CLC Microbial Genomics Module includes tools for microbial community analysis as well as tools for epidemiological typing of microbial isolates.
Microbiome composition analysis based on 16S rRNA and other commonly used metagenome derived amplicon data is fully supported. The primary output of the clustering, tallying and taxonomic assignment processes is an OTU abundance table that lists the abundances of OTUs in the samples under investigation. In addition, analyses based on whole shotgun metagenomic data are also available, leading to taxonomic profiling abundance tables. CLC Microbial Genomics Module also offers the possibility to investigate biological functions associated with complex communities using Gene Ontology (GO) and Pfam databases to annotate whole shotgun metagenomic data in functional abundance tables. All abundance tables are viewable through a number of intuitive visualization options. Secondary analyses include estimations of alpha and beta diversities, in addition to various statistical tests for differential abundance.
Tools for NGS-MLST typing and identification of antimicrobial resistance genes are included in CLC Microbial Genomics Module to enable epidemiological typing of microbial isolates using NGS data. In cases when the precise identity of the isolated species is not known, the tool automatically detects the most closely related reference genome in NCBI's RefSeq bacterial genome collection and the corresponding MLST scheme from MLST.net or PubMLST.org. The powerful new CLC metadata framework allows fast and intuitive browsing, sorting, filtering and selection of samples and associated metadata, including results obtained during analysis. This metadata framework provides a dashboard-like overview for easy filtering and selection of samples for other analyses such as k-mer or SNP tree reconstruction and visualisation for outbreak analysis.
For convenience, expert-configured workflows for microbiome analysis as well as epidemiological typing allow the user to get from raw NGS reads through data processing and statistical analysis to the final graphical results in very few steps. Reference databases and MLST schemes needed to perform the analyses are automatically downloadable using dedicated tools, and can be easily customized to fit the specific needs of your research.