MICCA

MICCA processes amplicon sequencing data to produce Operational Taxonomic Unit (OTU) tables, perform taxonomy classifications, and infer phylogenetic trees for marker gene-based microbiome studies such as 16S rRNA, Internal Transcribed Spacer (ITS), and 28S rRNA analyses.

Key Features:

• Quality Filtering: Implements an optimized reads filtering process to improve the accuracy and reliability of downstream analyses.
• OTU Clustering: Uses a de-novo clustering algorithm tailored for inferring Operational Taxonomic Units (OTUs), reported to provide more accurate and robust estimates than existing methods.
• Taxonomy Assignment: Performs taxonomy assignment of sequences to generate taxonomic classifications for microbial community characterization.
• Phylogenetic Tree Inference: Constructs phylogenetic trees to assess evolutionary relationships within microbial communities.
• Modularity: Provides a modular architecture enabling integration of individual processing steps into analysis workflows.

Scientific Applications:

• Microbiota Characterization: Processes marker-gene amplicon datasets to generate OTU tables, taxonomic profiles, and phylogenies for characterizing microbiota across sample types.
• Environmental Microbiome Studies: Enables analysis of environmental microbiomes using 16S rRNA, ITS, and 28S rRNA amplicons for ecological investigations.
• Human-associated Microbiome Research: Supports profiling of human-associated microbiomes to inform medical and clinical research questions.

Methodology:

Computational steps explicitly include quality filtering of reads, de-novo OTU clustering, taxonomy assignment, phylogenetic tree construction, and validation on real and synthetic datasets.