SHAMAN

SHAMAN performs normalization, DESeq2-based generalized linear model differential abundance analysis, and visualization of metagenomic count data to identify differentially abundant taxa and investigate microbial community dynamics.

Key Features:

• Normalization: Applies normalization techniques for count-based NGS data to account for library size and compositional effects.
• Differential abundance analysis: Implements the DESeq2 generalized linear model (Anders and Huber 2010; Love 2014) for statistical testing of count data.
• Taxonomic resolution: Identifies differentially abundant taxa at the genus level within complex microbial communities.
• Visualization: Provides multiple visualization techniques for interpretation of differential abundance and community composition results.
• Standards alignment: Follows protocols and recommendations from McMurdie and Holmes (2014) and Jonsson et al. (2016).
• Count-based NGS support: Designed specifically for analysis of count-based next-generation sequencing (NGS) microbiome datasets.

Scientific Applications:

• Host–pathogen interaction analysis: Investigates how perturbations and infections alter microbiota composition and dynamics.
• Listeria monocytogenes studies: Supports analysis of microbiota shifts associated with Listeria monocytogenes infection, including effects of lineage I strains and the virulence factor Listeriolysin S (LLS), relevant to gastroenteritis and severe invasive disease.

Methodology:

Performs normalization of count-based NGS data and differential testing using the DESeq2 generalized linear model (Anders and Huber 2010; Love 2014), following recommendations from McMurdie and Holmes (2014) and Jonsson et al. (2016).