Simon Anders

This study identifies three centrosome cohesion pathways that regulate Golgi organization and function through distinct mechanisms.

This paper introduces scReMeMber-Seq, a method to reconstruct single-cell trajectories by linking past and present transcriptomes using methylation-based memory recording.

The paper shows that inflammatory injury leads to polyploid pancreatic acinar cells with CIN and STING loss, contributing to tissue repair and cancer risk.

The paper identifies two chromatin modules, ACs and DCs, that regulate proinflammatory gene expression through distinct mechanisms in human endothelial cells.

The paper benchmarks deep learning models against linear baselines for predicting gene perturbation effects and finds they do not outperform simple models.

The paper uses single-cell triple-omics to show DNA methylation patterns are key to stemness in adult brain cells, especially under ischemic conditions.

The paper introduces MethSCAn, a tool for analyzing scBS data that improves methylation quantification and DMR detection, enhancing cell type discrimination with fewer cells.

The paper identifies JQ1 as a molecular glue that targets centromeres by stabilizing BRD4-CENP-B interactions, enhancing centromere cohesion and affecting cell proliferation.

R/LinkedCharts simplifies creating interactive linked visualizations for exploratory data analysis with minimal R code.

This study shows interferon regulates stem cell function across all ages by controlling mTOR and cell cycle, with beneficial effects in youth and harmful effects in aging brains.

The paper identifies AAV variants that efficiently target adult neural stem cells in the subventricular zone, enabling genetic modification for potential regenerative therapies.

The paper develops a statistical method to analyze thermal proteome profiles and identifies off-target drug interactions in epigenetic drugs.

The paper analyzes 3'UTR changes during neuronal differentiation and in ASD, finding longer 3UTRs in ASD patients and a role for CPEB4 and APLP1 in APA regulation.

Sleepwalk is an interactive tool for exploring dimension-reduced embeddings, highlighting distortions and revealing hidden data characteristics across multiple samples.

proDA models intensity-dependent missing values probabilistically within variance-moderated linear models to test differential protein abundance in label-free mass spectrometry without the false positives of ad hoc imputation.

This paper introduces NPARC, a non-parametric method for analyzing thermal proteome profiles to identify drug-binding proteins with improved specificity and sensitivity.

The paper develops a machine learning classifier, EpiSmokEr, to predict smoking status from DNA methylation data, showing robust performance across multiple datasets and tissues.

DESeq2 uses empirical Bayes priors to shrink negative binomial dispersions and log fold changes, giving reproducible effect sizes and error-controlled differential expression calls for RNA-seq.