Biology subjects

Buchert, R.

Publications and source records attributed to Buchert, R..

3 recordsLinked to original sources

bioRxiv2026

MethylBench: A comprehensive benchmark of DNA methylation profiling methods across diverse sequencing platforms

BackgroundDNA methylation can be profiled using multiple technologies that vary in resolution, coverage and cost. Prior benchmarking efforts have laid important groundwork, yet critical gaps remain. The SEQC2 EpiQC study[14] provided a multi-platform QC framework across whole-genome bisulfite sequencing (WGBS), oxidative bisulfite sequencing, enzymatic deamination, ONT and Illumina 850k arrays, showing high overall concordance but limited replication and no coverage of current enzymatic conversion, hybridization-based panels or modern long-read chemistries. More recently, Sigurpalsdottir et al.[40] systematically compared methylation detection tools within the long-read domain, demonstrating high ONT accuracy relative to oxidative bisulfite sequencing, but restricted their analysis to ONT and PacBio, excluding short-read, targeted-panel and array-based platforms as well as the biological interpretability of differential methylation across platform classes. A comprehensive cross-platform benchmark addressing coverage heterogeneity and annotation redundancy in the interpretation of differentially methylated cytosines thus remains absent. MethodsWe compared six widely used technologies - Illumina EPIC array, TWIST, Whole-Genome Enzymatic Conversion (WGEC), Reduced Representation Bisulfite Sequencing (RRBS), Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT) - using GIAB reference samples and ten blood/fibroblast samples from 5 individuals. We assessed CpG coverage, consistency of differentially methylated cytosine (DMC) detection, and genomic annotation, with particular attention to overlapping signals across assays. ResultsDespite major assay differences, all technologies consistently identified DMCs enriched in promoter and intronic regions, marking these as robust hotspots of epigenetic variability. Annotation redundancy strongly shaped initial interpretations, with CpG island-related categories largely disappearing once annotations were collapsed to unique features. Sequencing-based methods (WGEC, TWIST, ONT) achieved the most comprehensive coverage, while EPIC arrays reliably captured promoter-associated differences despite limited scope. ONT showed strong concordance with short-read methods after coverage filtering, but required higher, more uniform coverage for reproducible CpG-level agreement. PacBio showed a coverage-independent discrepancy, with concordance plateauing regardless of mean coverage - pointing to residual technology-specific bias rather than a simple coverage effect. ConclusionsCross-platform benchmarking yields coherent biological insights once coverage and annotation redundancies are addressed. EPIC arrays remain valuable for promoter-focused cohort studies, WGEC and TWIST enable genome-wide discovery and ONT offers unique phasing and multimodal potential - together guiding method selection and more robust interpretation of DNA methylation data.

genomics↗

bioRxiv2023

Brain age predictions in longitudinal data reveal the importance of scan quality and field strength

IntroductionBrain age, the estimation of a persons age from magnetic resonance imaging (MRI) parameters, has been used as a general indicator of health. The marker requires however further validation for application in clinical contexts. Here, we show how brain age predictions perform for for the same individual at various time points and validate our findings with age-matched healthy controls. MethodsWe used densly sampled T1-weighted MRI data from four individuals (from two datasets) to observe how brain age corresponds to age and is influenced by acquision and quality parameters. For validation, we used two cross-sectional datasets. Brain age was predicted by a pre-trained deep learning model. ResultsWe find small within-subject correlations between age and brain age. We also find evidence for the influence of field strength on brain age which replicated in the cross-sectional validation data, and inconclusive effects of scan quality. ConclusionThe absence of maturation effects for the age range in the presented sample, brain age model-bias (including training age distribution and field strength) and model error are potential reasons for small relationships between age and brain age in longitudinal data. Future brain age models should account for differences in field strength and intra-individual differences.

neuroscience↗

bioRxiv2019

Heterozygous variants in KMT2E cause a spectrum of neurodevelopmental disorders and epilepsy

We delineate a KMT2E gene-related neurodevelopmental disorder based on 38 individuals in 36 families. This includes 31 distinct heterozygous variants in the KMT2E gene (28 ascertained from Matchmaker Exchange and 3 previously reported), and 4 individuals with chromosome 7q22.2-22.23 microdeletions encompassing the KMT2E gene (1 previously reported). Almost all variants occurred de novo, and most were truncating. Most affected individuals with protein-truncating variants presented with mild intellectual disability. One-quarter of individuals met criteria for autism. Additional common features include macrocephaly, hypotonia, functional gastrointestinal abnormalities, and a subtle facial gestalt. Epilepsy was present in about one-fifth of individuals with truncating variants, and was responsive to treatment with anti-epileptic medications in almost all. Over 70% of the individuals were male and expressivity was variable by sex, with epilepsy more common in females and autism more common in males. The four individuals with microdeletions encompassing KMT2E generally presented similarly to those with truncating variants, but the degree of developmental delay was greater. The group of four individuals with missense variants in KMT2E presented with the most severe developmental delays. Epilepsy was present in all individuals with missense variants, often manifesting as treatment-resistant infantile epileptic encephalopathy. Microcephaly was also common in this group. Haploinsufficiency versus gain-of-function or dominant negative effects specific to these missense variants in KMT2E may explain this divergence in phenotype, but requires independent validation. Disruptive variants in KMT2E are an under-recognized cause of neurodevelopmental abnormalities.

genetics↗