Gives access to many free software tools for sequence analysis. EMBOSS aims to serve the molecular biology community. It permits the creation and the release of software in an open source spirit. This tool is useful for sequence analysis into a seamless whole. It is free of charge and is available in open source.
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.
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.
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.
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.
Identifies methylation quantitative trait loci at high sensitivity. tICA detects biological sources of data variation and gene modules whose expression variation across tumours is driven by copy-number of DNA methylation changes in a cancer context. This software can be applied to any multi-way data tensor to pinpoint complex patterns of variation correlating with phenotypes of interest and the underlying features driving these variations patterns.
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.
Identifies correlative modules in multi-dimensional genomics data. jNMF aims to detect subsets of Messenger RNA (mRNAs), micro-ARN (miRNAs) and methylation markers. It represents features across multiple datasets and reduces the complexity of the data. Moreover, this tool selects associations among sets of different types of variables. It highlights vertical associations between multiple regulatory levels and can reveal significantly disrupted pathways.
Statistically ranks predicted feedforward loops (FFLs) by their explanatory power to account for differential gene and miRNA expression between two biological conditions. dChip-GemiNi combines gene and microRNAs (miRNA) expression profiles available for a disease process and also incorporates regulatory network structure in the form of computationally identified transcription factors (TFs)-miRNA FFLs.
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.
Provides answers to simple questions such as these for the domain of protein folds. PartsList is a web application where properties for a protein fold include: (i) amino acid composition, (ii) alignment information, (iii) fold occurrences in various genomes, (iv) statistics related to motions, (v) absolute expression levels of yeast in different experiments, (vi) relative expression ratios for yeast, worm and Escherichia coli in various conditions, (vii) information on protein–protein interactions (based on whole-genome yeast interaction data and databank surveys) and (Viii) sensitivity of the genes associated with the fold to inserted transposons.
Permits the exploration of patterns among tumor samples arranged relative to one another based on their molecular similarities. TumorMap consists of an analysis and visualization web portal that presents samples on the basis of their molecular profile similarity and attributes associated with samples, such as disease histological subtypes. Furthermore, this platform allows users to build their own interactive maps from several uploaded high-throughput platforms.
Contributes to a framework for easy integration of new analysis algorithms and simple interface for biologists to run and compare algorithms. miXGENE is a tool that permits users to learn from heterogeneous genomic measurements that make use of prior knowledge (PK). It can also give specific learning methods and suggests sample workflows relevant to the given task.
Studies classification-based investigations that leads to increased biological interpretability. LogMiNeR was applied to transcriptional profiling data to better understand differential influenza vaccination responses. It can be applied to classification of many immune as well as non-immune-mediated diseases. This tool presents distinct aspects of the underlying biology while maintaining predictive accuracy.
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).
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.
Allows to integrate different data types. rMKL-LPP is an extension of the multiple kernel learning with dimensional reduction (MKL-DR) method. The Locality Preserving Projections (LPP) allows to conserve the sum of distances for each sample's k-Nearest Neighbors.
Improves the understanding of complex molecular interactions and disease mechanisms for integrative analysis, differential network analysis, and community detection. xMWAS recognizes and displays associations between genes, cytokines, and metabolites. It is based on existing algorithms and provides an automated framework for integrative and differential network analysis of up to four datasets from unpaired or paired study designs.
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.
Performs penalized co-inertia analysis (CIA). pCIA is an R package that implements sparse co-inertia analysis and structured sparse co-inertia analysis models with two sparse CIA methods : sparse CIA (sCIA) and the structured sparse CIA (ssCIA), that both impose penalties on the CIA loading vectors. The software also allows cross validation for the selection of optimal tuning parameters in each model.