Transcriptomics

You are here

SATORI

SATORI is an ontology-guided visual exploration system for data repositories, which combines powerful metadata search with a treemap and a node-link diagram that visualize the repository structure, provide context to retrieved data sets, and serve as an interface to drive semantic querying and exploration, and thereby support the information foraging loop. SATORI is  web-based, open-source, and integrated  in  the Refinery-Platform—an application for biomedical data management, analysis, and visualization.

Release Date:
Status:
Availability:
Data type:
Techniques:
Software:
Technology:
Platform:
Requirements:

Project development

Institution: Harvard Medical School

Why?  Biomedical repositories are growing rapidly and provide scientists with tremendous opportunities to re-use data. In order to exploit published data sets efficiently, it is crucial to understand the content of repositories and to discover data relevant to a question of interest. These are challenging tasks, as most repositories currently only support finding data sets through  text-based search of metadata and in some cases also through metadata-based browsing. To address this, we conducted a task analysis through semi-structured interviews with 8 PhD-level domain experts and identified 3 distinct user roles.

What?  Biological data sets consists of experimental data and metadata describing the studies, properties of the analyzed biological samples, and attributes of individual data files. In this context, a data set is a collection of data files, along with the metadata. Additionally, metadata is partially annotated with ontology terms. An ontology describes a certain domain (e.g. human anatomy), defines controlled vocabularies for its concepts and relationships (e.g., kidney and is-part-of) and relates concepts with each other (e.g., nephron is-part-of kidney). By means of ontology terms, sets of annotated data sets can be classified hierarchically. SATORI extracts free-text and ontologically annotated metadata. The free-text metadata is indexed in a text-based search system. Additionally, data set-related ontology classes are parsed and visualized to provide  semantic context to data sets. Since SATORI's goal is to support exploration rather than to visualize ontologies themselves, only a relevant subtree of the ontologies is shown, i.e., effectively enforcing a strict containment hierarchy.

How?  SATORI is composed of two main interlinked views: the data set view and the exploration view. In the treemap an ontology term is illustrated by a rectangle. The area of the rectangle visualizes the size of the term relative to its sibling terms and the color indicates the distance to the farthest child term. The farther away this child term is, the darker is the color. The node-link diagram represents ontology terms as nodes and links shown parent and child terms. Additionally, the diagram visualizes the precision and recall for each term given the currently retrieved data sets. In this context, precision is useful to understand how frequently a term is used for annotation in the retrieved set of data sets and recall provides a notion of information scent by indicating if there are more data sets annotated with this term. Finally, the exploration view acts as a semantic query interface and lets users filter down collections of data sets via ontology term-based Boolean queries.

MLCut

Choosing a single similarity threshold for cutting dendrograms is not sufficient for performing hierarchical clustering analysis of heterogeneous data sets. In addition, alternative automated or semi-automated methods that cut dendrograms in multiple levels make assumptions about the data in hand. In an attempt to help the user to find patterns in the data and resolve ambiguities in cluster assignments, we developed MLCut: a tool that provides visual support for exploring dendrograms of heterogeneous data sets in different levels of detail. The interactive exploration of the dendrogram is coordinated with a representation of the original data, shown as parallel coordinates. The tool supports three analysis steps. Firstly, a single-height similarity threshold can be applied using a dynamic slider to identify the main clusters. Secondly, a distinctiveness threshold can be applied using a second dynamic slider to identify ''weak-edges'' that indicate heterogeneity within clusters. Thirdly, the user can drill-down to further explore the dendrogram structure - always in relation to the original data - and cut the branches of the tree at multiple levels. Interactive drill-down is supported using mouse events such as hovering, pointing and clicking on elements of the dendrogram. Two prototypes of this tool have been developed in collaboration with a group of biologists for analysing their own data sets. We found that enabling the users to cut the tree at multiple levels, while viewing the effect in the original data, is a promising method for clustering which could lead to scientific discoveries.

Release Date:
Status:
Availability:
Data type:
Techniques:
Software:
Technology:
Platform:
Requirements:

Project development

Institution: Edinburgh Napier University

Listeriomics

As for many model organisms, the amount of Listeria omics data produced has recently increased exponentially. There are now >80 published complete Listeria genomes, around 350 different transcriptomic data sets, and 25 proteomic data sets available. The analysis of these data sets through a systems biology approach and the generation of tools for biologists to browse these various data are a challenge for bioinformaticians. We have developed a web-based platform, named Listeriomics, that integrates different tools for omics data analyses, i.e., (i) an interactive genome viewer to display gene expression arrays, tiling arrays, and sequencing data sets along with proteomics and genomics data sets; (ii) an expression and protein atlas that connects every gene, small RNA, antisense RNA, or protein with the most relevant omics data; (iii) a specific tool for exploring protein conservation through the Listeria phylogenomic tree; and (iv) a coexpression network tool for the discovery of potential new regulations. Our platform integrates all the complete Listeria species genomes, transcriptomes, and proteomes published to date. This website allows navigation among all these data sets with enriched metadata in a user-friendly format and can be used as a central database for systems biology analysis.

