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mixOmics
Permits exploration and integration of highly dimensional datasets. mixOmics proposes multivariate statistical approaches to identify similarities between two heterogeneous datasets. It summarizes information in a smaller data set and aims to highlight the biological entities that are of potential relevance with a strong focus on graphical representation. This tool assists in finding signatures of vaccine effect and allows a better understanding of immunological mechanisms activated by the intervention.
PheNetic
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A user-friendly web server for inferring a sub-network based on probabilistic logical querying. PheNetic extracts from an interactome, the sub-network that best explains genes prioritized through a molecular profiling experiment. Depending on its run mode, PheNetic searches either for a regulatory mechanism that gave explains to the observed molecular phenotype or for the pathways (in)activated in the molecular phenotype. The web server provides access to a large number of interactomes, making sub-network inference readily applicable to a wide variety of organisms. The inferred sub-networks can be interactively visualized in the browser.
SAMNetWeb / Simultaneous Analysis of Multiple Networks
A web-based tool that enables functional enrichment analysis and visualization of high-throughput datasets. SAMNetWeb can analyse two distinct data types (e.g. mRNA expression and global proteomics) simultaneously across multiple experimental systems to identify pathways activated in these experiments and then visualize the pathways in a single interaction network. Through the use of a multi-commodity flow based algorithm that requires each experiment 'share' underlying protein interactions, SAMNetWeb can identify distinct and common pathways across experiments.
pwOmics
A straightforward approach for the integrative analysis of data from different high-throughput technologies based on pathway and interaction models from public databases. pwOmics performs pathway-based level-specific data comparison of coupled human proteomic and genomic/transcriptomic datasets based on their log fold changes. Separate downstream and upstream analyses results on the functional levels of pathways, transcription factors and genes/transcripts are performed in the cross-platform consensus analysis. These provide a basis for the combined interpretation of regulatory effects over time. As high-throughput data are increasingly used to follow time-dependent biological regulation after pertubation, the main benefit of pwOmics is the cross-platform time series analysis functionality, but consensus analysis can be performed also on single time point measurements.
SteinerNet
Provides a convenient tool for using a powerful constrained optimization method to reconstruct signaling and response pathways by integrating multiple ‘omic’ data. SteinerNet seeks a network composed of high-confidence interactions that ultimately link a subset of the omic hits either directly or through intermediate proteins. This is achieved by solving the prize-collecting Steiner tree (PCST) problem. SteinerNet serves a diverse range of researchers who would like to integrate multiple ‘omic’ data sources to reconstruct biologically meaningful pathways.
BCC / Bayesian Consensus Clustering
Simultaneously models the dependence and the heterogeneity of various data sources. BCC is a flexible clustering approach that models both an overall clustering and a clustering specific to each data source. In addition to multisource data, it may be used to compare clustering from different statistical models for a single homogeneous dataset. The software was applied to subtype identification of breast cancer tumor samples using publicly available data from The Cancer Genome Atlas (TCGA).
FSMKL
A variety of learning strategies to boost prediction performance based on the use of all available data. We consider data integration via the use of multiple kernel learning supervised learning methods. We propose a scheme in which feature selection by statistical score is performed separately per data type and by pathway membership. We further consider the introduction of a confidence measure for the class assignment, both to remove some ambiguously labeled datapoints from the training data and to implement a cautious classifier that only makes predictions when the associated confidence is high.
MARIO / MArkov Random fields to Integrate Omics variables
Classifies genes as differential or not differential based on a generalized correlation measure for multiple sequencing-based genomic variables. MARIO is a hierarchical Bayesian model approach for the parallel, integrative analysis of data from several genomic types. It also enables to facilitate the incorporation of information from functional genomic networks. It also allows to perform inference on the gene level even when the sample size is very small.
Plant MetGenMAP
Integrates and analyzes large-scale gene expression and metabolite profile data sets along with diverse biological information such as biochemical pathways and Gene Ontology (GO) terms. Plant MetGenMAP is an analysis and visualization package for plant systems biology. The software consists of three functional components: data management, pathway browser, and data set analyzer. It can assist researchers to generate novel biological hypotheses and derive new conclusions from high-throughput omics data sets.
JIVE / Joint and Individual Variation Explained
Allows general decomposition of variation for the integrated analysis of datasets. JIVE decomposes a dataset into a low-rank approximation capturing joint structure between data types, low-rank approximations capturing structure individual to each data type, and residual noise. It is applicable to datasets with more than two data types and has a simple algebraic interpretation. A JIVE analysis of gene expression and micro-RNA (miRNA) data on Glioblastoma Multiforme tumor samples reveals gene–miRNA associations and provides characterization of tumor types.
