Journal: Genome Medicine

Article Title: Nanopore-based random genomic sampling for intraoperative molecular diagnosis

doi: 10.1186/s13073-025-01427-7

Figure Lengend Snippet: Prospective molecular analysis of diagnostically challenging brain tumors with iSCORED pipeline. a Shown is the incorporated iSCORED workflow applied during intraoperative morphology-based diagnosis. Additional 10–15 scrolls of tissue sections, each 5 μm thick, are prepared to extract gDNA for subsequent iSCORED library preparation. Either MinION or PromethION sequencing could be utilized (both with concurrent analysis during sequencing). The final output graphs comprise whole genome CNV, gene amplification regions, and methylation classification with quantitative confidence scores ( Z scores for gene amplification and calibrated scores for methylation classification). b Real-time intraoperative molecular diagnosis with precise timestamps recorded from tissue arrival to final reports in 15 diagnostically challenging brain tumors. The entire workflow could be completed within ~ 105 min. The morphology-based intraoperative diagnosis was compared to generated molecular results, including methylation classification and CNV results . The numbers within the brackets of methylation classification and oncogene amplification denote the calibrated scores of corresponding diagnoses and detected copy number, respectively. * Scores of different glioblastoma subtypes. PXA = pleomorphic xanthoastrocytoma c Sequencing data, including mapped fragments and identified CpG sites, obtained within the initial 18 min (PromethION flowcells). d A comparative analysis of Nanopore-based molecular assays, including genomic detection resolution, library preparation time, input genomic DNA quantity, and required sequencing duration, reveals the iSCORED-based assay as the only method to achieve genome-wide high-resolution CNV detection within the surgical window. SMURF = sampling molecules using re-ligated fragments . STORK = short-read transpore rapid karyotyping . WGS = whole genome sequencing

Article Snippet: A custom python script converted the bedfile to make it compatible with Rapid-CNS 2 which processes the methylation information using a random forest classifier trained on Illumina BeadChip 450 K methylation array from the Heidelberg reference cohort of brain tumor methylation profiles [ ].

Techniques: Biomarker Discovery, Sequencing, Amplification, Methylation, Generated, Genome Wide, Sampling

Journal: Genome Medicine

Article Title: Nanopore-based random genomic sampling for intraoperative molecular diagnosis

doi: 10.1186/s13073-025-01427-7

Figure Lengend Snippet: Concurrent methylation analysis of primary CNV tumors. a Acquired methylation classification features with iSCORED-processed MinION sequencing over time using the Sturgeon and Rapid CNS. b Comprehensive comparison of Rapid CNS and Sturgeon methylation classification for primary brain tumors across multiple time points from the initiation of sequencing. c In silico mixture of glioblastoma, medulloblastoma, and oligodendrogliomas with control brain tissue dataset at various ratios using the Sturgeon (T2T) and Rapid CNS (hg38) (total data quantity after 1 h of sequencing). d Exact amplified regions covering EGFR oncogene in glioblastoma samples. e Methylation characterization of amplified EGFR oncogene reveals promoter hypomethylation. Subep = subependymoma, oligo = oligodendroglioma, medullo = medulloblastoma, GBM = glioblastoma

Article Snippet: A custom python script converted the bedfile to make it compatible with Rapid-CNS 2 which processes the methylation information using a random forest classifier trained on Illumina BeadChip 450 K methylation array from the Heidelberg reference cohort of brain tumor methylation profiles [ ].

Techniques: Methylation, Sequencing, Comparison, In Silico, Control, Amplification

Journal: Emerging Infectious Diseases

Article Title: Disease-Associated Streptococcus pneumoniae Genetic Variation

doi: 10.3201/eid3001.221927

Figure Lengend Snippet: Resubstitution estimate and cross-validation results based on random forest models used in study of disease-associated Streptococcus pneumoniae genetic variation*

Article Snippet: For the additional validation analysis that used the best RF classifier k-mer (in srtG1 ), 2 independent datasets of S. pneumoniae genomes with genotype distribution similar to that of our study were available on the National Center for Biotechnology Information Assembly database ( https://www.ncbi.nlm.nih.gov/assembly (data1: 60 noninvasive vs. 60 carriage isolates; data2: 60 invasive versus 60 carriage isolates; the prevalence of the predominant GPSCs [GPSC1 and GPSC321] was 58.3% for noninvasive, 55.0% for invasive and 30.0% for carriage isolates) ( Table 4).

Techniques: