Transcriptome viewer tools | RNA sequencing data analysis
In the end, most de novo RNA-seq studies result in a multitude of different datasets, including sequences, their annotation, expression levels and DE as well as co-expression testing results. Since most of the datasets contain thousands of entries they remain hard to handle. The vast amount of different data types necessitates the usage of a simple interface, optimally through a web browser, to allow uniform data access also for non-IT personal.
A tool for visualizing RNA-seq analysis results. CummeRbund takes the various output files from a cuffdiff run and creates a SQLite database of the results describing appropriate relationships betweeen genes, transcripts, transcription start sites, and CDS regions. Once stored and indexed, data for these features, even across multiple samples or conditions, can be retrieved very efficiently and allows the user to explore subfeatures of individual genes, or genesets as the analysis requires.
Allows exploration of Arabidopsis microarray data to permit intuitive visualization of gene expression data across approximately 22,000 genes from Arabidopsis thaliana. Arabidopsis eFP Browser allows personalization of the Data Source from a list of predefined choices. It offers an overview of gene response for some experiments as well as an improved understanding of the experimental set-up for the data set selected. This tool is a part of the eFP Browser.
Allows exploration of Arabidopsis thaliana through multiple levels. ePlant permits researchers to extract information from their data. It can identify connections between layers and facilitate hypothesis generation. This tool promotes a deeper understanding of biological processes and functioning. It offers a web interface containing a gene panel, some navigation icons and the module viewer panel.
Provides a workflow for RNA-Seq-based differential gene expression (DGE) analysis. RobiNA gathers multiple packages and software with the aim of furnishing a cross-platform processing in four main steps: (i) quality assessment and filtering; (ii) mapping the reads to a user-provided reference genome or transcriptome; (iii) perform the experimental design and (iv) statistical analysis of DGE.
Identifies genes showing similar expression or response profiles from selected databases. Expression Angler employs the Pearson correlation coefficient to identify co-regulated genes. It provides powerful means to query the data. This tool can recognize marker genes for genotoxic stress and different kinds of pathogen response. It uses the pattern to search for genes with similar expression profiles.
Visualizes transcript structure for model and non model organisms. SuperTranscript acts as a reference for transcriptome data, where each gene is represented by a single sequence that contains the union of all the exons in their transcriptional order, built from any combination of transcripts, including reference assemblies, de novo assemblies and long-read sequencing. SuperTranscript constructs comprehensive transcriptome sequences in an automated way from any source and can reveal unique insights into the complexities of transcriptomes and identify novel expressed sequence.
Allows users to store, visualize and analyze epigenomics and transcriptomics data using a biologist-friendly web interface, without the need for programming expertise. Predefined pipelines allow users to download data, visualize results on a genome browser, calculate RPKMs (reads per kilobase per million) and identify peaks. Advanced capabilities include differential gene expression and binding analysis, and creation of average tag -density profiles and heatmaps.