SeqScreen: accurate and sensitive functional screening of pathogenic sequences via ensemble learning

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...

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Detalles Bibliográficos
Autores principales: Balaji, Advait, Kille, Bryce, Kappell, Anthony D., Godbold, Gene D., Diep, Madeline, Elworth, R. A. Leo, Qian, Zhiqin, Albin, Dreycey, Nasko, Daniel J., Shah, Nidhi, Pop, Mihai, Segarra, Santiago, Ternus, Krista L., Treangen, Todd J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Software
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9208262/
https://www.ncbi.nlm.nih.gov/pubmed/35725628
http://dx.doi.org/10.1186/s13059-022-02695-x
Descripción
Sumario: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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-022-02695-x.

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