Release Date:
Status:
Availability:
Data type:
Techniques:
Software:
Technology:
Platform:
Requirements:

Project development

Institution: Institut Pasteur

Developed in Java using Eclipse RCP/RAP API

 

expVIP

expVIP is an expression visualization and integration platform, which allows easy analysis of RNA-seq data combined with an intuitive and interactive interface. Users can analyze public and user-specified data sets with minimal bioinformatics knowledge using the expVIP virtual machine. This generates a custom Web browser to visualize, sort, and filter the RNA-seq data and provides outputs for differential gene expression analysis.

Release Date:
Status:
Availability:
Data type:
Techniques:
Software:
Technology:
Platform:
Requirements:

Project development

Institution: John Innes Centre, Norwich; Genome Analysis Centre, Norwich

BioJS

BioJS is a library of over hundred JavaScript components enabling you to visualize and process data using current web technologies.

Release Date:
Status:
Availability:
Data type:
Techniques:
Software:
Technology:
Platform:
Requirements:

Project development

Institution: BioJS

Integrated Genome Browser

The Integrated Genome Browser (IGB, pronounced Ig-Bee) is a fast, flexible, and free desktop genome browser. First developed at Affymetrix in 2001 to support visual analytics of genome tiling arrays, IGB provides an advanced, highly customizable environment for exploring and analyzing large-scale genomic data sets.

Using IGB, you can:

  • View your RNA-Seq, ChIP-chip or ChIP-seq data alongside genome annotations and sequence.
  • Investigate alternative splicing, regulation of gene expression, epigenetic modifications of DNA, and other genome-scale questions.
  • View results from aligning short-read sequences onto a target genome, identify SNPs, and check alignment quality.
  • Copy and paste genomic sequences for further analysis into other tools, such as primer design and promoter analysis tools.
  • Create high-quality images for publication in a variety of formats.

 

IGB features

IGB lets you view results from your own experiments or computational analyses alongside public domain gene annotations, sequences, and genomic data sets, thus making it easier for you to determine how your experiments agree or disagree with current thinking and models of genomic structure.

Some features IGB offers include:

  • Animated zooming. Most genome browsers implement "jump zooming" only, in which you click a zoom button (or other type of control) and then wait for the display to re-draw. In IGB, zooming is animated, allowing you to easily and quickly adjust the zoom level as needed without losing track of your location.
  • Simple Data Sharing System - QuickLoad. IGB implements a very simple, easy-to-use system for sharing data called QuickLoad. You can use the QuickLoad system to set up a Web site you can use to share your data with colleagues, reviewers, and the public.
  • Draggable graphs. You can display genome graphs data (e.g., "bar" and "wiggle" files) alongside and even on top of reference genome annotations, thus making it easier to see how your experimental results match up to the published reference genome annotations. You can reset your graphs to "floating" and click-drag them over annotations to compare your results with annotations and others' experiments.
  • Edge-matching across tracks. When you click an item in the display, the edges of other items in the same or different tracks with identical boundaries light up, highlighting interesting similarities or differences across gene models, sequence reads, or other features.
  • Integration with local and remote external data sources. IGB can load data from a variety of sources, including Distributed Annotation Servers, QuickLoad servers, ordinary Web sites, and local files.
  • Intron-trimming sliced view. In many species, introns are huge when compared to the exonic (coding) regions of genes. IGB provides a Sliced View tab that trims uninformative regions from introns.
  • Web-controls. IGB can be controlled from a web browser or any other program capable of sending HTTP requests. Via IGB links, you can create Web pages that direct IGB to scroll to a specific region and load data sets from local files or servers.
  • Scripting. IGB understands a simple command language that allows users to write simple scripts directing IGB to show a genome, zoom and scroll to specific regions, and other functions.
  • Open source. All development on IGB proceeds via a 100% open source model. The license allows developers to incorporate IGB (and its components) into new applications.
Release Date:
Status:
Availability:
Data type:
Techniques:
Software:
Technology:
Platform:
Requirements:

Project development

Institution: UNC @ Charlotte

Sunburst view of in situ gene-expression data from EMAGE

The eMouse Atlas of Gene Expression (EMAGE) is an online resource that publishes the results of in situ gene expression experiments on the developmental mouse. The resource provides comprehensive search facilities, but few analytical tools or visual mechanisms for navigating the data set.

Release Date:
Status:
Availability:
Data type:
Techniques:
Software:
Technology:
Platform:
Requirements:

Project development

Institution: Heriot-Watt University

TIALA

The time series alignment analysis tool Tiala allows one to align multiple time series experiments and to visually explore the aligned expression profiles. A two- and three-dimensional visualization strategy was implemented that is especially designed to enhance the display of multiple aligned time series expression profiles.

Tiala is available as a part of the microarray data analysis software Mayday. Mayday itself is open source software distributed under the terms of the GNU General Public License. It is available from http://it.inf.uni-tuebingen.de/?page_id=248.

Release Date:
Status:
Availability:
Data type:
Techniques:
Software:
Technology:
Platform:
Requirements:

Project development

Institution: Center for Bioinformatics, Tubingen University of Tubingen

Part of this work has been funded by the DFG Priority Program 1335 “Scalable Visual Analytics”.