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Unifies a compendium of published cancer microarray data with a suite of advanced analytic tools, facilitating biologist-friendly data mining. Oncomine consists of three general layers: data input, data analysis, and data visualization, with the database playing a central role in storing microarray data, sample data, and analysis results. The software offers peer-reviewed analysis methods and a set of analysis functions that compute gene expression signatures, clusters and gene-set modules.
Allows users to compare two or more groups of Samples in a GEO Series in order to identify genes that are differentially expressed across experimental conditions. GEO2R performs comparisons on original submitter-supplied processed data tables using the GEOquery and limma R packages from the Bioconductor project. This tool provides a simple interface that allows users to perform R statistical analysis without command line expertise. Results are presented as a table of genes ordered by significance.
MERGE / mutation, expression hubs, known regulators, genomic CNV, and methylation
Recognizes reliable gene expression markers. MERGE employs a principled way of integrating multi-omic prior information relevant to disease processes. It integrates prior information on genes’ relevance in order to prioritize gene-drug associations. This method increases the chance that the identified gene-drug associations are replicated in validation data. It gives the potential to make novel discoveries about molecular markers.
LCB / LINCS Canvas Browser
An interactive HTML5 web-based software application that facilitates querying, browsing and interrogating many of the currently available LINCS L1000 data. LCB implements two compacted layered canvases, one to visualize clustered L1000 expression data, and the other to display enrichment analysis results using 30 different gene set libraries. Clicking on an experimental condition highlights gene-sets enriched for the differentially expressed genes from the selected experiment. A search interface allows users to input gene lists and query them against over 100,000 conditions to find the top matching experiments. The tool integrates many resources for an unprecedented potential for new discoveries in systems biology and systems pharmacology.
MERAV / Metabolic gEne RApid Visualizer
A web-based tool that can query a database comprising ∼4300 microarrays, representing human gene expression in normal tissues, cancer cell lines and primary tumors. MERAV has been designed as a powerful tool for whole genome analysis which offers multiple advantages: one can search many genes in parallel; compare gene expression among different tissue types as well as between normal and cancer cells; download raw data; and generate heatmaps; and finally, use its internal statistical tool. Most importantly, MERAV has been designed as a unique tool for analyzing metabolic processes as it includes matrixes specifically focused on metabolic genes and is linked to the Kyoto Encyclopedia of Genes and Genomes pathway search.
LungGENS / Lung Gene Expression iN Single-cell
Facilitates the retrieval of lung cell-specific gene expression information from extensive data sets derived from RNA sequencing of single cells. LungGENS is a web-based bioinformatics resource for querying single-cell gene expression databases by entering a gene symbol or a list of genes or selecting a cell type of their interest. It also integrates the data with previous RNA expression studies from mouse lung at various developmental times.
A package that allows access to the wealth of information within GEO directly from BioConductor, eliminating many formatting and parsing problems that have made such analyses labor-intensive in the past. The primary goal of GEOquery is to download and parse the SOFT format files from GEO, maintaining all of the information contained in the GEO records. The design of GEOquery makes accessing data from GEO very simple. There is only one command that is needed, getGEO. GEOquery provides a bridge between the BioConductor analysis tools and the vast public data resources contained in the NCBI GEO repositories. By maintaining the full richness of the GEO data rather than focusing on getting only the ‘numbers’, it is possible to integrate GEO data into Bioconductor data structures and to perform analyses on that data quite quickly and easily or to export the data into any number of formats for use by other tools or for local storage and data mining.
Provides an alternative, yet much more flexible and efficient, set of tools for both online and programmatic access to Gene Expression Omnibus (GEO) metadata. GEOmetadb was developed in an attempt to make querying the GEO metadata both easier and more effective. It includes a web-based query engine with several convenient utilities and a Bioconductor package, also called GEOmetadb, which queries a locally installed GEOmetadb SQLite database update regularly and supply for download; each can be used independently of the other.
A deep learning method to infer the expression of target genes from the expression of landmark genes. We used the microarray-based GEO dataset, consisting of 111K expression profiles, to train our model and compare its performance to those from other methods. In terms of mean absolute error averaged across all genes, deep learning significantly outperforms linear regression with 15.33% relative improvement. A gene-wise comparative analysis shows that deep learning achieves lower error than linear regression in 99.97% of the target genes. We also tested the performance of our learned model on an independent RNA-Seq-based GTEx dataset, which consists of 2,921 expression profiles. Deep learning still outperforms linear regression with 6.57% relative improvement, and achieves lower error in 81.31% of the target genes.
