Houtan Noushmehr

The paper integrates single-nucleus RNA and ATAC sequencing to identify cellular states and regulatory networks associated with the G-CIMP epigenomic transition in IDH-mutant gliomas.

This study examines how genomic background and developmental state affect signaling pathways and therapy response in GBM patient-derived cells, finding that differentiation increases TMZ resistance and activates DNA-damage interferon responses.

This study analyzes the epigenetic evolution of IDH1-mutant and IDH1-wildtype gliomas under treatment, showing IDHmut tumors undergo epigenomic changes resembling IDHwt tumors, with implications for treatment strategies.

The paper introduces new features in the TCGAbiolinks R/Bioconductor package to improve cancer data analysis, including better differential expression analysis, tumor purity estimation, and integration of normal samples from GTEx.

ELMER v.2 reconstructs gene regulatory networks from DNA methylation and transcriptome data to identify Master Regulators in diseases like breast cancer.

This study identifies 21 novel candidate susceptibility genes for high grade serous ovarian cancer using transcriptome-wide association analysis across multiple tissue types.

The paper uses transcriptomic and epigenomic profiling to show that high-grade serous ovarian cancers predominantly originate from fallopian tube secretory epithelial cells, with distinct transcription factors driving tumorigenesis based on cell-of-origin.

TCGAbiolinksGUI provides a graphical interface for analyzing GDC cancer data, making the TCGAbiolinks package more accessible to non-R users.

Moonlight identifies cancer driver genes and their roles by integrating functional analysis and gene regulatory networks from expression data, distinguishing tumor suppressors, oncogenes, and moonlight genes.

RGBM identifies transcriptional regulators of glioma subtypes by inferring gene regulatory networks from diverse data sources and outperforming existing methods on benchmark datasets.

The paper identifies an epigenetic shift from G-CIMP-high to G-CIMP-low in gliomas during recurrence, associated with genomic instability and stem cell-like features, and proposes biomarkers for predicting recurrence.