bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2024.12.18.629129

ASMS: finding allele specific methylation in human genomes without phasing

Raineri, E.·Esteve Marco, M. A.·Esteve-Codina, A.
View original source ↗Export metadata ↓

Abstract

MotivationAllele-specific methylation (ASM) refers to differential DNA methylation patterns between two alleles at a given locus. This phenomenon is often driven by genetic variants, such as single nucleotide variants (SNVs), which influence methylation in cis by affecting transcription factor or methylation regulator binding, leading to allele-specific differences. Understanding ASM is critical for elucidating gene regulation, as it impacts gene expression and contributes to normal biological variation as well as disease processes, including cancer [1] and autoimmune disorders [2],[3]. Another key driver of ASM is genomic imprinting, an epigenetic mechanism in which gene expression is regulated in a parent-of-origin-specific manner. Imprinted regions, marked during gametogenesis and maintained through cell divisions, are essential for growth, development, and metabolism. Dysregulation of imprinting is associated with developmental and metabolic disorders, such as Prader-Willi and Angelman syndromes, and certain cancers. Detecting ASM across the genome remains challenging due to its tissue- and cell-specific nature and the technical difficulty of phasing reads to assign methylation patterns to specific alleles. Current ASM detection pipelines (e.g., [4])) often require phasing via genetic variants, a computationally intensive process that is limited in regions with low heterozygosity. ResultsTo address these limitations, we developed asms (Allele-Specific Methylation Scanner), a tool designed to detect ASM directly from methylation data without the need for prior phasing. asms offers a faster and complementary approach to uncovering the regulatory effects of ASM, particularly in genomic regions where genetic variants or imprinting play a critical role. For demonstration purposes we leverage the fact that reads generated by Oxford Nanopore (ONT) technology measure sequence and methylation status at once, but the same software can be used with other sequencing technologies. asms can check thousands of loci in a short time. The initial list of loci to examine can be given by the user, or generated by asms through a genomic scan. The asms cluster subcommand separates the reads based on methylation. If phasing results are available, asms can use them to verify whether distinct alleles correspond to distinct base modifications patterns. We benchmark our software using publicly available Ashkenazi trio data [5]. Implementation and availabilityasms is implemented in rust and python. The software is available at https://github.com/ecmra/asms.

Source connections

Explore related subjects

bioinformaticsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Raineri, E., Esteve Marco, M. A., Esteve-Codina, A.. 2024-12-22. ASMS: finding allele specific methylation in human genomes without phasing. https://doi.org/10.1101/2024.12.18.629129

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

spatialMET: an open and scalable framework for spatial metabolomics analysis

Mass spectrometry imaging (MSI) enables spatially resolved metabolomics in intact tissue sections, but analysis remains challenging at scale. Existing MSI workflows often require users to combine multiple software tools, while others rely on proprietary vendor software that limits interoperability and reproducibility. To address these challenges, we developed spatialMET, an open-source framework that provides an end-to-end workflow for MSI analysis. spatialMET provides a unified platform for preprocessing, spatial domain detection, and visualization. Downstream analyses include differential abundance testing, spatial autocorrelation and gradient analysis, dimensionality reduction, and correlation network analysis. Spatial domain detection uses hcdist, a C-based hierarchical clustering implementation that substantially reduces runtime and memory use relative to existing R-based approaches. spatialMET can be run through an interactive R Shiny application or as a standalone command-line workflow for larger datasets or high-performance computing environments. Applied to mouse small cell lung cancer MALDI-MSI data containing 284,673 pixels, spatialMET identified tumor-associated, stromal, and adjacent lung spatial domains that aligned with matched histology. Differential abundance analysis identified 117 m/z features that differed between tumor and stromal regions, while spatial autocorrelation analyses revealed spatially structured abundance patterns. Applying spatialMET to mouse lung adenocarcinoma data from an entire lung lobe containing 338,477 pixels further demonstrated scalability and captured spatial heterogeneity across tumor and surrounding lung tissue. In summary, spatialMET provides a scalable, open-source framework for end-to-end spatial metabolomics analysis, and it is distributed as a Docker container for reproducible deployment. Source code and installation instructions are available at https://github.com/biodatalab/spatialMET.

bioinformatics↗
bioRxiv2026

Probing the transcriptome response to shivering in skeletal muscle using a multilayered bioinformatics approach

Cold acclimation holds therapeutic potential for improving metabolic health. We previously demonstrated that repeated cold-induced shivering enhances insulin sensitivity in humans. However, the molecular pathways that underlie the skeletal muscle shivering response, and how these relate to beneficial physiological effects, remain poorly understood. In this study, we combined complementary bioinformatics approaches to allow in-depth analysis of the transcriptomic response of human skeletal muscle to repeated shivering. We identified a robust transcriptional signature and show a sex-specific component in the shivering skeletal muscle response, which seemed to diminish following cold adaptation. Our findings provide mechanistic insights into cold-induced muscle adaptations, shed light on potential interesting molecular targets for further investigation, and emphasize the importance of including both sexes in future cold acclimation studies.

bioinformatics↗
bioRxiv2026

An Information Geometry approach to model topological trajectories and Gene Expression Radius from UMAP geometry.

Understanding the relationship between gene expression dynamics and cellular identity remains a central challenge in single cell biology. Here, we introduce a novel computational and mathematical framework that integrates information geometry, fuzzy topology, and UMAP analysis to model gene expression landscapes derived from single cell RNA sequencing data. We formalize gene expression data as a fuzzy topological space, where interactions between expression points are governed by probabilistic distributions inspired by manifold learning approaches such as UMAP. Within this framework, we define an information geometric structure through a Fisher metric induced by these distributions, enabling the computation of geodesic trajectories that capture cellular differentiation processes. A key contribution of this work is the derivation of analytical conditions, expressed as expression radius formulas, that characterize local neighborhoods in gene expression space. These conditions allow for the identification of genes associated with stem cell states and predictions in transitional cell types in future work. Application of the proposed framework to single cell datasets reveals biologically meaningful gene sets enriched in key regulatory pathways and transcription factors, demonstrating the capacity of our approach to uncover latent structure in complex gene expression data. Our results suggest that integrating differential geometry with statistical learning theory offers a powerful paradigm for modeling genotype and phenotype relationships and cellular state transitions, with potential implications for precision medicine and systems biology.

bioinformatics↗