AMAISE: a machine learning approach to index-free sequence enrichment

Meera Krishnamoorthy; Piyush Ranjan; John R. Erb-Downward; Robert P. Dickson; Jenna Wiens

doi:10.1038/s42003-022-03498-3

Communications Biology (Jun 2022)

AMAISE: a machine learning approach to index-free sequence enrichment

Meera Krishnamoorthy,
Piyush Ranjan,
John R. Erb-Downward,
Robert P. Dickson,
Jenna Wiens

Affiliations

Meera Krishnamoorthy: Division of Computer Science and Engineering, Department of Electrical Engineering and Computer Science, University of Michigan
Piyush Ranjan: Division of Pulmonary & Critical Care Medicine, Department of Medicine, University of Michigan
John R. Erb-Downward: Division of Pulmonary & Critical Care Medicine, Department of Medicine, University of Michigan
Robert P. Dickson: Division of Pulmonary & Critical Care Medicine, Department of Medicine, University of Michigan
Jenna Wiens: Division of Computer Science and Engineering, Department of Electrical Engineering and Computer Science, University of Michigan

DOI: https://doi.org/10.1038/s42003-022-03498-3
Journal volume & issue: Vol. 5, no. 1
pp. 1 – 11

Abstract

Read online

A machine learning-based tool, AMAISE, separates microbial and host sequences in metagenomics data without relying on reference genomes to remove host sequences.

Published in Communications Biology

ISSN: 2399-3642 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science: Biology (General)
Website: https://www.nature.com/commsbio/

About the journal