correction on a custom CRC capture panel. A Shown is the average number of mutations covered by each panel according to respective panel size in kb as observed in the test subset of CRC patients ( n = 108), not used for TRON CRC panel design. Other panel include Agilent Custom Comprehensive Cancer Panel v2 (AGL), Burning Rock DX OncoScreen Plus (BRP), Integrated DNA Technologies xGen Pan-Cancer Panel (IDT), iGeneTech AIOnco-seq (IGT), Illumina TruSight Tumor 170 (ILM), QIAGEN Comprehensive cancer panel (QGN), Roche SeqCap EZ Choice custom PHC Panel (ROC), and Thermo Fisher Oncomine Comprehensive Assay v3 (TFS). B Histogram showing how patients of the CRC test dataset ( n = 108) distribute across different coverage levels ranging from not covered = 0 mutations to > = 10 mutations. C Estimated mean error rate by raw sequencing depths via down-sampling of sequencing reads for the three cell lines CL34, MOLT4, and PF382

Article Snippet: Synthetic cfDNA Pan-cancer Reference Standards were purchased from Twist Bioscience (WT and 5% VAF; 3.0 μg per tube [San Francisco, CA, US]).

Techniques: Sequencing, Sampling

Journal: Genome Medicine

Article Title: Tumor-naïve ctDNA detection with deep learning-enhanced error suppression for sensitive mutation calling

doi: 10.1186/s13073-026-01694-y

Figure Lengend Snippet: DeepES and RF classifier synergize for TWIST artificial ctDNA mutation detection. A Observed VAF in plasma cfDNA and matched PBMC DNA for tumor confirmed (blue) and not confirmed (green) mutations predicted in plasma cfDNA. B A schematic showing the steps of DEEPctMUT computational pipeline which includes: mapping to reference genome, UMI collapsing, candidate mutation generation, and three steps of error polishing. C The counts of true positive mutations (blue) and true negative positions (green) are shown for different observed VAF ranges in Twist spiked-in samples . D Recall, precision, and f1- score are shown for UMI (green), UMI + DeepES (blue), UMI + RF (red), UMI + DeepES + RF (grey), and UMI + DeepES + RF + matched PBMC (pink) across different VAF ranges

Article Snippet: Synthetic cfDNA Pan-cancer Reference Standards were purchased from Twist Bioscience (WT and 5% VAF; 3.0 μg per tube [San Francisco, CA, US]).

Techniques: Mutagenesis, Clinical Proteomics

Journal: Genome Medicine

Article Title: Tumor-naïve ctDNA detection with deep learning-enhanced error suppression for sensitive mutation calling

doi: 10.1186/s13073-026-01694-y

Figure Lengend Snippet: Our combined approach can accurately predict ctDNA in CRC patients. A Characteristics of 32 preoperative plasma samples derived from 31 CRC patients. For more details, see Additional file 1: Table S3. B cfDNA levels in ng/ml blood for healthy and CRC preoperative samples from primary and metastasized patients. C Comparison of mutation calls when applying different stages of the pipeline across 32 CRC patients. The left plot shows the number tumor- confirmed mutations, and the right plot shows tumor- unconfirmed mutations (a proxy for false positive) D Fragment length distribution of tumor confirmed, tumor unconfirmed, and healthy control mutations in plasma. E Called mutations of DEEPctMUT and DEEPctMUT without PBMC on a cohort of 22 CRC patients and 27 healthy controls. F Called mutations of DEEPctMUT, DEEPctMUT without PBMC, Avenio, and Avenio BAM files analyzed by PI DeepES and RF on an independent cohort of 10 CRC patients and 10 healthy controls according to two VAF ranges. G Patient-level performance of the DEEPctMUT, DEEPctMUT without PBMC, Avenio, and Avenio analyzed by PI DeepES and RF on 10 CRC patients and 10 healthy controls. For Avenio (with iDES), a VAF threshold of > 0.5% was applied. For all other pipelines, VAF > 0.03% was applied. H VAF with DEEPctMUT of mutations in metastatic and localized tumor samples

Article Snippet: Synthetic cfDNA Pan-cancer Reference Standards were purchased from Twist Bioscience (WT and 5% VAF; 3.0 μg per tube [San Francisco, CA, US]).

Techniques: Clinical Proteomics, Derivative Assay, Comparison, Mutagenesis, Control

Journal: bioRxiv

Article Title: Performance Evaluation of a Quantitative Metabolomics Workflow Incorporating Microchip Capillary Electrophoresis, Indexed Migration Time, and Single-Point External Calibration

doi: 10.64898/2026.07.10.737294

Figure Lengend Snippet: Performance of the end-to-end mCE-HRMS workflow across multiple biological matrices. (A) Principal components analysis (PCA) using 126 analytes with ≤50% CV in SPQC and ≤50% missing data across all measurements. Samples cluster by matrix type with no apparent inter-batch clustering; the SPQC (gray) plots at the centroid of all samples (PC1: 59.8%, PC2: 11.2%). (B) Metabolite coverage by sample type and metabolite class; the Library bar represents the full 325-compound target list. SPQC shows the broadest coverage due to contributions from all matrices. (C)Quantitative comparison of NIST SRM-1950 concentrations measured by the mCE-HRMS single-point calibration workflow versus published reference values from Mandal et al. (2025) for 64 metabolites in common (Deming slope = 0.862, pseudo-R 2 = 0.994). Cysteine (excluded from regression, asterisk) is elevated in the CE-MS data due to the addition of a reducing agent during sample preparation, which converts disulfide-bound cysteine to the free form.

Article Snippet: Metabolite reference standards for library construction were purchased from IROA Technologies (Ann Arbor, MI) and Cerilliant Corporation (Round Rock, TX).

Techniques: Comparison, Sample Prep