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.
• Shotgun metagenomics and advancements in sequencing technologies enable species-level microbial analysis.

Purpose of the Study:

• To develop a novel bioinformatics tool, Coverage-based Analysis for Identification of Microbiome (CAIM), for precise taxonomic classification and quantification.
• To improve upon existing methods for analyzing both long- and short-read metagenomic data.

Main Methods:

• Developed CAIM, an alignment-based tool utilizing containment techniques and genome coverage for species identification.
• Implemented a nucleotide-count-based abundance estimation method.
• Evaluated CAIM performance on mock and synthetic datasets, comparing it with other leading tools.

Main Results:

• CAIM demonstrated consistent performance in identifying microbial taxa and estimating relative abundances across diverse 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 provides accurate taxonomic profiling and abundance estimation for metagenomic samples.
• The tool effectively handles both long- and short-read data and shows high concordance across different sequencing platforms.
• CAIM's genome-coverage approach offers a robust method for identifying disease-associated microbial markers in complex datasets.