SPARKI: a tool for the statistical analysis of pathogen identification results

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Kraken 2 is a popular tool for pathogen identification and microbiome analysis.
• Existing downstream tools aid Kraken 2 output interpretation, but a comprehensive statistical framework for single-sample analysis is lacking.

Purpose of the Study:

• Introduce SPARKI, an R package for statistical analysis of Kraken 2 outputs.
• Facilitate pathogen identification in next-generation sequencing (NGS) samples.
• Provide a probabilistic framework to complement existing Kraken 2 analysis tools.

Main Methods:

• Development of the SPARKI R package.
• Integration of SPARKI into an end-to-end pathogen identification pipeline (sparki-nf).
• Availability of an additional pipeline (map-to-genome) for SPARKI results exploration and validation.

Main Results:

• SPARKI enables statistical analysis of Kraken 2 outputs.
• The package aids in identifying pathogens within NGS samples.
• SPARKI introduces a probabilistic perspective to Kraken 2 data analysis.

Conclusions:

• SPARKI enhances pathogen detection by providing statistical insights into Kraken 2 results.
• The R package serves as a valuable discovery tool, complementing methods like KrakenTools, Bracken, and Pavian.
• SPARKI and its associated pipelines offer an automated, end-to-end solution for pathogen identification.