Sonali Arora

The paper analyzes bulk RNA-seq data from brain tumors and healthy samples to identify differential regulation of synaptic components and their impact on tumor progression and patient survival.

This paper constructs a unified transcriptional landscape of breast cancer using bulk RNA-seq data from multiple cohorts, identifying subtype and microenvironmental programs associated with therapeutic resistance.

The paper constructs a unified transcriptomic landscape of lung cancer using RNA-seq data from 1,824 tumors, identifying nine molecular clusters with distinct biological and clinical features.

This study identifies MERTK as a therapeutic target in ZFTA-RELA-driven ependymomas through transcriptomic analysis and drug screening.

The paper identifies PTEN loss as a driver of aggressive NF2 mutant meningiomas and validates a cross-species model for preclinical testing.

This paper uses topic modeling to identify developmental states in medulloblastoma subtypes across multiple datasets and technologies.

This study identifies two critical m6A-regulated developmental windows in human erythropoiesis, showing m6A-MTase activity is essential for proliferation and differentiation in BFU-E and CFU-E.

The paper constructs a transcriptomic landscape for medulloblastoma and ependymoma, revealing subtype-specific biology through clustering and molecular signatures.

This study characterizes quiescent subpopulations and proliferative compartments in glioblastoma, finding that quiescent cells continuously cycle and lack stable dormant states.

The paper designs TrkA agonists that activate neuronal differentiation and neurite outgrowth without triggering pain-related gene expression.

This study combines bulk RNA-Seq datasets to create a transcriptomic landscape of meningiomas, identifies subtypes with distinct biology and prognosis, and provides an interactive tool for outcome prediction.

The paper shows that aggressive high-grade NF2 mutant meningiomas have reduced YAP1 activity due to increased VGLL4 and FAT3/4 expression, linked to higher recurrence risk.

The paper combines five RNA-seq datasets to create a reference landscape of human brain tissues and tumors, enabling comparative gene expression analysis across normal and neoplastic samples.

The paper shows that RNA-seq processing pipelines produce inconsistent abundance estimates for over 12% of genes in at least 1% of samples, raising clinical concerns.