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Estimates gene and eQTL networks from high-throughput expression and genotyping assays. qpgraph is based in the so-called q-order limited partial correlation graphs, qp-graphs, which is specifically tailored towards molecular network discovery from microarray expression data. qp-graphs yield more stable performance figures than other state-of-the-art methods when the ratio of genes to experiments exceeds one order of magnitude. More importantly, the better performance of the qp-graph method on such a gene-to-sample ratio has a decisive impact on the functional coherence of the reverse-engineered transcriptional regulatory modules and becomes crucial in such a challenging situation in order to enable the discovery of a network of reasonable confidence that includes a substantial number of genes relevant to the essayed conditions.
SPELL / Serial Pattern of Expression Levels Locator
Enables biology researchers to explore the entirety of very large microarray compendia in a biologically meaningful manner. SPELL is a scalable, context-specific search methodology implemented in an interactive, web-based search engine. The web-interface allows a researcher to provide a list of query genes, then the search engine reports which datasets are most relevant to that query, lists additional genes related to the query within the relevant conditions and displays the expression levels of these genes.
Assists users in research on microarrays, proteomics and metabolomics. irMF uses non-negative matrix factorization to create groups of genes and sets of predictors. It uses non-negative matrix factorization (NMF) to capture correlation structures in the dataset and then utilizes a sequential form of testing to control the error rate of the procedure. Several functions are permitted with this tool, it allows visualization, heatmaps, score plots of the factorization or provides association statistics between NMF clusters and actual groups.
A Windows-program for the analysis of experiments with hierarchical probe-sets. PhylochipAnalyzer operates in two modes: first, the hierarchy of probes is defined interactively; second, the intensity data of a hybridized chip is loaded and analyzed according to the hierarchy. The program can export hierarchy trees to Newick-format and analyzed data to Excel. It contains a Delphi-script that makes it configurable with respect to different criteria for positive signals.
An open-source, platform-independent software pipeline for two-channel microarray spot quality control. MASQOT-GUI hosts a set of independent applications for gridding, segmentation, quantification, quality assessment and data visualization. All required steps, from raw data to final quantification and quality assessment, can be performed within MASQOT-GUI. Optionally, the user can utilize external software for gridding and subsequently import spot coordinates into MASQOT-GUI prior to segmentation.
Provides methods for computing different p-value based correction methods. Myriads incorporates a simulator for two sample t-tests and Cochran-Armitage case-control tests. The simulator can be used to obtain estimates of the variance of the number of false discoveries jointly with the per family error rate (PFER) committed by any of the correction methods. This software presents three main aspects: (i) correction methods, (ii) dependence test and (iii) the incorporation of an autocorrelation test based on the generalized Durbin-Watson statistic.
Uses as submission tool for Microarray experiments. AtMIAMExpress is an open-source Web-based software application for the submission of Arabidopsis-based microarray data to ArrayExpress. It was designed as a tool for users with minimal bioinformatics expertise, has comprehensive help and user support, and represents a simple solution to meeting the MIAME guidelines for the Arabidopsis community. It allows three types of submissions: protocols, array designs, and experiments.
MGFM / Marker Gene Finder in Microarray data
Predicts tissue/cell type marker genes using microarray gene expression data. The main advantages of MGFM are i) a short running time of some seconds per analysis. This is achieved by sorting the gene expression values instead of using gene differential expression. ii) MGFM offers the user the possibility to modify the set of samples by easily removing or adding new samples. iii) MGFM is available as a standalone version (R-package) as well as a web application integrated into the CellFinder platform.
SPEC / Subset Prediction from Enrichment Correlation
Supports blood genomics studies with general applicability to basic and translational research. SPEC predicts the cellular source of predictive gene expression signatures using transcriptional profiling data from total peripheral blood mononuclear cells (PBMCs). The software takes advantage of the fluctuations of cell proportions in a mixed cell sample to find the most likely source for a gene expression signal. It was applied to data from chronic Hepatitis C Virus (HCV) patients.
MaLTE / Machine Learning of Transcript Expression
An alternative approach to gene expression estimation that uses a machine learning approach. MaLTE relies on a gold standard (such as RNA-Seq) to infer gene expression models that make better use of microarray fluorescence probe intensities. This approach can be used to accurately estimate absolute expression levels from microarray data, at both gene and transcript level, which has not previously been possible. This methodology will facilitate re-analysis of archived microarray data and broaden the utility of the vast quantities of data still being generated.
A meta-analysis method able to efficiently identify associated biomarkers (ABs) from different independently performed array-based datasets. MiningABs quantifies the similarity between paired probe sequences as a bridge to connect these datasets together. The ABs can be subsequently identified from an “improved” common logit model (c-LM) by combining several sibling-like LMs in a heuristic genetic algorithm selection process. Investigating the ABs using Gene Ontology (GO) enrichment, literature survey, and network analyses indicated that ABs are not only strongly related to cancer development but also highly connected in a diverse network of biological interactions.
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Provides a platform for storage and investigation of microarray data. [email protected] is divided into five main features: (i) “storage”, that is able to handle both commercial and personal microarray experiments; (ii) “processing” includes features for filter and standardize data; (iii) the “analysis” panel allows users to perform a SAM analysis and to cluster data ; (iv) “data mining” furnishes module for annotation and (v) “main” gives access to tools for managing gene lists or gene expression matrix.
Provides an environment for analyzing both transcriptome data (gene expression profiles) and metabolome data (compound profiles). KegArray is a Java application that enables users to easily map those data to KEGG resources including PATHWAY, BRITE and genome maps. It can read data format of the EXPRESSION database or tab-delimitated text similar to the EXPRESSION format. It can convert the external database IDs to the KEGG GENES IDs, which are necessary for mapping the array data to the KEGG resources such as pathway maps.
Allows researchers to easily conduct a comprehensive and objective performance evaluation of their newly developed missing value imputation algorithm for microarray data or any data which can be represented as a matrix form (e.g. Next Generation Sequencing (NGS) data or proteomics data). MVIAeval provides a user-friendly interface allowing users to upload the R code of their new algorithm and select (i) the test datasets among 20 benchmark microarray (time series and non-time series) datasets, (ii) the compared algorithms among 12 existing algorithms, (iii) the performance indices from three existing ones, (iv) the comprehensive performance scores from two possible choices, and (v) the number of simulation runs.
FSPMA / Friendly Statistics Package for Microarray Analysis
Analyzes microarray experiments. FSPMA doesn’t require computer programming because the entire process is controlled by a definition file that defines all steps to produce analysis results from microarray and is based on a mixed model ANOVA library. This software permits users to make normalization based on RNAs of known concentration where the spiked into the RNA samples where the spike residual log ration is utilized to normalize the data. By providing pre-defined definition files, it allows to perform analysis of balanced reference designs.
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