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Serves for the functional analysis of gene expression and genomic data. Babelomics offers the possibility to explore the effects of alteration in gene expression levels or changes in genes sequences within a functional context. It provides user-friendly access to a full range of methods that cover: (1) primary data analysis; (2) a variety of tests for different experimental designs; and (3) different enrichment and network analysis algorithms for the interpretation of the results of such tests in the proper functional context.
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 comprehensive array-based comparative genomic hybridization analysis workflow, integrating computational improvements and functionalities specifically designed for precision medicine. rCGH ensures the traceability of the entire process of individual samples and provides interactive visualization tools allowing to better interpret—and potentially reprocess—genomic profiles, individually. The web-server application can assist oncologists in reviewing copy-number alterations in genomic profiles and identifying matched therapeutic orientations. rCGH supports the major microarray platforms and facilitates profiles interpretation and decision-making through sharable interactive visualizations.
cghRA / Comparative Genomic Hybridization R Analysis
Permits to conduct comparative genomic hybridization (CGH) array analysis. cghRA is based on a mixture of normal distributions, as several other algorithms. It extends many Rgb classes and is fully compliant. Parsing functions can be customized to permits to the software to directly read files from other manufacturers. It can perform several segmentations with distinct parameters and select the copy number model that seems to fit best to the data.
dChip / DNA-Chip Analyzer
Allows analysis of Affymetrix gene expression microarrays and single nucleotide polymorphism (SNP) microarrays. dChip is a microarray analysis software accessible to both biologists and data analysts through a friendly user interface. At the probe level, the model-based approach allows pooling information across multiple arrays and automatic probe selection to handle cross-hybridization and image contamination. High-level analysis in dChip includes comparing samples, hierarchical clustering, loss of heterozygosity (LOH) and copy number analysis of SNP arrays.
Unifies scientific workflows with graph databases for selecting relevant genes according to the evaluated biological systems. GeNNet is an integrated transcriptome analysis platform that includes pre-loaded biological data, pre-processes raw microarray data and conducts a series of analyses including normalization, differential expression inference, clusterization and geneset enrichment analysis. This platform integrates the analytical process of transcriptome data with graph database. It provides a comprehensive set of tools that would otherwise be challenging for non-expert users to install and use.
An application toolbox for analysis and visualization of microarray-comparative genomic hybridization (array-CGH or aCGH) data which is based on Matlab. Full process of aCGH analysis, from denoising of the raw data to the visualization of the desired results, can be obtained via Mat-aCGH straight-forwardly. The main advantage of this toolbox is that it is collection of recent well-known statistical and information theoretic methods and algorithms for analyzing aCGH data. More importantly, the proposed toolbox is developed for multisample analysis which is one of the current challenges in this area. Mat-aCGH is convenient to apply for any format of data, robust against diverse noise and provides the users with valuable information in the form of diagrams and metrics. Therefore, it eliminates the needs of another software or package for multisample aCGH analysis.
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.
Provides detection and reporting of copy number changes. oneClickCGH is a platform-independent array copy number analysis software compatible with data formats used by all major array comparative genomic hybridization (CGH) and single-nucleotide polymorphism (SNP) platforms. User can add sample or patient information, region annotation, interpretation comments and other kinds of useful records to the report. Detected copy number variations can also be added to report document and manually edited.
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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A versatile web server for the analysis and comparison of genomic copy number alterations in multiple samples from any species. waviCGH processes data generated by high density SNP-arrays, array-CGH or copy-number calls generated by any technique. waviCGH includes methods for pre-processing of the data, segmentation, calling of gains and losses, and minimal common regions determination over a set of experiments. The server is a user-friendly interface to the analytical methods, with emphasis on results visualization in a genomic context. Analysis tools are introduced to the user as the different steps to follow in an experimental protocol. All the analysis steps generate high quality images and tables ready to be imported into spreadsheet programs. Additionally, for human, mouse and rat, altered regions are represented in a biological context by mapping them into chromosomes in an integrated cytogenetic browser.
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