MathIOmica
A Mathematica package written in the Wolfram Language that provides bioinformatics utilities for analyzing dynamic omics datasets. MathIOmica addresses the necessity to integrate multiple omics information arising from dynamic profiling in a personalized medicine approach. It provides multiple tools to facilitate bioinformatics analysis, including importing data, annotating datasets, tracking missing values, normalizing data, clustering and visualizing the classification of data, carrying out annotation and enumeration of ontology memberships and pathway analysis. MathIOmica not only helps in the creation of new bioinformatics tools, but also in promoting interdisciplinary investigations, particularly from researchers in mathematical, physical science and engineering fields transitioning into genomics, bioinformatics and omics data integration.
Lemon-Tree
Implements state-of-the-art ensemble methods for module network inference. Lemon-Tree is a “one-stop shop” software suite for module network inference based on previously validated algorithms. The software is able to associate co-expression modules to multiple “regulator” types (expression regulators, structural DNA variants, phenotypic states, etc.) by assigning each of those independently as regulators of a module. It was benchmarked using large-scale datasets of somatic copy-number alterations and gene expression levels measured in glioblastoma samples from The Cancer Genome Atlas (TCGA).
Mergeomics
A computational pipeline to retrieve biological pathways, gene networks, and central regulators critical for disease development. The Mergeomics web server pre-populates a wide range of publically available data sources. It provides curated genomic resources including tissue-specific expression quantitative trait loci, ENCODE functional annotations, biological pathways, and molecular networks, and offers interactive visualization of analytical results. Multiple computational tools including Marker Dependency Filtering (MDF), Marker Set Enrichment Analysis (MSEA), Meta-MSEA, and Weighted Key Driver Analysis (wKDA) can be used separately or in flexible combinations. Users can apply the pipeline to their own data in conjunction with any preloaded data to identify disease-associated pathways, gene networks, and key regulators.
BioVLAB-mCpG-SNP-EXPRESS
Enables to examine the multi-omics integrated analysis and supplies users a way to study their own multi-omics data. It works on the integrated analysis of gene expression, DNA methylation, and genetic variations. BioVLAB-mCpG-SNP-EXPRESS allows user to explore the analysis result at the multiple levels such as the gene, gene set, pathway, and network, and also from the multiple perspectives such as DNA methylation, gene expression, and sequence variation in terms of phenotype differences.
IIS / Integrated Interactome System
An integrative platform with a web-based interface, which integrates four different modules for processing, annotation, analysis and visualization of the interaction profiles of proteins/genes, metabolites and/or drugs of interest. IIS organizes the analysis in a project context and the user can create several projects protected by password. The project is a structure inside the system where researchers can develop and organize their thematic studies, choosing between two types: (i) chromatogram project or (ii) genes/metabolites/drugs project.
MetaComp
Allows comparison of metagenomics and other meta-omics data. MetaComp is a graphical software that incorporates metagenomics, metatranscriptomics, metaproteomics and metabolomics data. The software provides a series of statistical analysis and the visualization for the comparison of functional, physiological and taxonomic signatures in two-, multi- and two-group sample tests. It can automatically select the proper statistical method in two-group sample test. MetaComp can be used in for revealing the relationship between environmental factors and meta-omic samples directly through a nonlinear regression analysis.
MIPHENO
Aims to improve first-pass screening capabilities for large datasets. MIPHENO is a program that bring samples into the same distribution allowing for dataset-wide comparisons. It assists in processing of large datasets prior to Meta analyses combining different data sets from high-throughput experiments. It provides in summary, this tool is a valuable processing platform that can be applied to very diverse measurement types (e.g. gene expression, enzyme kinetics, metabolite amounts).
WPBS / Weighted Power Biological Score
Represents a weighted power scoring framework. The WPBS method entails: (1) extraction of pairwise similarity of yeast Saccharomyces cerevisiae genes, (2) separately rescoring the similarities, obtained from different data sources, (3) power and weight coefficient estimation and then integration of the positive predictive values (PPVs), (4) predicting functions of classified as well as unclassified genes from clusters. The function of a gene is predicted by calculating the functional enrichment of the cluster using Munich Information for Protein Sequences (MIPS) annotation.
multiOmicsViz
Calculates the spearman correlation between the source omics data and other target omics data. multiOmicsViz identifies the significant correlations and plots the significant correlations on the heat map in which the x-axis and y-axis are ordered by the chromosomal location. It contains a function that uses the spearman correlation to identify the significant correlations between two matrices. If the user inputs multiple target omics data, the tool will represent the number of specific significant correlations for the target omics data and the number of common significant correlations among all target omics data.
Grinn
Contains an internal graph database (Neo4j), and an R package for -omic studies. The graph database incorporates data from several databases including KEGG, SMPDB, HMDB, REACTOME, CheBI, UniProt and ENSEMBL. The R package allows reconstruction of different network types e.g. metabolite-protein-gene, metabolite-protein, metabolite-pathway, protein-gene, protein-pathway and gene-pathway. Grinn applies different correlation-based network analyses to estimate relationships among different omics levels independently from domain knowledge, and with the internal graph database it provides rapid integration of domain knowledge i.e. to aid annotation of unknown metabolites.
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