Narges Rezaie

The paper develops lr-kallisto, a tool for accurate long-read transcriptome quantification using ONT data, showing improved results with exome capture.

The paper identifies regulatory programs in postnatal mouse development using single-nucleus data, revealing cell-type-specific and common regulatory topics across multiple tissues.

The paper introduces Topyfic, a reproducible LDA method, to identify cellular programs in single-cell data and shows sex-specific differences in microglial activation in Alzheimer's disease mouse models.

PyWGCNA is a Python package for weighted gene co-expression network analysis, enabling module identification and comparison in RNA-seq data.

Long-read sequencing of 81 human and mouse samples yields 200,000 full-length transcripts plus a triplet framework that separates promoter choice, splicing and 3' end use across tissues.

This paper characterizes the 3xTg-AD mouse model of Alzheimer's Disease across its lifespan and compares it with the 5xFAD model to assess pathology and model drift.

The paper performs a comprehensive phenotypic analysis of the 5xFAD mouse model across its lifespan, identifying novel age-related pathological features.

The paper uses single-cell and nucleus RNA-seq to analyze glial subpopulations in AD mouse models, finding differences in astrocyte activation and microglia subtypes between 3xTg-AD and 5xFAD models.

MaxHiC improves Hi-C and capture Hi-C analysis by robustly correcting biases and identifying significant chromatin interactions with better biological relevance.