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OOMPA / Object-Oriented Microarray and Proteomics Analysis
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.
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.
MEM / Multi Experiment Matrix
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.
InCroMAP / Integrated analysis of Cross-platform MicroArray and Pathway data
A tool for the analysis and visualization of high-level microarray data from individual or multiple different platforms. Currently, InCroMAP supports mRNA, microRNA, DNA methylation and protein modification datasets. Several methods are offered that allow for an integrated analysis of data from those platforms. The available features of InCroMAP range from visualization of DNA methylation data over annotation of microRNA targets and integrated gene set enrichment analysis to a joint visualization of data from all platforms in the context of metabolic or signalling pathways.
CARMAweb / Comprehensive R-based Microarray Analysis web service
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.
Allows the automated application of complex analyses to microarray data which can range from single slides to large data sets including replicates and dye-swaps. It handles output from most commonly used quantification software packages for dual-labelled arrays. Application features range from quality assessment of slides through various data visualizations to multi-step analyses including normalization, detection of differentially expressed genes, and comparison and highlighting of gene lists. A highly customizable action set-up facilitates unrestricted arrangement of functions, which can be stored as action profiles. A unique combination of web-based and command-line functionality enables comfortable configuration of processes that can be repeatedly applied to large data sets in high throughput. The output consists of reports formatted as standard web pages and tab-delimited lists of calculated values that can be inserted into other analysis programs. Additional features, such as web-based spreadsheet functionality, auto-parallelization and password protection make this a powerful tool in microarray research for individuals and large groups alike.
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.
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.
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.
GAA / Gene Array Analyzer
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.
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.
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 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.
NIA Array Analysis
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.
M-CHiPS / Multi-Conditional Hybridization Intensity Processing System
A data warehousing concept, focuses on providing both structure and algorithms suitable for statistical analysis of a microarray database's entire contents including the experiment annotations. It addresses the rapid growth of the amount of hybridization data, more detailed experimental descriptions, and new kinds of experiments in the future. We have developed a storage concept, a particular instance of which is an organism-specific database. Although these databases may contain different ontologies of experiment annotations, they share the same structure and therefore can be accessed by the very same statistical algorithms.
MMAD / Microarray Microdissection with Analysis of Differences
Performs robust tissue microdissection in silico, and improves the detection of differentially expressed genes. MMAD is a computational approach that (i) allows for simultaneous estimation of cell fractions and gene expression profiles of contributing cell types, (ii) adjusts for microarray normalization bias, (iii) uses the corrected Akaike information criterion during model optimization to minimize overfitting and (iv) provides mechanisms for comparing gene expression and cell fractions between samples in different classes.
Allows storage, analysis, and integration of microarray data and related genotype and phenotype data. PhenoGen enables researchers to mine data from microarray expression experiments. The website allows access to and manipulation of whole brain gene expression data from the BXD recombinant inbred (RI) mouse panel, the HXB/BXH RI rat panel, and several commonly used inbred strains of mice. It also offers the ability for extensive quality control, user-generated datasets, and a broad choice of statistical analysis.
Harnesses the advanced statistical analysis functions of the R/BioConductor project. Robin implements streamlined workflows that guide the user through all steps of two-color, single-color, or Affymetrix microarray analysis. It provides functions for thorough quality assessment of the data and automatically generates warnings to notify the user of potential outliers, low-quality chips, or low statistical power. Robin includes both integrated help and comprehensive external documentation.
Exposes a statistical model capable of accurately inferring the composition of peripheral whole blood samples from Affymetrix Gene ST expression profiles. The strategy adopted to derive Enumerateblood is readily applicable to other tissues and/or platforms, which would allow for the development of tools to accurately segment and quantify a variety of admixed tissues from their gene expression profiles, to account for cellular heterogeneity across indications or model interactions between gene expression, some cell types and the indication under study. Enumerateblood outperforms other current methods when applied to Gene ST data. By allowing a more complete study of the various components of the immune compartment of blood from whole blood gene expression, this model will significantly improve the ability to study disease pathobiology in blood, and may generate novel insights from existing Affymetrix Gene ST blood gene expression datasets.
Provides access to multiple algorithms for tasks in statistical microarray analysis. ArrayMining consists of six main modules for microarray analysis: Cross-Study Normalization, Gene selection, Class Discovery, Class Assignment, Network Analysis, and Gene Set Analysis. The software allows users to analyze arbitrary DNA-chip data and other high-dimensional data sets. Depending on the chosen module and algorithm, the data can be forwarded to further analysis modules and is interlinked with annotation data from external web-tools and data bases.
