Dataset Information

MoNET: an R package for multi-omic network analysis.

ABSTRACT:

Motivation

The increasing availability of multi-omic data has enabled the discovery of disease biomarkers in different scales. Understanding the functional interaction between multi-omic biomarkers is becoming increasingly important due to its great potential for providing insights of the underlying molecular mechanism.

Results

Leveraging multiple biological network databases, we integrated the relationship between single nucleotide polymorphisms (SNPs), genes/proteins and metabolites, and developed an R package Multi-omic Network Explorer Tool (MoNET) for multi-omic network analysis. This new tool enables users to not only track down the interaction of SNPs/genes with metabolome level, but also trace back for the potential risk variants/regulators given altered genes/metabolites. MoNET is expected to advance our understanding of the multi-omic findings by unveiling their transomic interactions and is likely to generate new hypotheses for further validation.

Availability and implementation

The MoNET package is freely available on https://github.com/JW-Yan/MONET.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Li J

PROVIDER: S-EPMC10060724 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

MoNET: an R package for multi-omic network analysis.

Li Jin J Chen Feng F Liang Hong H Yan Jingwen J

Bioinformatics (Oxford, England) 20220101 4

<h4>Motivation</h4>The increasing availability of multi-omic data has enabled the discovery of disease biomarkers in different scales. Understanding the functional interaction between multi-omic biomarkers is becoming increasingly important due to its great potential for providing insights of the underlying molecular mechanism.<h4>Results</h4>Leveraging multiple biological network databases, we integrated the relationship between single nucleotide polymorphisms (SNPs), genes/proteins and metabol ...[more]

PMID: 34694378

Similar Datasets

Project description:To better understand dynamic disease processes, integrated multi-omic methods are needed, yet comparing different types of omic data remains difficult. Integrative solutions benefit experimenters by eliminating potential biases that come with single omic analysis. We have developed the methods needed to explore whether a relationship exists between co-expression network models built from transcriptomic and proteomic data types, and whether this relationship can be used to improve the disease signature discovery process. A naïve, correlation based method is utilized for comparison. Using publicly available infectious disease time series data, we analyzed the related co-expression structure of the transcriptome and proteome in response to SARS-CoV infection in mice. Transcript and peptide expression data was filtered using quality scores and subset by taking the intersection on mapped Entrez IDs. Using this data set, independent co-expression networks were built. The networks were integrated by constructing a bipartite module graph based on module member overlap, module summary correlation, and correlation to phenotypes of interest. Compared to the module level results, the naïve approach is hindered by a lack of correlation across data types, less significant enrichment results, and little functional overlap across data types. Our module graph approach avoids these problems, resulting in an integrated omic signature of disease progression, which allows prioritization across data types for down-stream experiment planning. Integrated modules exhibited related functional enrichments and could suggest novel interactions in response to infection. These disease and platform-independent methods can be used to realize the full potential of multi-omic network signatures. The data (experiment SM001) are publically available through the NIAID Systems Virology (https://www.systemsvirology.org) and PNNL (http://omics.pnl.gov) web portals. Phenotype data is found in the supplementary information. The ProCoNA package is available as part of Bioconductor 2.13.

Dataset Information

MoNET: an R package for multi-omic network analysis.

Motivation

Results

Availability and implementation

Supplementary information

Publications

MoNET: an R package for multi-omic network analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets