Mark D. Robinson

This study shows that CENP-A, a centromere-specific histone variant, is dynamically regulated by synaptic activity in neurons and is required for activity-dependent gene expression and hippocampus-dependent learning and memory.

The paper introduces spatialFDA, a framework for analyzing differential co-localisation in spatial omics data, showing its effectiveness in controlled simulations and biological applications.

The paper benchmarks small variant calling pipelines for long-read RNA sequencing data, finding that sequencing quality is the main factor affecting performance, followed by variant caller and alignment strategy.

The paper introduces a method for analyzing multicellular anatomical structures from spatial omics data and provides an open-source tool for this purpose.

The paper introduces three R/bioconductor packages to address technical variations in ATAC-seq data and improve motif accessibility analysis.

The paper introduces SACCELERATOR, a framework for spatially aware clustering that improves benchmarking by incorporating expert feedback and consensus analysis across diverse datasets.

DESpace2 identifies differential spatial patterns in spatial omics data by comparing gene expression across experimental conditions.

The paper predicts transcription factor binding across 43 human cell types using ATAC-seq data and a new platform, revealing promiscuous occupancy and chromatin contacts.

The paper evaluates five single-cell RNA sequencing pipelines using Omnibenchmark, highlighting differences in parameter implementation across tools.

This study benchmarks long-read RNA-seq across bulk and single-cell platforms, technologies, and sequencing depths, identifying optimal methods for transcript discovery and quantification.

MIMIC is a pipeline that integrates Mass Spectrometry Imaging and Imaging Mass Cytometry to analyze spatial omics data, recovering known molecular associations and revealing novel spatial relationships.

This study identifies synovial fibroblasts as key targets of JAK inhibition in RA and reveals cytokine- and drug-driven mechanisms affecting JAKi efficacy and safety.

scprocess is a Snakemake pipeline that automates and integrates single cell RNA sequencing data processing, enabling reproducible and scalable analysis of large datasets.

This study benchmarks PacBio Kinnex long-read RNA sequencing against Illumina short-read RNA-seq, finding Kinnex provides more accurate transcript discovery and quantification with comparable gene-level performance.

This study compares two workflows for integrating imaging mass cytometry (IMC) and sialic-acid-linkage-resolving N-glycan MALDI-MSI on the same tissue section, finding that IMC-first acquisition preserves antibody signal and glycan integrity.

BiCLUM integrates unpaired single-cell multi-omics data by aligning cell and feature spaces through bilateral contrastive learning, outperforming existing methods in visualization and quantitative metrics.

The paper improves cell typing in imaging mass cytometry by addressing spillover through spatial smoothing and marker aggregation techniques.

RoCK and ROI improves single-cell transcriptomics by targeting specific transcripts and regions, enhancing detection of critical biological features.

The paper improves single-cell differential expression analysis by integrating bulk RNA-seq data through bulk-based hypothesis weighing, enhancing power and robustness.

This study shows that rbm8a deficiency in zebrafish causes hematopoietic defects by impairing non-canonical Wnt/PCP signaling, leading to reduced thrombocytes and LPM anomalies.

The paper develops a workflow using long-read RNA-seq and MS data to construct haplotype-resolved proteomes, enabling detection of variant and allele-specific protein isoforms.

The paper provides an open-source online book for analyzing spatial transcriptomics data using Bioconductor in R, with reproducible code and datasets.

The paper evaluates feature selection strategies to disentangle cell type and state effects in single-cell data, aiming to improve differential testing consistency between clustering and neighborhood-based approaches.

The paper develops a hybrid sequencing strategy combining short-read and long-read methods to enhance genotype-phenotype analysis at single-cell resolution.

This paper provides a framework for selecting and understanding validation metrics for subpopulation detection in single-cell and spatial omics data, developing new metrics and implementing them in a Bioconductor package.

The paper compares in vitro and in vivo macrophage phenotypes using mass cytometry, finding distinct surface marker expression patterns between macrophages derived from culture and those from tumor-associated environments.

Patches decodes shared and condition-specific transcriptional programs in wound healing using scRNA-seq data, revealing universal patterns and treatment-specific changes.

The paper uses single nuclei RNAseq to identify distinct glial gene expression patterns in MS patients, revealing subgroup-specific pathological processes and suggesting precision medicine approaches.

The paper benchmarks methods for identifying differentially-active transcription factors from ATAC-seq data using curated and simulated datasets, identifying three promising approaches.

The paper benchmarks computational methods for analyzing scATAC-seq data, finding that SnapATAC and SnapATAC2 outperform other methods in cell-type discrimination and scalability.