CAIM: coverage-based analysis for identification of microbiome
Issued Date
2024-07-25
Resource Type
eISSN
14774054
Scopus ID
2-s2.0-85203112809
Pubmed ID
39222062
Journal Title
Briefings in bioinformatics
Volume
25
Issue
5
Rights Holder(s)
SCOPUS
Bibliographic Citation
Briefings in bioinformatics Vol.25 No.5 (2024)
Suggested Citation
Acheampong D.A., Jenjaroenpun P., Wongsurawat T., Kurilung A., Pomyen Y., Kandel S., Kunadirek P., Chuaypen N., Kusonmano K., Nookaew I. CAIM: coverage-based analysis for identification of microbiome. Briefings in bioinformatics Vol.25 No.5 (2024). doi:10.1093/bib/bbae424 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/101170
Title
CAIM: coverage-based analysis for identification of microbiome
Corresponding Author(s)
Other Contributor(s)
Abstract
Accurate taxonomic profiling of microbial taxa in a metagenomic sample is vital to gain insights into microbial ecology. Recent advancements in sequencing technologies have contributed tremendously toward understanding these microbes at species resolution through a whole shotgun metagenomic approach. In this study, we developed a new bioinformatics tool, coverage-based analysis for identification of microbiome (CAIM), for accurate taxonomic classification and quantification within both long- and short-read metagenomic samples using an alignment-based method. CAIM depends on two different containment techniques to identify species in metagenomic samples using their genome coverage information to filter out false positives rather than the traditional approach of relative abundance. In addition, we propose a nucleotide-count-based abundance estimation, which yield lesser root mean square error than the traditional read-count approach. We evaluated the performance of CAIM on 28 metagenomic mock communities and 2 synthetic datasets by comparing it with other top-performing tools. CAIM maintained a consistently good performance across datasets in identifying microbial taxa and in estimating relative abundances than other tools. CAIM was then applied to a real dataset sequenced on both Nanopore (with and without amplification) and Illumina sequencing platforms and found high similarity of taxonomic profiles between the sequencing platforms. Lastly, CAIM was applied to fecal shotgun metagenomic datasets of 232 colorectal cancer patients and 229 controls obtained from 4 different countries and 44 primary liver cancer patients and 76 controls. The predictive performance of models using the genome-coverage cutoff was better than those using the relative-abundance cutoffs in discriminating colorectal cancer and primary liver cancer patients from healthy controls with a highly confident species markers.