Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.09.02.271098v1?rss=1 Authors: Sulit, A. K. L., Kolisnik, T., Frizelle, F. A., Purcell, R., Schmeier, S. Abstract: Background: The identification of functional processes taking place in microbiome communities augment traditional microbiome taxonomic studies, giving a more complete picture of interactions taking place within the community. While there are applications that perform functional annotation on metagenome or metatranscriptomes, very few of these are able to link taxonomic identity to function and are limited by their input types or databases used. Results: Here we present MetaFunc, a workflow which takes input reads, and from these 1) identifies species present in the microbiome sample and 2) provides gene ontology (GO) annotations associated with the species identified. MetaFunc can also provide a differential abundance analysis step comparing species between sample conditions. In addition, MetaFunc allows mapping of reads to a host genome, and separates these reads, before proceeding with the microbiome analyses. From the host reads, MetaFunc is able to identify host genes, perform differential gene expression analysis, and gene-set enrichment analysis. A final correlation analysis between microbial species and host genes can also be performed. Finally, MetaFunc builds an R shiny application that allows users to view and interact with the microbiome results. In this paper we show how MetaFunc can be applied to metatranscriptomic datasets of colorectal cancer. Conclusion: MetaFunc is a one-stop shop microbiome analysis pipeline that can identify taxonomies and their respective functional contributions in a microbiome sample through GO annotations. It can also analyse host reads in a microbiome sample, providing information on host gene expression, and allowing for correlations between the microbiome and host genes. MetaFunc comes with a user-friendly R shiny application that allows for easier visualisation and exploration of its results. MetaFunc is freely available through https://gitlab.com/schmeierlab/workflows/metafunc.git. Copy rights belong to original authors. Visit the link for more info