Back

Finding Candida auris in public metagenomic repositories

Mario-Vasquez, J. E.; Bagal, U. R.; Lowe, E.; Morgulis, A.; Phan, J.; Sexton, D. J.; Shiryev, S.; Slatkevicius, R.; Welsh, R.; Litvintseva, A. P.; Blumberg, M.; Agarwala, R.; Chow, N. A.

2023-09-01 bioinformatics
10.1101/2023.08.30.555569 bioRxiv
Show abstract

Candida auris is a newly emerged multidrug-resistant fungus capable of causing invasive infections with high mortality. Despite intense efforts to understand how this pathogen rapidly emerged and spread worldwide, its environmental reservoirs are poorly understood. Here, we present a collaborative effort between the U.S. Centers for Disease Control and Prevention, the National Center for Biotechnology Information, and GridRepublic (a volunteer computing platform) to identify C. auris sequences in publicly available metagenomic datasets. We developed the MetaNISH pipeline that uses SRPRISM to align sequences to a set of reference genomes and computes a score for each reference genome. We used MetaNISH to scan [~]300,000 SRA metagenomic runs from 2010 onwards and identified five datasets containing C. auris reads. Finally, GridRepublic has implemented a prospective C. auris molecular monitoring system using MetaNISH and volunteer computing.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.