CAIM: Coverage-based Analysis for Identification of Microbiome.

Area of Science:

• Microbial ecology
• Bioinformatics
• Genomics

Background:

• Accurate taxonomic profiling is crucial for understanding microbial ecology.
• Whole shotgun metagenomic (WMS) sequencing advances species-level microbial analysis.
• Existing tools face challenges in precise classification and quantification.

Purpose of the Study:

• To develop a novel bioinformatics tool, CAIM, for accurate taxonomic classification and quantification in metagenomic samples.
• To enhance species identification using genome coverage information and nucleotide-count based abundance estimation.
• To compare CAIM's performance against other leading tools and validate its utility in real-world datasets.

Main Methods:

• Developed CAIM, an alignment-based bioinformatics tool utilizing containment techniques and genome coverage for species identification.
• Implemented a nucleotide-count based abundance estimation method.
• Evaluated CAIM on mock and synthetic datasets, comparing performance with other tools.
• Applied CAIM to Nanopore, Illumina, and fecal shotgun metagenomic datasets.

Main Results:

• CAIM demonstrated consistent performance in identifying microbial taxa and estimating relative abundances across various datasets.
• High similarity in taxonomic profiles was observed between Nanopore and Illumina sequencing platforms.
• Models using CAIM's genome-coverage cutoff showed superior predictive performance in discriminating cancer patients from controls compared to relative abundance cutoffs.

Conclusions:

• CAIM offers a robust and accurate method for taxonomic profiling in both long- and short-read metagenomic data.
• The tool effectively filters false positives and provides reliable abundance estimations.
• CAIM's genome-coverage based approach holds promise for identifying disease-specific microbial markers in clinical metagenomics.