SeqScreen: accurate and sensitive functional screening of pathogenic sequences via ensemble learning.
Genome Biol
; 23(1): 133, 2022 06 20.
Article
in English
| MEDLINE | ID: covidwho-1896371
ABSTRACT
The COVID-19 pandemic has emphasized the importance of accurate detection of known and emerging pathogens. However, robust characterization of pathogenic sequences remains an open challenge. To address this need we developed SeqScreen, which accurately characterizes 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 synthetic DNA screening and pathogen characterization, available for download at www.gitlab.com/treangenlab/seqscreen .
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Machine Learning
Type of study:
Diagnostic study
Limits:
Humans
Language:
English
Journal:
Genome Biol
Journal subject:
Molecular Biology
/
Genetics
Year:
2022
Document Type:
Article
Affiliation country:
S13059-022-02695-X
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