MetaFunc: taxonomic and functional analyses of high throughput sequencing for microbiomes

Abstract

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 metagenomes or metatranscriptomes, very few of these are able to link taxonomic identity to function or are limited by their input types or databases used. Here we present MetaFunc, a workflow which takes RNA sequences as 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. In addition, MetaFunc allows for host gene analysis, mapping the reads to a host genome, and separating these reads, prior to microbiome analyses. Differential abundance analysis for microbe taxonomies, and differential gene expression analysis and gene set enrichment analysis may then be carried out through the pipeline. 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 showed how MetaFunc can be applied to metatranscriptomic datasets of colorectal cancer.

Bacterial lipopolysaccharide modulates immune response in the colorectal tumor microenvironment

“…After trimming, the 260 samples were run through the MetaFunc pipeline 51 using default settings, except for setting reverse stranded option for featureCounts 52 , a species needing at least 0.01% abundance in at least one of the 260 samples to be included in the microbiome analysis, and setting TaxChoices in the configuration file to include Bacteria, Archaea, Fungi, and Viruses. Databases used were those provided in https://metafunc.readthedocs.io/en/latest/usage.html#databases .…”

Human gene and microbial analyses in rectal cancer complete responses to radiotherapy

“…Raw sequencing data was parsed through the Metafunc pipeline 19 , which performs read preprocessing, host gene mapping and microbiome species identification. Further details of the computational pipeline may be found at https://gitlab.com/schmeierlab/workflows/metafunc , and complete analysis of this article is available at https://gitlab.com/alsulit08/uoc_response_rectalca .…”

“…Expression levels for each human gene and sample were generated by the MetaFunc pipeline 19 , and differential human gene expression analysis (DGEA) using DESeq2 21 was used to detect DEGs. To detect DEGs that were significantly differentially expressed in the tumour relative to each participant’s normal tissue between groups of responders, the model fitted by DESeq2 included covariates for response (complete or other), tissue type (tumour or normal), response:participant (index) and response:tissue.…”