Lieven Clement

This study identifies genetic loci associated with childhood eating behaviour and shows they mediate obesity risk through distinct pathways, primarily driven by the child's own genotype.

IsoformSwitchAnalyzeR v2 identifies and analyzes functional isoform switches in long-read and single-cell sequencing data, showing disease-relevant changes in Alzheimer's and glioblastoma.

gamdid is a statistical framework for differential distribution analysis in single-cell proteomics, detecting shape differences and outperforming existing methods for such cases.

The paper analyzes Common Toad population trends in Flanders over four decades and finds no significant link between population decline and surrounding landscape characteristics.

The paper introduces a method to enhance RNA-seq tools for differential usage, aberrant splicing, and expression retrieval by modifying normalization offsets and presents a faster tool for outlier detection.

The paper evaluates library-free DIA workflows for LiP-MS and introduces DIA-LiPA, a pipeline that improves structural interpretation of limited proteolysis data.

The paper develops a workflow for differential detection analysis in single-cell RNA-seq data and shows it provides complementary information to differential expression analysis.

The paper introduces msqrob2TMT, a mixed model-based workflow for differential protein abundance analysis in labelled proteomics, showing improved performance over existing tools.

omicsGMF integrates dimensionality reduction, batch correction, and imputation for bulk and single-cell proteomics data, improving statistical power for differential abundance detection.

This paper evaluates strategies to address pseudoreplication in multi-patient scRNA-seq data, finding that Poisson GEEs and pseudobulk approaches outperform single-cell methods.

The paper introduces msqrob2PTM, a workflow for differential abundance and usage analysis of PTMs and peptidoforms in proteomics data, showing it performs on par or better than existing methods.

The paper introduces TargetDecoy, a Bioconductor package to assess and control the quality of the target-decoy approach for peptide identification in proteomics.

satuRn analyzes differential transcript usage in bulk and single-cell RNA-seq data, offering scalability and accurate false discovery rate control.

The paper identifies genes responding to sex-inducing pheromones in the diatom Seminavis robusta and links them to sexual reproduction processes.

This paper introduces MSqRob-Sum, a novel summarization strategy for proteome-wide label-free quantification that maintains the performance of MSqRob while enabling protein-level analysis and reducing computational demands.

The paper introduces a hurdle model to address missing values in proteomics by combining binomial count and intensity-based components, showing improved performance over existing methods.

The paper identifies issues in re-analyzing observational omics studies, highlighting problems with false positives, method limitations, and lack of confounders in public metadata.

The paper introduces a generalized additive model framework for trajectory-based differential expression analysis in single-cell RNA-seq data, enabling flexible inference of within and between lineage gene expression differences.

The paper introduces a weighting strategy using a zero-inflated negative binomial model to improve bulk RNA-seq tools for single-cell RNA-seq data with zero inflation.

The paper introduces a stage-wise testing procedure for differential expression and transcript usage that provides gene-level FDR control while maintaining transcript-level resolution.

zingeR improves differential expression analysis in scRNA-seq by using a zero-inflated negative binomial model to address zero-inflation, matching performance of dedicated scRNA-seq tools.

The paper argues that searching all expected peptides and removing irrelevant ones before FDR calculation improves reliability compared to discarding irrelevant peptides before or after FDR calculation.