Stephanie J. Spielman

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.

LEISR infers relative evolutionary rates for protein and nucleotide data, extending Rate4Site to handle both data types and larger datasets.

The paper predicts eGFP's tolerance to amino-acid deletions using structural properties, finding that weighted contact number is the best single predictor.

The paper challenges claims of rapid Ebola virus evolution during the 2014 outbreak, attributing the apparent increased substitution rate to technical artifacts rather than adaptive evolution.

The paper evaluates how structural properties predict evolutionary sequence variation in viral proteins and finds that buriedness and packing density are better predictors than flexibility or design-based measures.