SimkaMin

SimkaMin estimates genomic similarity between metagenomic read sets from next-generation sequencing (NGS) datasets using shared k-mers and a subsampling scheme to compute Bray-Curtis and Jaccard dissimilarities.

Key Features:

• Shared k-mer estimation: Uses the fraction of shared k-mers between read sets to estimate genomic similarity.
• Data subsampling scheme: Applies an efficient subsampling strategy to enable rapid and accurate estimation of Bray-Curtis and Jaccard dissimilarities.
• Scalability: Capable of processing up to one billion metagenomic reads in less than three minutes.
• Low resource footprint: Operates with approximately 0.3 GB of disk space and 1.09 GB of memory usage.

Scientific Applications:

• Comparative metagenomics: Rapidly compares genomic similarities and computes Bray-Curtis and Jaccard dissimilarities between metagenomic read sets.
• Large-scale metagenomic surveys: Enables similarity estimation across extensive NGS datasets and high-throughput read collections.
• Microbial ecology and environmental microbiology: Facilitates comparison of community composition and diversity between environmental metagenomic samples.

Methodology:

Estimates genomic similarity from the fraction of shared k-mers and computes Bray-Curtis and Jaccard dissimilarities using an efficient data subsampling scheme.