David A. Knowles

The paper identifies microglial states associated with Parkinson's disease genetic risk and Lewy body pathology using single-cell profiling and integrates data with AMP PD.

LRP2 is a proteogenomics pipeline that integrates long-read RNA sequencing and mass spectrometry to detect novel protein isoforms and improve their translation inference.

The paper introduces perplexity as a metric to quantify isoform diversity in the human transcriptome using long-read RNA-sequencing data, showing it provides interpretable and reproducible measurements.

This paper introduces FABOr, a Bayesian framework for matrix factorization of binary and ordinal phenotypes, showing improved imputation accuracy on real-world data.

The paper introduces parmigiano, an empirical Bayesian framework that improves rare variant association testing in Alzheimer's disease by integrating functional annotations, identifying 23 candidate genes including 19 new ones.

SingleBrain integrates snRNA-seq and genotype data to map cis-eQTLs across brain cell types, identifying novel disease-associated genes and causal variants.

The paper introduces CLADES, a contrastive learning method for differential splicing prediction using orthologous positive pairs, achieving strong performance in classification and regression tasks.

The paper identifies aging-associated alternative splicing programs in human and mouse tissues using scRNA-seq and introduces LeafletFA for discovering coordinated splicing programs.

The paper develops scEEMS models to predict cell-type-specific eQTLs, enhancing discovery of AD risk variants by linking them to cell-type-specific genes.

SpliceVI integrates sparse single-cell alternative splicing and gene expression data to better capture biological correlates and uncover splicing-based differences in mouse cells.

The paper identifies regulatory variants associated with Alzheimer's disease in human microglia using MPRA and highlights differences in variant activity between cell sources.

The paper infers fungal cis-regulatory networks from genome sequences using unsupervised and interpretable representation learning, identifying conserved motif signals that predict gene expression and regulation.

The paper identifies 38,731 edQTLs in 3,554 genes through a meta-analysis of A-to-I RNA editing in the human brain and links 24 GWAS loci for neurological disorders to edQTLs.

The paper maps genetic effects on splicing in 10,725 brain samples, identifying 68,358 cis-sQTLs and 97 loci where splicing mediates neurological disease risk.

This paper introduces RNA fingerprinting, a method to map transcriptional responses to perturbations using reference dictionaries, showing accurate single-cell assignments and broad biological applications.

The paper uses Bayesian TWAS to identify splicing-disease associations across multiple cohorts, revealing causal mechanisms in immune, vascular, and metabolic traits.

This study uses Mendelian Randomization to identify causal relationships between neuroimaging phenotypes and bipolar disorder, finding that white matter tract variations have larger causal effects on BD.

The paper develops a CRISPR/Cas13d-based method to specifically knockdown RNA isoforms and provides computational tools for experimental design and analysis.

The paper finds that mosaic chromosomal alterations in blood are associated with increased Alzheimer's disease risk, with variations by ancestry, mCA subtype, APOE ε4 status, and chromosomal location.

This paper introduces SPLISOSM, a framework for analyzing spatial transcriptomics data to detect isoform-level patterns, identifying spatially variable transcript diversity and regulatory relationships in mouse and human brain tissues.

This study shows oncogenic mutations disrupt lineage differentiation by altering DNA methylation, impairing stem cell reprogramming and differentiation while maintaining proliferation.

The paper introduces RNA-SPRITE to map RNA-RNA spatial proximity genome-wide, revealing conserved mRNA organization and colocalization driven by shared motifs and functional relationships.

The paper introduces Clorinn, a convex optimization method to decompose genetic variant associations into shared and distinct components across complex traits, showing improved performance in simulations and application to GWAS data.

This study investigates the phenotypic complexities of rare heterozygous NRXN1 deletions, showing cell-type-specific synaptic effects through LOF and GOF mechanisms, highlighting the need for precision medicine strategies.

This paper improves identification of causal Alzheimer's disease variants by integrating deep learning annotations with functional genomics, resulting in better fine-mapping and polygenic risk prediction across diverse populations.

This paper introduces JOLI, a hierarchical model that improves RNA isoform quantification by integrating short-read and long-read data across multiple samples.

This study investigates the effects of two common Atp1a3 mutations in mice, revealing distinct neurological phenotypes and potential therapeutic avenues for alternating hemiplegia of childhood.

This study introduces gruyere, a Bayesian model that improves rare variant association analysis by integrating functional annotations, identifying 15 new AD-associated genetic associations.

DIISCO infers dynamic intercellular interactions from time-series single-cell data using a Bayesian framework, showing its ability to capture time-dependent communication patterns in simulated and CAR-T cell data.

Smoother integrates positional information into non-spatial models for spatial omics data, enabling accurate imputation, deconvolution, and dimensionality reduction in simulated and real datasets.