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

Area of Science:

• Microbiome research
• Metagenomics
• Bioinformatics

Background:

• Traditional microbiome studies focus on taxonomy, limiting functional insights.
• Existing functional annotation tools struggle to link taxonomic identity with function or have input limitations.

Purpose of the Study:

• To present MetaFunc, a novel workflow for linking microbiome species to their functions.
• To enable integrated analysis of host gene expression and microbial community function.

Main Methods:

• MetaFunc processes RNA sequences to identify species and their associated gene ontology annotations.
• The workflow includes optional host gene analysis and differential expression analysis.
• It performs correlation analysis between microbial species and host genes, with results visualized in an R shiny application.

Main Results:

• MetaFunc successfully links taxonomic identity to functional annotations from RNA sequences.
• The workflow demonstrates utility in analyzing metatranscriptomic data, as shown in a colorectal cancer study.
• Integrated analysis of host and microbial data is facilitated.

Conclusions:

• MetaFunc provides a comprehensive approach to microbiome functional analysis.
• The tool enhances understanding of host-microbe interactions in disease contexts like colorectal cancer.
• MetaFunc offers an interactive platform for exploring complex microbiome data.