Table 3. Data integration tools for multi-omics.

Algorithms and bioinformatics tools for data integration across multiple omics platforms.

		Types of Omics Data
Tool	Purpose	Metabolomics	Proteomics	Transcriptomics	Pathway Analysis	miRNA	SNP Analysis	Microbiome	DNA Methylation	Copy Number Variants (CNV)	Genomics	Visualization	Pros	Cons	Reference
MetaboAnalyst 4.0	Metabolomics data analysis, interpretation, and integration with other omics data	x	x		x							x	Relatively easy to use. Has a web interface. Basic computer skills.	File size limit (50 Mb), Installed version requires Linux and programming experience.	[143]
Paintomics 3.0 (web based)	Joint visualization of transcriptomics and metabolomics data	x	X		x						x		Relatively easy to use. Automatic feature name conversion. Has a web interface. Basic computer skills.	Most web applications have a file size limit.	[97]
integrOmics (R package)	Integrative analysis of two types of omics datasets	X	x	x				x					Customizable	Requires programming skills in R	[89]
Omics Integrator	Maps Protein data to other data sets	X	x	x	X						X	X	Easy to use web interface. Basic computer skills.	Most web applications have a file size limit. Local installation requires advanced computer skills	[144]
mixOmics (R package)	Data exploration, dimension reduction, and visualization	X	x	x		x						x	Customizable	Requires advanced programming skills, programming in R	[90]
PARADIGM	Extraction of disease-perturbed sub pathways within pathway networks			x	x					x	X		Uses a combination of algorithms to improve accuracy	Requires advanced programming skills, command line interface, programming in R. Pathways are measured independently, and interactions among pathways are not considered.	[145]
Micrographite (R Package)	Pathway analysis of miRNA and gene expression profiles			x	x	x							Customizable, integrates pathway information with predicted and validated miRNA–target interactions.	Requires advanced computer skills, Programming in R.	[91]
iCIusterplus	Integrative clustering of multiple data sets			x					x	x	X		Customizable. Incorporates flexible modeling of the associations between different data types	Requires advanced computer skills, computationally intensive, limitations in statistical inference, programming skills in R	[92]
LRAcluster	Integrative clustering of multiple data sets			x			x		x	x			Fast and efficient unsupervised clustering	Command line interface, requires advanced computer skills.	[93]
GENEASE	disease ontology exploration, analysis, and visualization of multiple databases			x	x		x		x	X	x	X	Web based interface. Uses multiple databases in real time.	Most web applications have a file size limit.	[146]
ProteoClade	Annotate taxa to proteomics data		x					x			x		Customizable. Can work with large data sets. Targeted and De Novo database searches. Good tutorials.	Requires advanced computer skills, Programming in Python.	[147]
Qiime2 (q2-micom)	Metabolic modeling	x						x					Customizable, Highly versatile. Good tutorials.	Steep learning curve. Requires advanced computer skills	[148]
Qiime2 (q2mmvev)	Learning microbiome/metabolic interactions	x						x					Customizable, Highly versatile. Good tutorials.	Steep learning curve. Requires advanced computer skills	[149]
Qiime2 (q2-metabolomics)	Tool to import metabolomic data into Qiime2	x						x					Customizable, Highly versatile. Good tutorials.	Steep learning curve. Requires advanced computer skills	[150]