Furnishes data standards for high-throughput biological experiments. FuGE has been developed to facilitate data management and the creation of repositories, software or standards. It can also produce modular data transfer standards and complete workflow formats. It intends to solve the challenges brought by developing data standards that are resilient to evolution in technology and to provide a single format for laboratory workflows.
Provides several R packages for analysis of gene expression (RNA), proteomics profiling and other high-throughput molecular biology data. OOMPA can build object-oriented tools with consistent user interface. Among the different packages, SIBER assists for identifying bimodally expressed genes from next-generation RNAseq data. Another one, SuperCurve, classifies reverse-phase protein array (RPPA) with a generalized linear model and logistic function.
Allows the analysis of gene expression data. EXPANDER gives the user access to a range of microarray analysis algorithms covering the complete analysis process: preprocessing (2) visualizing (3) clustering (4) biclustering and (5) performing downstream analysis of clusters and biclusters such as functional enrichment and promoter analysis. The software incorporates several conventional gene expression analysis algorithms.
An easy-to-use application for microarray, RNA-Seq and metabolomics analysis. For splicing sensitive platforms (RNA-Seq or Affymetrix Exon, Gene and Junction arrays), AltAnalyze will assess alternative exon (known and novel) expression along protein isoforms, domain composition and microRNA targeting. In addition to splicing-sensitive platforms, AltAnalyze provides comprehensive methods for the analysis of other data (RMA summarization, batch-effect removal, QC, statistics, annotation, clustering, network creation, lineage characterization, alternative exon visualization, gene-set enrichment and more).
Enables visualization and statistical analysis of microarray gene expression, copy number, methylation and RNA-Seq data. BRB-ArrayTools provides scientists with software to (1) use valid and powerful methods appropriate for their experimental objectives without requiring them to learn a programming language, (2) encapsulate into software experience of professional statisticians who read and critically evaluate the extensive published literature of new analytic and computational methods, and (3) facilitate education of scientists in statistical methods for analysis of DNA microarray data.
Provides a powerful and integrated platform for the analysis of microarray gene-expression data. J-Express includes a range of analysis tools and a project management system supporting the organization and documentation of an analysis project. The software offers a choice of different unsupervised analysis methods, including clustering and projection methods. J-Express can also serve to analyze any set of objects where each measurement is represented by a multidimensional vector.
Detects co-expressed genes in large platform-specific microarray collections. MEM is a web application for searching gene expression similarity. The software encompasses a variety of conditions, tissues and disease states and incorporates about a thousand datasets for both human and mouse, as well as hundreds of datasets for other model organisms. The results are presented in a graphical user interface that opens up several paths for further data analysis.
Provides researchers with an easy-to-use and comprehensive interface to the functionality of R and Bioconductor packages for microarray data analysis. As a modular open source project, it allows developers to contribute modules that provide support for additional types of data or extend workflows.
Provides a workflow for RNA-Seq-based differential gene expression (DGE) analysis. RobiNA gathers multiple packages and software with the aim of furnishing a cross-platform processing in four main steps: (i) quality assessment and filtering; (ii) mapping the reads to a user-provided reference genome or transcriptome; (iii) perform the experimental design and (iv) statistical analysis of DGE.
Provides several unique features in a modular and flexible system for the analysis of microarray data. The design and modular conception of CARMAweb allows the use of the different analysis modules either individually or combined into an analytical pipeline. CARMAweb performs (i) data preprocessing (background correction, quality control and normalization), (ii) detection of differentially expressed genes, (iii) cluster analysis, (iv) dimension reduction and (v) visualization, classification, and Gene Ontology-term analysis.
A versatile tool to analyze GeneChip Array data at both gene and exon levels. Gene arrays were originally designed to measure genome-wide expression changes. However, their probe design also allows for the analysis of changes at the exon level to identify alternative splicing events. The applicability of GAA was demonstrated by analyzing datasets from heart development and cardiomyocyte differentiation. We were able to identify differentially expressed genes and differentially expressed exons in both datasets and illustrated how the graphical output of GAA helps to recognize different isoforms.
