This paper develops CRISPR-Cas13a-based diagnostics for real-time detection of Ebola and Lassa viruses in field settings, demonstrating sensitivity and usability in clinical samples.

Nature Communications · Aug 17, 2020Peer-reviewedMolecular Biology

Machine-Learning prognostic models from the 2014-16 Ebola outbreak: data-harmonization challenges, validation strategies, and mHealth applications

Andrés Colubri, Mary‐Anne Hartley, Matthew Siakor, Vanessa Wolfman, August Felix, Tom Sesay, Jeffrey G. Shaffer, Robert F. Garry, Donald Samuel Grant, Adam C. Levine, Pardis Christine Sabeti

The paper develops machine-learning prognostic models for Ebola virus disease using data from the 2014-16 outbreak, validated across independent datasets and integrated into a mobile health app.

EClinicalMedicine · May 1, 2019Peer-reviewedBioinformatics

Capturing diverse microbial sequence with comprehensive and scalable probe design

Hayden C. Metsky, Katherine J. Siddle, Adrianne D. Gladden, James Qu, David K. Yang, Patrick Brehio, Andrew Goldfarb, Anne L Piantadosi, Shirlee Wohl, Amber Carter, Aaron E. Lin, Kayla G. Barnes, Damien C. Tully, Björn Corleis, Scott Hennigan, , Christian B. Matranga

The paper introduces CATCH, a method for designing compact and scalable probe sets to enhance metagenomic sequencing sensitivity, demonstrating improved viral genome recovery and diversity preservation.

Nature Biotechnology · Feb 1, 2019Peer-reviewedGenomics