Jaclyn Taroni

The paper provides a uniformly processed compendium of alternative splicing profiles from pediatric cancer and non-diseased tissues, enabling large-scale analysis of splicing aberrations in pediatric cancers.

The paper introduces a data portal and open-source tools for single-cell transcriptomics of pediatric tumors, providing uniformly processed RNA-seq data and metadata from over 700 samples across 55 cancer types.

OpenPBTA provides a collaborative platform to genomically characterize 1,074 pediatric brain tumors and 22 cell lines, offering analysis modules and somatic datasets for research and clinical applications.

This study evaluates normalization methods for combining microarray and RNA-seq data, finding that quantile and Training Distribution Matching normalization work best for machine learning tasks.

GenomicSuperSignature compares new RNA-seq datasets to public archives for rapid interpretation using precomputed axes of variation.

annoFuse filters and prioritizes oncogenic gene fusions from RNA-Seq data, improving identification of clinically relevant fusions in cancer datasets.

The paper uses statistical and machine learning methods to analyze NF1 tumor genomic data, identifying key signaling pathways and molecular features associated with different tumor types and potential drug targets.

MultiPLIER uses transfer learning from large public data to improve analysis of small rare disease datasets by capturing systemic biological features.

The paper identifies shared molecular pathways between ANCA-associated vasculitis and nephrotic syndrome, highlighting JAK-STAT signaling as a potential therapeutic target.

The paper performs a meta-analysis of gene expression data from SSc clinical trials to identify therapeutic targets and predict treatment response using machine learning.