Analyzes Affymetrix GeneChip Exon 1.0 ST arrays (exon arrays) for expression changes of long non-coding RNAs (lncRNAs). noncoder provides the detailed annotation information of lncRNAs. It is equipped with unique features to allow for an efficient search for interesting lncRNAs to be studied further. It can process exon arrays for protein-coding genes and lncRNAs. The software allows for measuring gene expression changes and alternative splicing events of protein-coding genes.
A system for automatic analysis of data from DNA microarray experiments. Raw data are uploaded to the server together with a specification of the data. The server performs normalization, statistical analysis and visualization of the data. The results are run against databases of signal transduction pathways, metabolic pathways and promoter sequences in order to extract more information. The results of the entire analysis are summarized in report form and returned to the user.
An open-source, web-based, suite for the analysis of gene expression and aCGH data. Asterias implements validated statistical methods, and most of the applications use parallel computing, which permits taking advantage of multicore CPUs and computing clusters. Access to, and further analysis of, additional biological information and annotations (PubMed references, Gene Ontology terms, KEGG and Reactome pathways) are available either for individual genes (from clickable links in tables and figures) or sets of genes. These applications cover from array normalization to imputation and preprocessing, differential gene expression analysis, class and survival prediction and aCGH analysis.
A Food and Drug Administration (FDA) bioinformatics tool that has been widely adopted by the research community for genomics studies. It provides an integrated environment for microarray data management, analysis and interpretation. Most of its functionality for statistical, pathway and gene ontology analysis can also be applied independently to data generated by other molecular technologies. ArrayTrack has added capability to manage, analyse and interpret proteomics and metabolomics data after quantification of peptides and metabolites abundance, respectively. Annotation information about single nucleotide polymorphisms and quantitative trait loci has been integrated to support genetics-related studies. Other extensions have been added to manage and analyse genomics data related to bacterial food-borne pathogens. By providing powerful but easy-to-use utilities, ArrayTrack is positioned to assist in making integrated, contextualised analyses more common, which, in turn, will help to harness genetic knowledge to improve the protection of public health.
A graphical user interface (GUI) based on R-Tcl/Tk for the exploration and linear modeling of data from two-color spotted microarray experiments, especially the assessment of differential expression in complex experiments. limmaGUI provides an interface to the statistical methods of the limma package for R. It provides point and click access to a range of methods for background correction, graphical display, normalization, and analysis of microarray data. Arbitrarily complex microarray experiments involving multiple RNA sources can be accomodated using linear models and contrasts. Empirical Bayes shrinkage of the gene-wise residual variances is provided to ensure stable results even when the number of arrays is small. Integrated support is provided for quantitative spot quality weights, control spots, within-array replicate spots and multiple testing.
A web-based system for management and analysis of transcriptomic data. EMMA 2 allows mapping of gene expression data onto proteome data or pathways and vice versa. It provides extensible analysis and visualization Plug-Ins via the R-language. EMMA 2 now supports the MAGE-ML XML-language for the interchange of microarray data. With EMMA you can do normalization of single and multiple microarrays and run statistical tests for inferring differentially expressed genes. You can also run cluster analysis to find co-regulated genes.
Provides access to a variety of QC metrics for assessing the quality of RNA samples and of the intermediate stages of sample preparation and hybridization. Simpleaffy also offers fast implementations of popular algorithms for generating expression summaries and detection calls. It is designed to work alongside the core ‘affy’ package from BioConductor and provides high level functions for reading Affy .CEL files, phenotypic data, and then computing simple things with it, such as t-tests, fold changes and the like.
A program for microarray data analysis, which features the false discovery rate for testing statistical significance and the principal component analysis using the singular value decomposition method for detecting the global trends of gene-expression patterns. The NIA Array Analysis software can be used for both single-color and two-color microarrays with or without a dye swap. Additional features include analysis of variance with multiple methods for error variance adjustment, correction of cross-channel correlation for two-color microarrays, identification of genes specific to each cluster of tissue samples, biplot of tissues and corresponding tissue-specific genes, clustering of genes that are correlated with each principal component (PC), three-dimensional graphics based on virtual reality modeling language and sharing of PC between different experiments. The software also supports parameter adjustment, gene search and graphical output of results. It uses a tab-delimited text file as an input and generates outputs in both graphics and text formats.