Offers a convenient platform for bench biologists to access several cutting-edge microarray data analysis tools. Microarray analysis using WebArray can be executed in three steps: 1) uploading and managing files; 2) selecting datasets and methods for analysis; 3) browsing results. Partial web page for dual color array data analysis. While more sophisticated programs are available commercially, WebArray represents an excellent free open source software for microarray analysis that can be used by an average biologist after moderate training. To help biologists to understand the underlying statistics methods, we provide detailed explanations and references for most WebArray functions in the help document.
MARS / Microarray Analysis and Retrieval System
Provides a comprehensive MIAME (Minimum Information About a Microarray Experiment) supportive suite for storing, retrieving, and analyzing multi-color microarray data. MARS comprises a laboratory information management system (LIMS), a quality control management, as well as a sophisticated user management system. MARS is fully integrated into an analytical pipeline of microarray image analysis, normalization, gene expression clustering, and mapping of gene expression data onto biological pathways. The incorporation of ontologies and the use of MAGE-ML (MicroArray Gene Expression Markup Language) enables an export of studies stored in MARS to public repositories and other databases accepting these documents.
AMDA / Automated Microarray Data Analysis
Provides scientists an easy and integrated system for the analysis of Affymetrix microarray experiments. AMDA covers all of steps, performing a full data analysis, including image analysis, quality controls, normalization, selection of differentially expressed genes, clustering, correspondence analysis and functional evaluation. Finally a LaTEX document is dynamically generated depending on the performed analysis steps. The generated report contains comments and analysis results as well as the references to several files for a deeper investigation.
D-MaPs / DNA-Microarray Projects
Allows management and processing of microarray data obtained from different experimental designs, equipment and platforms. D-MaPs is a web-based system implemented to manage GeneTAC, ScanArray, CodeLink, NimbleGen and Affymetrix microarray platforms. The software provides, for each user, a separate workspace, called projects, for storing and analyzing microarray data. It allows researchers who are unfamiliar with computation and statistics to obtain results of differential gene expression.
Uses functions of various other packages together with other functions in a coordinated way to handle and analyse cDNA microarray data. maigesPack helps with the data organization and also with the analysis process. Besides that, it makes the analysis more robust, reliable and reproducible. The package aggregates several data analysis procedures reported in the literature, for instance: cluster analysis, differential expression, supervised classifiers, relevance networks and functional classification of gene groups or gene networks.
Integrates the analysis of the hybridization signal with the genomic position of probes and identifies portions of the genome transcribing for mRNAs. PIPE-chipSAD is a pipeline for bacterial transcriptome studies based on high-density microarray experiments. It includes a procedure, align-chipSAD, to build a multiple alignment of transcripts originating in the same locus in multiple experiments and provides a method to compare mRNA expression across different conditions. Finally, the pipeline includes anno-chipSAD a method to annotate the detected transcripts in comparison to the genome annotation.
Array Studio
Provides the premier enterprise solution for data content, framework and graphical user interface for omic and next-generation sequencing data analysis. Array Suite (Array Studio and Array Server) differs from standard desktop solutions or open source solutions, with Array Studio providing the graphical user interface for NGS and omic analysis and visualization and Array Server providing the enterprise back-end solution for project management, sample management and data storage.
Qlucore Omics Explorer
Assists users in investigating normalized omics large datasets. Qlucore Omics Explorer is a modular platform divided into four main functionalities (i) Visualization includes features for generating various plots types including principal component analysis (PCA) plots and real-time visualization; (ii) Exploration furnishes tools for comparing, browsing and selecting targeted information (iii) Analysis includes statistical methods such as quadratic regression, f-tests, or ANOVA and (iv) Sharing allows users to export results as multiple formats including videos or variable lists.
Allows visualization and analysis of data generated on Illumina array platforms. GenomeStudio is a data analysis tool that provides three modules: (1) Genotyping Module for the analysis of single nucleotide polymorphism (SNPs) and copy number variations (CNVs) data and detection of sample outliers, (2) Gene Expression Module for the detection of cytosine methylation at single-base resolution and identification of methylation signatures across the entire genome, and (3) Polyploid Genotyping Module for the analysis of polyploid organism genotyping data.
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