SeqScreen: accurate and sensitive functional screening of pathogenic sequences via ensemble learning
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 atwww.gitlab.com/treangenlab/seqscreen.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jun 20, 2022
- Source ID
- 10.1186/s13059-022-02695-x
Entities
People
- Advait Balaji
- Anthony D. Kappell
- Bryce Kille
- Daniel J. Nasko
- Dreycey Albin
- Gene D. Godbold
- Krista L Ternus
- Madeline Diep
- Mihai Pop
- Nidhi Shah
- R A Leo Elworth
- Santiago Segarra
- Todd J Treangen
- Zhiqin Qian
Organizations
- Division of Computer and Network Systems
- Division of Intramural Research, National Institute of Allergy and Infectious Diseases
- Intelligence Advanced Research Projects Activity
- National Science Foundation Directorate for Biological Sciences
- United States National Library of Medicine