Provides an environment for downloading functional genomics data from Gene Expression Omnibus (GEO), parsing the information into a local or remote database, and interacting with the database using dedicated R functions, thus enabling seamless integration with other tools available in R/Bioconductor. The compendiumdb package consists of a number of R functions to access this database either locally or remotely. The database schema has been designed to be rich enough to store information provided by MIAME-compliant expression databases such as GEO. The package provides R functions to (i) download data from GEO given the identifier of the experiment, (ii) load the expression data, sample and probe annotation to the relational database, and (iii) convert experimental data from the database to an R/Bioconductor ExpressionSet.
Maps spatiotemporal brain data to vector graphic diagrams of the human brain. cerebroViz allows rapid generation of publication-quality figures that highlight spatiotemporal trends in the input data, while striking a balance between usability and customization. cerebroViz is generalizable to any data quantifiable at a brain-regional resolution and currently supports visualization of up to thirty regions of the brain found in databases such as BrainSpan, GTEx and Roadmap Epigenomics.
DGET / Drosophila Gene Expression Tool
Stores and facilitates search of RNA-Seq based expression profiles available from the modENCODE consortium and other public data sets. DGET provides a flexible tool for expression data retrieval and analysis with short or long lists of Drosophila genes, which can help scientists to design stage- or tissue-specific in vivo studies and do other subsequent analyses. Using DGET, researchers are able to look up gene expression profiles, filter results based on threshold expression values, and compare expression data across different developmental stages, tissues and treatments.
A fast search engine that uses a query genomic profile to search for similar profiles in a very large genomic database. GEMINI enables users to identify similar profiles independent of sample label, data origin or other meta-data information. GEMINI implements a nearest-neighbor search algorithm using a vantage-point tree to store a database of n profiles and in certain circumstances achieves an O(log n) expected query time in the limit. We tested GEMINI on breast and ovarian cancer gene expression data from The Cancer Genome Atlas project and show that it achieves a query time that scales as the logarithm of the number of records in practice on genomic data. In a database with 10(5) samples, GEMINI identifies the nearest neighbor in 0.05 sec compared to a brute force search time of 0.6 sec.
TED toolkit / Transcriptomics profiler for Easy Discovery toolkit
Allows users to process and analyze RNA-seq data. Serves for transcriptomic profiling as a clinically-oriented application. TED toolkit is an application that is divided in several modules: (1) the first module provides quality control of the RNAseq data which are preprocessing steps; (2) the second module carries out analysis of differentially coding, non-coding and novel isoform gene expression; and (3) the third module transforms the analysis results produced from the second module into detailed, biologically interpreted annotated reports.
Permits to annotate many perturbation experiments from NCBI Gene Expression Omnibus (GEO) in a semi-automated fashion with full user control. GEOracle follows the same steps a bioinformatcian would employ when analysing perturbation data on GEO. After annotation, it performs differential expression analysis to identify gene targets of the perturbation agent. An interface guides user through the entire process and allows the user to manually adjust and verify all details of the predicted GSM labels and pairings.
C3 / Cross-species Compendium-based Cell-type identification
Identifies cross-species cell-type. C3 is based on a cross-species gene set analysis method that relieves the false positive bias while still maintain good statistical power for gene sets affected by highly complex homology structures. It is able to prioritize identification of the correct corresponding cell type as the most significant hit. This tool can be used to find an unknown cell type from a potentially poorly characterized organism.
A web application that allows a user to download gene expression data sets directly from GEO in order to perform differential expression and survival analysis for a gene of interest. In addition, shinyGEO supports customized graphics, sample selection, data export, and R code generation so that all analyses are reproducible. The availability of shinyGEO makes GEO datasets more accessible to non-bioinformaticians, promising to lead to better understanding of biological processes and genetic diseases such as cancer.
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A web server that allows for querying the Gene Expression Omnibus based on genome-wide patterns of differential expression. Using a novel, content-based approach, ProfileChaser retrieves expression profiles that match the differentially regulated transcriptional programs in a user-supplied experiment. This analysis identifies statistical links to similar expression experiments from the vast array of publicly available data on diseases, drugs, phenotypes and other experimental conditions.
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Enables simultaneous comparisons between a wide range of data by combining major resources from human and vertebrate model organisms. Manteia performs several types of analyses as well as data retrieval, gene or probe set annotation, information content analysis or candidate gene prediction and prioritization. It aims to help in investigating the genetic origin of human diseases or identifying significant correlations in lists of genes and proteins generated by modern high-throughput techniques.
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