Vasilis Ntranos

PepCL: A replay-based continual learning framework for updating peptide-MHC models

PepCL updates peptide-MHC predictors with new assay data while preserving prior MS knowledge, improving prediction across alleles in clinical contexts.

ApoE-Dependent Lipid Handling by Median Eminence Microglia Preserves Myelin Integrity and Metabolic Function

This study identifies ApoE-dependent lipid handling in median eminence microglia that preserves myelin integrity and metabolic function, showing therapeutic potential through liver X receptor activation.

Fezf2 regulates differentiation of Aire-expressing and post-Aire mimetic epithelial populations maintaining thymic homeostasis

This study shows Fezf2 regulates mTEC lineage commitment and homeostasis, with transcriptional repression playing a key role in maintaining thymic epithelial populations.

Democratizing Protein Language Model Training, Sharing and Collaboration

The paper introduces SaprotHub, a platform that simplifies training and sharing of protein language models, enabling non-experts to build and collaborate on customized models.

Phenotypic pleiotropy of missense variants in human B cell-confinement receptor P2RY8

The paper uses deep mutational scanning and structural analysis to study the functional effects of missense variants in the P2RY8 receptor and improves variant effect predictions.

Uncovering differential tolerance to deletions versus substitutions with a protein language model

The paper uses a protein language model to analyze amino acid deletions in the human proteome, identifying sites with differential tolerance to deletions versus substitutions.

Blocking Osteoprotegerin Reprograms Cancer Associated Fibroblast to Promotes Immune Infiltration into the Tumor Microenvironment

This study identifies OPG-secreting CAFs as immunosuppressive stromal cells and shows that blocking OPG promotes immune infiltration and tumor regression in mouse models.

Investigating the sources of variable impact of pathogenic variants in monogenic metabolic conditions

The paper investigates why carriers of pathogenic variants in monogenic metabolic conditions show variable disease severity, finding support for variant effect sizes, polygenic backgrounds, and epistasis.

nSMase2-mediated exosome secretion shapes the tumor microenvironment to immunologically support pancreatic cancer

This study shows that eliminating nSMase2 in pancreatic cancer reduces tumor growth, promotes a less aggressive tumor subtype, and enhances immune responses by altering exosome production and macrophage polarization.

Single cell sequencing as a general variant interpretation assay

The paper uses single cell RNA sequencing to assess the effects of protein-altering variants in three genes, showing that single cell expression signatures are more effective than bulk assays and computational models for predicting pathogenicity.

memento: Generalized differential expression analysis of single-cell RNA-seq with method of moments estimation and efficient resampling

The paper introduces memento, a method for differential expression analysis of scRNA-seq data, and shows it identifies significant differences in mean, variability, and gene correlation across experimental conditions.

Genome-wide prediction of disease variants with a deep protein language model

This paper uses a deep protein language model to predict the functional impact of all human missense variants, outperforming existing methods and providing isoform-specific annotations.

An integrated transcriptomic and epigenomic atlas of mouse primary motor cortex cell types

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

The Barcode, UMI, Set format and BUStools

The paper introduces the BUS format for single-cell RNA-seq pseudoalignments and the BUStools suite for analyzing data in this format.

Column subset selection for single-cell RNA-Seq clustering

This paper introduces a deterministic column subset selection method for scRNA-Seq clustering, showing it performs similarly to full data clustering while avoiding some limitations of PCA and thresholding methods.

Identification of transcriptional signatures for cell types from single-cell RNA-Seq

The paper introduces a method to identify cell types and their transcriptional signatures from single-cell RNA-Seq data by analyzing transcript compatibility counts.

One read per cell per gene is optimal for single-cell RNA-Seq

The paper determines the optimal sequencing depth for single-cell RNA-Seq as one read per cell per gene and identifies an empirical Bayes estimator as optimal for gene property estimation.