M Kellis

This paper improves scRNA-seq dataset integration by introducing regularization strategies for cVAEs, enhancing batch correction while preserving biological variation.

This study identifies ABCA7 loss-of-function variants' impact on phosphatidylcholine metabolism in human neurons and shows CDP-choline can reverse these effects.

The paper analyzes multi-omic data from two pig heart xenografts in human decedents to identify immune and metabolic responses associated with rejection and injury.

The paper evaluates 23 imputation models for cross-cell type epigenomic data, finding quality assessment challenging due to distributional shifts, data availability, and redundant metrics.

This study uses barcoded rabies virus with single-cell and in situ sequencing to map synaptic connectivity in the mouse brain, distinguishing cortical cell types by their connectivity patterns.

The paper systematically analyzes proteomic and genomic data of SARS-CoV-2 variants to identify how mutations affect viral gene expression, host interactions, and immune evasion.

The paper introduces a CRISPR-based synthetic transcription system for programmable gene expression in mammalian cells, demonstrating its ability to control gene activity and cellular functions with high tunability.

The paper identifies that DNA double strand breaks in neurons activate immune pathways, leading to microglia activation in neurodegeneration.

This paper outlines a community-driven initiative to create a consolidated catalog of human Ribo-seq ORFs, addressing the lack of standardized annotation for these features.

The paper examines transcriptional changes in striatal neurons in Huntington's disease, finding that striosomal SPNs are most depleted and compartmental markers are dysregulated in both mouse models and human HD.

This study analyzes somatic mosaicism in Alzheimer's Dementia using single-cell data, finding increased mutational burden in specific cell types and genes associated with disease processes.

The paper identifies cell-type-specific molecular changes in cerebrovascular cells across brain regions in Alzheimer's Disease, linking them to genetic variants and potential therapeutic targets.

The paper shows isomiRs significantly affect differential expression analysis and highlights the need to consider them instead of relying solely on reference miRNA sequences.

The paper introduces a causal inference framework to prioritize disease genes in single-cell gene expression analysis, identifying 215 causal genes differentially regulated in Alzheimer's disease across cell types.

This study develops a high-throughput method to engineer 5' UTRs that enhance protein expression in non-viral gene therapies, identifying synthetic 5 UTRs that outperform existing plasmids.

The paper uses single-cell transcriptomics to analyze the hippocampus and entorhinal cortex in Alzheimer's disease, identifying stage-specific molecular changes across cell types.

This paper introduces a framework using operads to model cellular intelligence through non-equilibrium compartmentalization and applies it as an unsupervised learning algorithm to machine learning datasets.

The paper investigates how genome plasticity and transcriptional organization enable metabolic resilience to nutrient limitation and fasting endurance.

NEBULA improves differential expression and co-expression analysis in large-scale multi-subject single-cell data by efficiently handling overdispersion with a negative binomial mixed model.

The paper compares human and mouse tissues to identify gene expression patterns in genes without 1-to-1 homology, highlighting potential therapeutic targets and functional insights.

The paper identifies a high-confidence SARS-CoV-2 gene set and analyzes evolutionary constraints and mutation impacts across 44 Sarbecovirus genomes.

This study characterizes human cerebrovascular cells in health and disease, identifying cell type-specific markers and revealing immune activation and BBB integrity loss in Huntington's disease.

The paper uses single-molecule long-read sequencing to identify structural variants driving high-altitude adaptation in Tibetans, revealing their role in gene regulation and biological functions.

This study identifies exonic variants in ERN1, TACR3, and SPPL2C associated with Alzheimer's disease through exome-wide age-of-onset analysis, with functional insights into their regulatory roles.

The paper deconvolves 3,387 post-mortem brain samples using SPLITR to identify cell-type-specific eQTLs and genetic effects in mental disorders.

ACTIONet is a multiresolution framework that uses archetypal analysis and network theory to characterize single-cell state landscapes across tissues and organisms.

The paper analyzes secondary-variant burden in BBS cohorts and finds enrichment of trans-acting rare nonsynonymous variants in chaperonin-encoding genes.

The paper characterizes bumblebee genomes across 17 species to identify genetic variation linked to ecological and behavioral traits, revealing insights into genome structure, evolution, and adaptation.

The paper presents a large ASE resource from GTEx v8 with 431 million SNP-level and 153 million haplotype-level measurements, and extends phASER to estimate cis regulatory variant effect sizes.

The paper uses bulk RNA-seq data to estimate cell type abundances and identifies cell type interaction QTLs that provide finer resolution of tissue specificity than bulk cis-eQTLs.