Sharan Janjuha

Predicting prime editing efficiency across diverse edit types and chromatin contexts with machine learning

Prime editing of the beta-1 adrenoceptor in the brain reprograms mouse behavior

This study uses prime editing in mice brains to introduce a mutation in the Adrb1 gene, leading to increased activity and exploratory behavior.

A single-cell atlas of de novo β-cell regeneration reveals the contribution of hybrid β/δ cells to diabetes recovery in zebrafish

This study maps β-cell regeneration in zebrafish using single-cell transcriptomics, identifying hybrid β/δ cells as key contributors to diabetes recovery.

Predicting base editing outcomes with an attention-based deep learning algorithm trained on high-throughput target library screens

This paper develops BE-DICT, a deep learning model trained on high-throughput screens to predict base editing outcomes with high accuracy.

Machine learning based classification of cells into chronological stages using single-cell transcriptomics.

This paper introduces GERAS, a machine learning framework that classifies single-cell transcriptomes into chronological stages with high accuracy and identifies aging-related factors in zebrafish and human pancreatic cells.

Massively parallel single cell lineage tracing using CRISPR/Cas9 induced genetic scars

The paper introduces a method to trace cell lineages and profile transcriptomes in thousands of single cells using CRISPR/Cas9 and scRNA-seq in zebrafish.