Genestrip: exact and efficient read classification for selected groups of organisms.

Area of Science:

• Computational Biology
• Bioinformatics
• Genomics

Background:

• Metagenomic analysis faces memory constraints with large k-mer databases.
• Existing tools like KrakenUniq require substantial memory resources.
• Efficient k-mer database management is crucial for large-scale genomic studies.

Purpose of the Study:

• To develop a memory-efficient tool for k-mer database creation and metagenomic analysis.
• To provide high-quality read classification with reduced computational overhead.
• To enable flexible and customizable k-mer database generation for specific organism groups.

Main Methods:

• Genestrip focuses on configurable groups of organisms to build k-mer databases.
• It assigns the most suitable lowest common ancestor taxon for each k-mer, considering external genomes.
• The tool avoids k-mer compression, thus preventing false positives from information loss.

Main Results:

• Genestrip produces databases and results comparable to KrakenUniq but with significantly less memory.
• Databases can cover thousands of species and are generated on a regular PC within hours.
• The tool achieves high precision and recall in read classification, improving recall with small, deep databases.

Conclusions:

• Genestrip enables memory-efficient database creation and analysis with favorable runtimes and high classification quality.
• Users can create small, deep k-mer databases for specific needs, even on regular PCs.
• The approach enhances read classification accuracy by avoiding k-mer compression-related errors.