bims-dinmec 2026-01-25 papers

Issue of 2026–01–25
three papers selected by
Lorena Ancona, Humanitas Research

Bioinformatics. 2026 Jan 22. pii: btag045. [Epub ahead of print]

MethylModes: Computationally Efficient Detection of Multimodal Distributions in DNA Methylation Data.

T Sophia Luo, Jonathon LeFaive, John Dou, Kelly M Bakulski, Erin B Ware, Matthew Zawistowski.

SUMMARY: MethylModes is an R package and Shiny application to identify multimodal distributions in human DNA methylation at individual CpG sites. Multimodal distributions, which can be the result of nearby genetic variation, environmental exposures or assay artifacts, are susceptible to confounding and important to identify for methylation analysis. MethylModes is easily incorporated into existing quality control pipelines of array-based DNA methylation data. The underlying algorithm uses kernel smoothing of probe-level data to locate the number and location of peaks. The algorithm can be parallelized across probes for efficient implementation at genome-scale. We provide a case study implementation of MethylModes in the Health and Retirement Study as well as the Airwave Health Monitoring Study.
AVAILABILITY AND IMPLEMENTATION: MethylModes is available on GitHub at https://github.com/lutiffan/methylModes as an R package wrapping an R Shiny application. We include a toy dataset to validate installation. The codebase is also published on Zenodo at https://doi.org/10.5281/zenodo.17448517.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DOI: https://doi.org/10.1093/bioinformatics/btag045

Bioinformatics. 2026 Jan 19. pii: btag034. [Epub ahead of print]

Enhancing interpretation of clinical disease-associated copy number variations from multiple sequencing strategies with CNVSeeker.

Xudong Xiang, Xinxin Mao, Tengfei Luo, Chenbin Liu, Bozhao Li, Pei Yu, Yu Zhang, Dai Wu, Yijing Wang, Qiao Zhou, Yixiao Zhu, Bin Li, Kun Xia, Guihu Zhao, Jinchen Li.

MOTIVATION: DNA copy number variations (CNVs) exert a profound impact on major genetic disorders in humans. Although multiple sequencing technologies have become the first line of molecular diagnosis for CNVs, existing tools are unable to resolve the pathogenicity of CNVs directly from raw sequencing data.
RESULTS: We developed CNVSeeker, a one-stop and easy-to-use pipeline that provides comprehensive analysis from raw sequencing data to variant interpretation reports, and supports multiple types of sequencing data including short-read data such as whole genome sequencing (WGS) data and whole exome sequencing (WES) data, and long-read sequencing data from Pacific Biosciences HiFi (PacBio) platform or Oxford Nanopore Technologies (ONT) platform. Through extensive benchmarking, CNVSeeker demonstrated comparable enhancement over the state-of-the-art methods for CNV calling. Moreover, CNVSeeker enables significantly precise variant classification with an accuracy of ∼87%. By applying CNVSeeker to 1946 individuals with autism spectrum disorder (ASD), a total of 133 ASD-associated CNVs in 122 patients were identified, yielding a diagnostic yield of ∼6.3%. Additionally, we have also provided a user-friendly webserver for intuitive visualization of results. This study highlights the potential of CNVSeeker to benefit clinicians and geneticists with limited bioinformatic skill by aiding them interpret CNVs directly from various types of raw sequencing data for auxiliary disease diagnosis.
AVAILABILITY: The web server is freely available at https://genemed.tech/cnvseeker and the open-source code can be found at https://github.com/lovelycatZ/CNVSeeker.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DOI: https://doi.org/10.1093/bioinformatics/btag034

Mol Ecol. 2026 Jan;35(2): e70216

A Beginner's Guide to Structural Variants in Eco-Evolutionary Population Genomics.

Katarina C Stuart, Rebekah A Oomen, Anna Tigano, Maren Wellenreuther, Jana Wold, David L Field, Claire Mérot.

Whole-genome sequencing (WGS) has greatly expanded researchers' ability to study structural variants (SVs), that is, the variation in the presence, number, orientation or position of a DNA sequence. This has paved the way to study the eco-evolutionary dynamics of SVs across the tree of life and within a population genomics framework. In this review, we provide the necessary fundamentals to help researchers generate and analyse population-level SV data. We discuss the unique properties of different SV groups and how these fundamental differences interact with important biological and evolutionary processes using both empirical results and theory. This includes discussion of unresolved issues around SVs, such as technical difficulties in identification, accounting for diversity and evaluating functional effects. We explicitly integrate into this discussion transposable elements, which are an important component of SVs often identified in population-level variant data. Finally, we focus on the practical side of SV analysis, offering a framework for SV identification and data analysis. In particular, we examine the heterogeneous nature of SV properties (type, length, sequence identity) that should be considered when studying them in ecology and evolution. This review aims to provide resources and guidelines to help researchers navigate the complexities of a relatively new field of eco-evolutionary genomics research.

Keywords: chromosomal rearrangements; copy number variants; distribution of fitness effects; inversions; rapid adaptation; transposable elements

DOI: https://doi.org/10.1111/mec.70216