Unlock your biological data


Try: RNA sequencing CRISPR Genomic databases DESeq

1 - 50 of 140 results
filter_list Filters
language Programming Language
healing Disease
settings_input_component Operating System
tv Interface
computer Computer Skill
copyright License
1 - 50 of 140 results
A clustering analysis platform to promote streamlined evaluation, comparison and reproducibility of clustering results in the future. This allowed us to objectively evaluate the performance of all tools on all data sets with up to 1,000 different parameter sets each, resulting in a total of more than 4 million calculated cluster validity indices. ClustEval allows biomedical researchers to pick the appropriate tool for their data type and allows method developers to compare their tool to the state of the art.
QUBIC / QUery BICluster
Implements a well-cited biclustering algorithm, QUBIC, for the interpretation of gene expression profile data. The unique features of QUBIC include: (i) biclustering is integrated with analyses functions (i.e. data discretization, query-based biclustering, bicluster expanding, biclusters comparison, heatmap visualization and co-expression network elucidation); (ii) the QUBIC source code is optimized and converted to C++, thus has better memory control and is more efficient than the original QUBIC; (iii) on five large-scale datasets, QUBIC performs the best among four popular tools according to the running time. Biclustering algorithms facilitate researchers in identification of co-expressed gene subsets in their gene expression dataset, and has become a useful approach for the interpretation of gene expression profile data.
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).
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.
Gecko / Gene cluster detection in prokaryotes
Discovers approximate gene clusters. Gecko enables ranking and filtering of the gene clusters. It performs a statistical evaluation of all computed clusters, based on the null model of random gene order. This tool does not require gene clusters to be collinear or monophyletic. It is able to assign P-values to all gene clusters which are based on the number of genomes a cluster is detected in, the number of genes and the degree of conservation.
Allows to visualize complex gene expression analysis results coming from biclustering algorithms. BicOverlapper visualizes the most relevant aspects of the analysis, including expression data, profiling analysis results and functional annotation. It integrates several state-of-the-art numerical methods, such as differential expression analysis, gene set enrichment or biclustering. The tool permits to have an overall view of several expression aspects, from raw data to analysis results and functional annotations.
Provides a way to judge the quality of clustering performed elsewhere on some entities. ClusterJudge is an R package and its judgement is based on some additional entity-attribute information. The software provides several functions, for instance to calculate the mutual information between each attribute of the entity.attribute pairs, to judge clustering using an entity.attribute table, or to convert the Saccharomyces Genome Database (SGD) Gene Id into the systematic name of the gene.
Solves the biclustering problem in a more general form, compared to existing algorithms, through employing a combination of qualitative (or semi-quantitative) measures of gene expression data and a combinatorial optimization technique. The QUBIC algorithm can identify all statistically significant biclusters including biclusters with the so-called ‘scaling patterns’, a problem considered to be rather challenging. Furthermore, the algorithm solves such general biclustering problems very efficiently, capable of solving biclustering problems with tens of thousands of genes under up to thousands of conditions in a few minutes of the CPU time on a desktop computer. rqubic can be a useful tool in transcriptional regulation network prediction.
SCUDO / Signature-based ClUstering for DiagnOstic purposes
An online tool for the analysis of gene expression profiles for diagnostic and classification purposes. SCUDO is based on a method for the clustering of profiles based on a subject-specific, as opposed to disease-specific, signature. This approach relies on construction of a reference map of transcriptional signatures, from both healthy and affected subjects, derived from their respective mRNA or miRNA profiles. A diagnosis for a new individual can then be performed by determining the position of the individual's transcriptional signature on the map.
Allows users to analyze pixelated data in digital imagery. Cluster could be used to facilitate the study of questions concerning the dominant inoculum sources and impact of cluster size on crop loss on a field scale. The application computes the percent area occupied by targeted pixels, identifies the centroids of targeted clusters, and calculates the relative compass angle of orientation for each cluster. Users can deselect anomalous clusters by specifying a size threshold value to exclude smaller targets from the analysis.
VISDA / VIsual Statistical Data Analyzer
Performs progressive, divisive hierarchical clustering and visualization. VISDA is a hierarchical data exploration and clustering approach that can discover and visualize gene clusters, sample clusters, phenotype clusters, and the hierarchical relationships between the detected clusters. The software visualizes data by structure-preserving projections and provides an interface for human-data interaction. The clustering results can enhance users understanding about the underlying biological mechanism and stimulate novel hypotheses for further research.
BicAT / Biclustering Analysis Toolbox
Finds the hidden order-preserving submatrices in the random matrix. BicAT recovers the hidden order-preserving submatrices with a very high success rate. It offers a graphical user interface (GUI) for several existing biclustering and clustering algorithms. The tool provides a number of algorithms to find biclusters (or clusters) within expression data, as well as a number of postprocessing utilities useful for a further analysis of the results. Its main purpose is to help biologists with the analysis and exploration of the gene expression data, e.g. microarrays.
APCluster / Affinity Propagation Clustering
Allows for determining typical cluster members. APCluster provides leveraged affinity propagation and an algorithm for exemplar-based agglomerative clustering that can also be used to join clusters obtained from affinity propagation. The package provides an agglomerative clustering algorithm that is geared towards the identification of meaningful exemplars. It determines a so-called exemplar for each cluster, that is, a sample that is most representative for this cluster.
PACK / Profile Analysis using Clustering and Kurtosis
