SeqScreen: accurate and sensitive functional screening of pathogenic sequences via ensemble learning.
Genome Biol
; 23(1): 133, 2022 06 20.
Article
en En
| MEDLINE
| ID: mdl-35725628
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 .
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Aprendizaje Automático
Tipo de estudio:
Diagnostic_studies
/
Screening_studies
Límite:
Humans
Idioma:
En
Revista:
Genome Biol
Asunto de la revista:
BIOLOGIA MOLECULAR
/
GENETICA
Año:
2022
Tipo del documento:
Article
País de afiliación:
Estados Unidos