gNOMO: a multi-omics pipeline for integrated host and microbiome analysis of non-model organisms

Abstract

The study of bacterial symbioses has grown exponentially in the recent past. However, existing bioinformatic workflows of microbiome data analysis do commonly not integrate multiple meta-omics levels and are mainly geared toward human microbiomes. Microbiota are better understood when analyzed in their biological context; that is together with their host or environment. Nevertheless, this is a limitation when studying non-model organisms mainly due to the lack of well-annotated sequence references. Here, we present gNOMO, a bioinformatic pipeline that is specifically designed to process and analyze non-model organism samples of up to three meta-omics levels: metagenomics, metatranscriptomics and metaproteomics in an integrative manner. The pipeline has been developed using the workflow management framework Snakemake in order to obtain an automated and reproducible pipeline. Using experimental datasets of the German cockroach Blattella germanica, a non-model organism with very complex gut microbiome, we show the capabilities of gNOMO with regard to meta-omics data integration, expression ratio comparison, taxonomic and functional analysis as well as intuitive output visualization. In conclusion, gNOMO is a bioinformatic pipeline that can easily be configured, for integrating and analyzing multiple meta-omics data types and for producing output visualizations, specifically designed for integrating paired-end sequencing data with mass spectrometry from non-model organisms.

Integrated multi-omics analyses of microbial communities: a review of the current state and future directions

“…gNOMO was employed in an integrative multi-omics analysis of hindgut samples of Blattella germanica, a non-model the German cockroach species by using metagenomics, metatranscriptomics and metaproteomics datasets. 102 Moreover, human gut microbiota samples were processed with this pipeline by combining metagenomics and metaproteomics data. 102 mmvec.…”

“…102 Moreover, human gut microbiota samples were processed with this pipeline by combining metagenomics and metaproteomics data. 102 mmvec. Microbe-metabolite vectors (mmvec) uses a machine learning neural network to estimate the conditional probabilities of metabolites in the presence of a specific microorganism.…”

“…gNOMO is a bioinformatics pipeline specifically designed to process and analyse metagenomics, metatranscriptomics and metaproteomics data in an integrative manner. 102 This pipeline accepts raw sequencing and spectra data as inputs and executes all analysis steps from pre-processing to integrative analysis and visualization. gNOMO integrates metagenomics and metatranscriptomics datasets based on assignments to the same functional categories, uses metagenomics and metatranscriptomics data to create a custom database for metaproteomics analysis and finally applies a pathway integration to track microbiome alterations in different levels.…”

“…102 Moreover, human gut microbiota samples were processed with this pipeline by combining metagenomics and metaproteomics data. 102…”

Exploring the Microbiome in Human Reproductive Tract: High-Throughput Methods for the Taxonomic Characterization of Microorganisms

“…Among these, we highlight HONMF, 168 MOFA, 169 and gNOMO. 170 HONMF (Hypergraph-Induced Orthogonal Non-Negative Matrix Factorization) is used to identify and classify microbial samples, which could be various types of microorganisms like bacteria, fungi, or viruses. HONMF is an unsupervised method, which means that it can automatically analyze the data without being trained on specific information.…”