What happened
DNA methylation is an important epigenetic mechanism involved in gene regulation. Most methods focus on analysing DNA methylation averaged from multiple reads, yet these average methylation profiles obscure heterogeneity between individual DNA molecules and coordinated methylation states across loci. Native Oxford Nanopore Technology (ONT) sequencing directly captures long, native DNA molecules together with their base modifications, allowing methylation to be studied at single-molecule resolution. Here, we present a scalable framework for genome-wide methylation analysis using ONT sequencing at single-molecule resolution, focused on two features that site-level summaries cannot recover. First, it quantifies molecule-to-molecule heterogeneity in DNA methylation by detecting Variable Methylated Domains (VMDs) and Variable Methylated Regions (VMRs). Second, it identifies coordinated methylation, as co-methylated positions (CMPs) and regions (CMRs), from the states observed on the same individual DNA molecules. Using data from human LCL cells, we demonstrate that substantial molecule-level methylation heterogeneity is masked by site-level summaries. Co-methylation analysis reveals coordinated patterns between CpG sites and genomic regions, including shared and sex-specific patterns, uncovering methylation organisation not apparent from average methylation levels. We also show that CMPs can be used to detect TF-pairs that are predicted to have coordinated binding. This framework is integrated within the DMRcaller R/Bioconductor package. Update (2026-09-18): ACLU and CDT Urge Court to Stop Government from Punishing Anthropic for Important Advocacy on AI Guardrails - Center for Democracy and Technology. ACLU and CDT Urge Court to Stop Government from Punishing Anthropic for Important Advocacy on AI Guardrails Center for Democracy and Technology Update (2026-09-18): OpenLipid: a large language model workflow for targeted analysis of DIA mass spectrometry data in lipidomics . Mass spectrometry has become a central technology for lipidomics, with data-independent acquisition (DIA) enabling broad and reproducible sampling of lipid signals. However, the multiplexed fragment-ion spectra in DIA data complicate lipid identification. Here, we introduce OpenLipid, a large language model (LLM)-based workflow for targeted DIA lipidomics. Using assay libraries built from data-dependent acquisition (DDA) results, OpenLipid directly evaluates extracted ion chromatograms (XICs) from DIA data in a zero-shot setting to identify target lipid peaks and generate human-readable rationales for individual lipid identification decisions. We benchmarked OpenLipid against manual annotations across four datasets comprising human plasma and mouse feces analyzed in positive and negative ionization modes. The plasma assay libraries contained 199 target lipids in positive mode and 147 in negative mode. The fecal assay libraries contained 181 target lipids in positive mode and 264 in negative mode. At a 5% false discovery rate (FDR) threshold, Open Update (2026-09-20): SMORE: joint dimension reduction and cell population discovery on single-cell methylome data . Single-cell DNA methylation profiling technology captures novel epigenetic data modality but are challenging to analyze because of their heterogeneity, high dimensionality, and ultra-sparsity. Here we present SMORE (Single-cell MethylOme Reduction and Embedding), a computational method for joint dimensionality reduction and cell population discovery dedicated to single-cell DNA methylation data. SMORE operates on a Bayesian framework that converts methylation proportions into ordered methylation states and jointly infers a low-dimensional representation, cell populations and their number. By using low-rank latent Gaussian factorization and adopting a mixture-of-finite-mixtures prior on latent cell scores, SMORE infers cell assignments without requiring a prespecified cluster number, and propagates uncertainty from methylation measurements to cell assignments. Across simulations spanning varying sample sizes, population imbalance, signal strengths and model misspecification, SMORE accurately recovered latent population structure and outperformed existing methods.