metabolisHMM

metabolisHMM analyzes metagenomic sequencing datasets using curated and custom Hidden Markov Model (HMM) profiles to detect ribosomal proteins and single marker genes, construct phylogenies, and summarize metabolic pathway marker presence for microbial phylogenomic and metabolic investigations.

Key Features:

• Phylogenetic Construction: Utilizes curated and custom Hidden Markov Model (HMM) profiles to detect ribosomal protein sequences and single marker genes and to construct phylogenies.
• Functional Annotation: Performs functional annotation using predefined or user-defined HMM profiles and summarizes presence/absence of metabolic pathway markers within microbial communities.
• Data Visualization: Generates heatmap visualizations representing the distribution and presence/absence summaries of metabolic markers across samples.

Scientific Applications:

• Metagenomic functional profiling: Detects and summarizes metabolic pathway markers from high-throughput metagenomic sequencing datasets to infer community functional potential.
• Microbial phylogenomics: Constructs phylogenies from ribosomal proteins and single marker genes to investigate evolutionary relationships and dynamics of uncultivated microbial lineages.

Methodology:

Integrates curated or user-provided Hidden Markov Model (HMM) profiles to search metagenomic sequencing datasets for ribosomal proteins, single marker genes, and metabolic pathway markers, then builds phylogenies and produces heatmap presence/absence summaries.