Finds genes that define either small outlier subgroups or major subdivisions, depending on the sign of kurtosis. The method can also be used as a filtering step, prior to supervised analysis, in order to reduce the false discovery rate. The workflow is based on two steps: PACK (i) provides for each gene separately, the number of clusters present in its one-dimensional expression profile; (ii) computes a measure of non-gaussianity and rank them according to this measure. This measure can be used to pick out genes that are either discriminating approximately sized subgroups or genes that are defining small ‘outlier’ subgroups.
CLIFF / Clustering via Iterative Feature Filtering
Allows users to cluster biological samples using gene expression microarray data. CLIFF combines a clustering process and a feature selection process in a bootstrap-like iterative way. The algorithm can capture the partition that characterizes the samples but is masked in the original high-dimensional feature space. It is generalizable to arbitrary multi-way clustering, either through recursive 2-way cuts or simultaneous use of several eigenvectors.
BCP / Bayesian Change Point
Provides an implementation of the Barry and Hartigan’s product partition model for the normal errors change point problem using Markov Chain Monte Carlo (MCMC). BCP is sensitive enough to catch both larger aberrations that are short lived, and longer aberrations that are of very low magnitude, without significantly increasing the false discovery rate (FDR). It also extends the methodology to regression models on a connected graph. It allows estimation of change point models with multivariate responses. Finally, this methodology provides a natural framework for combining samples.
An improved gene clustering approach for inferring gene signaling pathways from gene microarray data. geneNT has the following features: (i) it tends to group functionally related genes into a tight cluster disregarding whether these genes have similar expression profiles; (ii) it is sufficiently flexible because the calculated network constrained distance matrix can be fitted into many popular distance-based clustering software packages and (iii) the algorithm runs in polynomial time.
Enables scientists to employ annotation information, clustering algorithms and visualization tools in their array data analysis and interpretation strategy. goCluster is a solution that implements a statistical analysis procedure yielding gene lists that are subsequently searched for non-random enrichment of related gene ontology (GO) annotation terms from all three categories (biological process, molecular function and cellular component). The package provides four clustering algorithms and GeneOntology terms as prototype annotation data.
iBBiG / Iterative Binary Biclustering of Genesets
A package based on a bi-clustering algorithm to perform meta-GSA that addresses the shortcomings of ‘ranked list’ meta-GSA approaches. iBBiG scales well when applied to hundreds of datasets, is tolerant to noise characteristic of genomics data and when applied on simulated data, outperforms clustering and bi-clustering methods including hierarchical and k-means clustering, FABIA, COALESCE and Bimax. iBBiG is optimized for meta-analysis of large numbers of diverse genomics data that may have unmatched samples. It does not require prior knowledge of the number or size of clusters. When applied to simulated data, it outperforms commonly used clustering methods, discovers overlapping clusters of diverse sizes and is robust in the presence of noise. In summary, iBBiG provides a simple, robust, rapid and scalable method for meta-GSA.
CAMPAIGN / Clustering Algorithms for Massively Parallel Architectures Including GPU Nodes
Offers data clustering algorithms and tools that are implemented specifically for execution on massively parallel processing architectures. CAMPAIGN provides up to two orders of magnitude speed-up over respective CPU-based clustering algorithms and is intended as an open-source resource. It intends to form the basis for devising new parallel clustering codes specifically tailored to the GPU and other massively parallel architectures.
Identifies gene clusters that exhibit distinctly similar or different gene expression patterns among the comparing sample conditions. TimesVector is a triclustering algorithm which is designed for clustering three-dimensional time series data to capture distinctively similar or different gene expression patterns between two or more sample conditions. This tool identifies clusters with distinctive expression patterns in three steps: (i) dimension reduction and clustering of time-condition concatenated vectors, (ii) post-processing clusters for detecting similar and distinct expression patterns and (iii) rescuing genes from unclassified clusters.
GSOM / Granular Self-Organizing Map
Captures the uncertainty and underlying clusters in the data. GSOM is effective in both clustering samples and developing an unsupervised fuzzy rough feature selection (UFRFS) method for gene selection in microarray data. It integrates the concept of a fuzzy rough set with the Self-Organizing Map (SOM). The genes selected by the UFRFS are not only better in terms of classification accuracy and a feature evaluation index, but also statistically more significant than the related unsupervised methods.
BicNET / Biclustering NETworks
Enables the efficient unsupervised analysis of largescale network data for the discovery of coherent modules with parameterizable homogeneity. BicNET is a biclustering algorithm to discover non-trivial yet coherent modules in weighted biological networks with heightened efficiency. It extends state-of-the-art contributions on pattern-based biclustering with efficient searches on networks, thus enabling the exhaustive discovery of constant, symmetric and plaid models in biological networks.
Identifies potential local patterns, characterized by coherent groups of genes and conditions. Co-clustering employs an alternating minimization scheme and generates coclusters in a “checkerboard” structure. It can correct initial cluster assignments brought up by hierarchical clustering, and the final clustering is comparable to that from spectral initialization. The tool prevents empty clusters by means of the local search strategy, discovers coclusters with coherent expression profiles, resulting in “checkerboard” patterns, many of which contain discriminative genes, and produces very stable sample clustering even with substantial variation in the number of gene clusters.
0 - 0 of 0 results
1 - 2 of 2 results
filter_list Filters
computer Job seeker
Disable 1
thumb_up Fields of Interest
public Country
language Programming Language
1 - 2 of 2 results

By using OMICtools you acknowledge that you have read and accepted the terms of the end user license agreement.