Abstract
There is an increasing demand for accurate and fast metagenome classifiers that can not only identify bacteria, but all members of a microbial community. We used a recently developed concept in read mapping to develop a highly accurate metagenomic classification pipeline named CCMetagen. The pipeline substantially outperforms other commonly used software in identifying bacteria and fungi and can efficiently use the entire NCBI nucleotide collection as a reference to detect species with incomplete genome data from all biological kingdoms. CCMetagen is user-friendly, and the results can be easily integrated into microbial community analysis software for streamlined and automated microbiome studies.
Highlights
Microbial communities in natural and host-associated environments commonly harbor a mix of bacteria, archaea, viruses, and microbial eukaryotes
Bacterial diversity has been extensively studied with high-throughput sequencing (HTS) targeting 16S rDNA markers [1, 2]
Metagenomics and metatranscriptomics are promising tools to bridge the knowledge gap in the diversity of microbial eukaryotes because they are essentially kingdom-agnostic, are less susceptible to amplification bias, and yield a large set of genes that can be used for taxonomic identification
Summary
Microbial communities in natural and host-associated environments commonly harbor a mix of bacteria, archaea, viruses, and microbial eukaryotes. Bacterial diversity has been extensively studied with high-throughput sequencing (HTS) targeting 16S rDNA markers [1, 2]. These do not amplify eukaryotic sequences, and our knowledge on the diversity and distribution of microbial eukaryotes is limited [3, 4]. The problematic amplification step can be bypassed by sequencing the total DNA (metagenome) or RNA (metatranscriptome) in a sample to characterize all the genes contained or expressed within it. Metagenomics and metatranscriptomics are promising tools to bridge the knowledge gap in the diversity of microbial eukaryotes because they are essentially kingdom-agnostic, are less susceptible to amplification bias, and yield a large set of genes that can be used for taxonomic identification
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