Fig. 3

Overview of the different steps in the meta-omics analysis workflow. The different workflows are depicted, from left to right for 16s amplicon data, metagenomics data and metatranscriptomic data. The main steps for 16s amplicon data is the definition of OTUs together with taxonomic assignment, followed by statistical analysis. For metagenome data, first steps involve quality control steps, followed by a metagenome assembly. The workflow splits afterwards into two directions, one being the taxonomic assignment, the other one the definition of metagenomic bins and the functional annotation. Genes can be predicted from the genome assembly, which can be functionally profiled. With the coverage information of the genes, it is also possible to define genome bins. After this step is done, the same statistics as for 16s amplicon data can be performed, as well as differential expression/abundance analysis together with pattern detection through machine learning, and finally analysis of the metabolism. The workflow for metatranscriptomic data is in general the same, except that rRNA, which does not provide any information in this setting, needs to be removed before most of the steps, and that no binning is possible with transcriptome data