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Genome biologyBioinformaticsPeer-reviewed journal article

SeqScreen: Accurate and Sensitive Functional Screening of Pathogenic Sequences via Ensemble Learning

Advait BalajiiD, Bryce L. KilleiD, Anthony D. KappelliD, Gene D. GodboldiD, Madeline M. DiepiD, Ryan A. Leo ElworthiD, Zhiqin Qian, Dreycey AlbiniD, Daniel J. NaskoiD, Nidhi Shah, Mihai PopiD, Santiago SegarraiD, Krista L. TernusiD, Todd J. TreangeniD

PDFDOIPreprintCode

Abstract

The COVID-19 pandemic has emphasized the importance of detecting known and emerging pathogens from clinical and environmental samples. However, robust characterization of pathogenic sequences remains an open challenge. To this end, we developed SeqScreen, which can accurately characterize short nucleotide sequences using taxonomic and functional labels, and a customized set of curated Functions of Sequences of Concern (FunSoCs) specific to microbial pathogenesis. We show our ensemble machine learning model can label protein-coding sequences with FunSoCs with high recall and precision. SeqScreen is a step towards a novel paradigm of functionally informed pathogen characterization and is available for download at: www.gitlab.com/treangenlab/seqscreen

Citations
42
FWCI
2.12
Field percentile
top 11%

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