Anuj Srivastava

The paper describes a panel of genetically diverse embryonic stem cells from Diversity Outbred mice to study natural genetic variation's impact on pluripotency and differentiation.

The paper identifies a composite transcriptome signature with high distal heritability that explains 30% of metabolic trait variation across species.

The paper develops a Nextflow pipeline for processing Visium spatial transcriptomics and H&E images from PDX samples, enabling multi-modal analysis and integration of spatial gene expression and morphometric features.

GenomeMUSter integrates and harmonizes mouse genetic variation data across populations to enable multi-trait, multi-population analyses and improve variant-phenotype association studies.

The paper compares genetic variation in two outbred mouse stocks, showing J:DO has higher genetic diversity and more damaging variants, while J:ARC is more suitable for general research due to lower variability.

The paper improves a drug synergy prediction algorithm by using random forest regression and cross-validation, achieving better performance and robustness than the original method and competing algorithms.

The paper introduces RIP-SIR, a method to identify endogenous miRNA-target interactions across tissues and cell types.

The paper develops bioinformatics workflows to analyze genomic data from patient-derived xenografts, addressing challenges like separating mouse and human reads and identifying somatic mutations without paired normal DNA.

The paper identifies a rare subpopulation of slow-cycling germline stem cells regulated by PLZF using RNAseq and single-cell RNA sequencing.