Anup Mahurkar

SEngEAR is a web platform for annotating senescent cells, visualizing communication networks, and comparing senescence signatures across datasets and contexts.

The paper identifies distinct fibroblast and perivascular senescence subtypes in fibrosis using single-cell and spatial transcriptomics, showing their spatial organization and roles in fibrosis progression.

The paper compiles a large transcriptomic dataset of neocortical development and uses joint matrix decomposition to identify conserved transcriptome dynamics across mammals and developmental stages.

The paper identifies multi-omic phenotypes in pancreatic precursor lesions to distinguish progressive from indolent precancers using integrative transcriptomic, genomic, and spatial data.

The paper analyzes single neuron transcriptomic changes in aging Aplysia to show progressive impairments in transcriptional plasticity during learning.

The paper introduces MAMS, a metadata standard for single-cell data matrices and analysis workflows, to improve reproducibility and interoperability across datasets.

The paper describes the Neuroscience Multi-Omic Archive, a repository for single-cell transcriptomic and epigenomic data from mammalian brains, providing tools for data access and analysis.

The paper describes the development of a metadata federation system for NIH Common Fund data to improve discoverability without data movement.

The paper compares cellular diversity in the primary motor cortex across human, marmoset, and mouse using single-nucleus transcriptomics and epigenomics, finding conserved cell types and species-specific adaptations.

The paper creates a comprehensive atlas of mouse primary motor cortex cell types using integrated transcriptomic and epigenomic data from over 600,000 samples.

gEAR provides a community-driven portal for multi-omic data visualization, analysis, and sharing across species and data types.

This study evaluates the impact of read length and pairing on differential expression analysis in host-pathogen interactions, finding 54-bp paired reads most robust and 36-bp single reads suitable for bacterial samples.

FADU improves RNA-Seq quantification for prokaryotes by better detecting genes missed by HTSeq and featurecounts, especially in organisms with overlapping transcriptional units and polycistronic transcripts.

Metaviz provides an interactive web tool for exploring and visualizing metagenomic data through heatmaps, bar plots, and PCA scatter plots.

This paper introduces a method to simultaneously analyze host and bacterial transcriptomes in infected cells and provides a protocol for its implementation.