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Coriolis: enabling metagenomic classification on lightweight mobile devices.

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Portable DNA sequencing needs field-based classification. We developed Coriolis, a metagenomic classifier for mobile devices, offering high throughput and low resource use for real-time DNA analysis.

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Area of Science:

  • Bioinformatics
  • Genomics
  • Mobile Computing

Background:

  • Portable DNA sequencers like Oxford Nanopore MinION enable field sequencing.
  • In-field DNA classification is crucial for actionable metagenomic analysis.
  • Mobile deployments face challenges: limited connectivity and computing power.

Purpose of the Study:

  • To develop strategies for in-field metagenomic classification on mobile devices.
  • To address the need for efficient DNA database deployment on memory-constrained devices.
  • To create a metagenomic classifier optimized for lightweight mobile platforms.

Main Methods:

  • Introduced a programming model for metagenomic classifiers with manageable abstractions.
  • Developed the compact string B-tree for indexing large DNA databases on mobile devices.
  • Integrated these solutions into Coriolis, a mobile-optimized metagenomic classifier.

Main Results:

  • Coriolis demonstrated higher throughput and lower resource consumption compared to state-of-the-art solutions.
  • The compact string B-tree proved viable for deploying massive DNA databases on memory-constrained devices.
  • Experiments confirmed Coriolis's effectiveness on actual MinION metagenomic reads using a portable supercomputer.

Conclusions:

  • Coriolis enables efficient in-field metagenomic classification on mobile devices.
  • The proposed programming model and data structure facilitate resource-constrained bioinformatics.
  • This work supports real-time, actionable metagenomic analysis in remote field settings.