SSuMMo: rapid analysis, comparison and visualization of microbial communities.

Area of Science:

• Microbiology
• Bioinformatics
• Genomics

Background:

• Next-generation sequencing generates vast datasets that often fail to capture full genetic diversity.
• Analyzing complex microbial communities requires effective tools for comparing datasets.
• Understanding genetic diversity is crucial in various environments.

Purpose of the Study:

• To develop a tool for probabilistic assignment of SSU rRNA gene fragments to taxonomic clades.
• To create consistent and comparable cladograms from sequence data.
• To provide a curatorial tool for identifying inaccuracies in public databases.

Main Methods:

• Developed the Small Subunit Markov Modeler (SSuMMo) as open-source Python software.
• Utilized Hidden Markov Model (HMM) databases for taxonomic assignment.
• Tested accuracy on public reference databases and next-generation sequencing data.

Main Results:

• SSuMMo achieved >90% accuracy in predicting genera from public reference databases.
• Demonstrated parallel visualization of comparable datasets from human microbiome samples.
• Identified numerous incorrect and outdated taxonomic entries in public databases.

Conclusions:

• SSuMMo is a valuable tool for curating and analyzing microbial community datasets.
• The software enables consistent and comparable analysis of genetic diversity.
• Accurate taxonomic assignment is essential for reliable microbiome research.