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Genetic Screens02:46

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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which...
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SeqScreen: accurate and sensitive functional screening of pathogenic sequences via ensemble learning.

Advait Balaji1, Bryce Kille1, Anthony D Kappell2

  • 1Department of Computer Science, Rice University, Houston, TX, USA.

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SeqScreen accurately identifies microbial pathogens using a novel machine learning approach. This tool enhances pathogen detection and synthetic DNA screening by providing functional labels for nucleotide sequences.

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Genomics

Background:

  • The COVID-19 pandemic highlighted the critical need for precise pathogen detection.
  • Characterizing pathogenic genetic sequences remains a significant scientific challenge.
  • Existing methods may lack the specificity required for identifying novel or concerning sequences.

Purpose of the Study:

  • To develop a computational tool, SeqScreen, for accurate characterization of short nucleotide sequences.
  • To implement a machine learning model for assigning taxonomic and functional labels to sequences.
  • To create a specialized database of Functions of Sequences of Concern (FunSoCs) relevant to microbial pathogenesis.

Main Methods:

  • Development of an ensemble machine learning model.
  • Utilizing a custom-curated set of FunSoCs for microbial pathogenesis.
  • Training the model to label protein-coding sequences with FunSoCs.
  • Validation of the model's performance using recall and precision metrics.

Main Results:

  • SeqScreen demonstrates high recall and precision in labeling protein-coding sequences with FunSoCs.
  • The tool effectively characterizes short nucleotide sequences with both taxonomic and functional information.
  • Successful application of machine learning for pathogen-related sequence annotation.

Conclusions:

  • SeqScreen represents a significant advancement in pathogen characterization and synthetic DNA screening.
  • The approach offers a functionally informed method for analyzing genetic sequences.
  • SeqScreen is available as an open-source tool to facilitate research in microbial pathogenesis and biosecurity.