MIntO: A Modular and Scalable Pipeline For Microbiome Metagenomic and Metatranscriptomic Data Integration

Area of Science:

• Microbiome research
• Bioinformatics
• Computational biology

Background:

• Omics technologies have transformed microbiome research, generating vast amounts of metagenomic and metatranscriptomic data.
• Analysis of this data is often hindered by computational limitations, lack of expertise, and non-standardized workflows.
• Consistent and comparable results across studies are essential for advancing microbiome science.

Purpose of the Study:

• To introduce MIntO (Microbiome Integrated meta-Omics), a versatile and scalable pipeline for integrating metagenomic and metatranscriptomic data.
• To address the limitations in pre-processing and analysis of microbiome omics data.
• To provide a standardized computational workflow for microbiome research.

Main Methods:

• Developed MIntO, a highly versatile pipeline integrating metagenomic and metatranscriptomic data.
• Implemented a scalable approach for data analysis.
• Designed a modular pipeline with three modes for different experimental designs, including de novo assembly.
• Incorporated community turnover and gene expression variations for accurate profile computation.

Main Results:

• MIntO enables the computation of gene expression profiles by integrating metagenomic and metatranscriptomic data.
• The pipeline accounts for community dynamics and gene expression variability.
• It facilitates the generation of metagenome-assembled genomes.
• Provides biochemical insights by linking microbial functions to retrieved genomes.

Conclusions:

• MIntO offers a powerful tool for disentangling mechanisms shaping metatranscriptomes.
• The pipeline enhances understanding of microbial ecology and gene expression.
• It is crucial for advancing knowledge of the microbiome's role in human health and the environment.
• MIntO v1.0.1 is publicly available for use.