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REleased tool that is continuing to actively development new features

CooccurViewer

The project seeks to expose correlation of observations made between any pair of events in a data sequence. In this project, we present two methods for identifying interesting co-occurrences (see the manuscript for a detailed discussion).

A demo is availble of these two approaches through the project website.

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Institution: Department of Computer Sciences at the University of Wisconsin—Madison

Sequence Bundles

Sequence Bundles is a new tool for visualising and exploring sequence motifs in multiple sequence alignment (MSA) data.

It enables the discovery of data features that would otherwise remain hidden.

In Sequence Bundles, MSA sequences are visualised as continuous strings of data, which helps in preserving and exposing important residue correlations.

Use Sequence Bundles web tool to easily look at your own MSA data in a completely new way.

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Institution: Science Practice

Sequence Bundles were designed by Science Practice in collaboration with Goldman group at the EMBL-EBI. For more details visit Sequence Bundles project homepage and read our article: BMC Proceedings 2014, 8(Suppl 2):S8

Vaa3D

Vaa3D(in Chinese ‘挖三维’) is an Open Source visualization and analysis software suite created mainly by Hanchuan Peng and his team at Janelia Research Campus, HHMI and Allen Institute for Brain Science. The software performs 3D, 4D and 5D rendering and analysis of very large image data sets, especially those generated using various modern microscopy methods, and associated 3D surface objects. This software has been used in several large neuroscience initiatives and a number of applications in other domains. It has been viewed as one of the leading Open Source software suites in the related research fields. It has also been used in several other award-winning work, e.g. mapping of dragonfly neurons and large-scale visualization of cellular data.

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Institution: Janelia Research Campus, HHMI; Allen Institute for Brain Science

GeneProf

GeneProf is a web-based, graphical software suite that allows users to analyse data produced using high-throughput sequencing platforms (RNA-seq and ChIP-seq; "Next-Generation Sequencing" or NGS): Next-gen analysis for next-gen data!GeneProf makes extensive use of visualisation to explore ChIP-seq and RNA-seq data in the context of the genome as well as for data quality control, visualisation of results from individual experiments and for meta-analysis across experiments. Some of GeneProf's highlights include:                                
  • Easy-to-use web-based interface:Access your data at any time from any reasonably modern computer with a working internet connection
  • Analysis wizards make your life easy:Pre-defined, step-by-step workflows make it possible for anybody to analyse their short read data in a minimum of hands-on time).
  • Versatile modules:Advanced users and data analysis experts benefit from GeneProf's broad range of analysis modules, which can be combined freely a minimum of hands-on time
  • Integrated Analysis:Analysis of ChIP-seq and RNA-seq data in one place, plus support for the integration of other external data (e.g. from microarrays).
  • Comprehensive Resource:GeneProf provides a comprehensive resource of analyzed next-generation sequencing data. Experimental results can be easily accessed and compared and the analysis procedures employed to produce the data are fully transparent and can be accessed readily
  • Extensibility:Algorithm developers and computer programmers can develop their own modules and extend the functionality of GeneProf. Existing software can be easily wrapped and integrated in the GeneProf framework  and data from GeneProf may be used externally
                  
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Institution: Centre for Regenerative Medicine, University of Edinburgh

Zegami

Zegami is a data visualisation tool that allows you to view large collections of images and associated metadata. Zegami has been built based on the latest web standards, for the latest web browsers. Its responsive design means that it works equally well on the desktop as it does on mobile devices.

Zegami was born out of the need to be able to visualise large collections of images produced by high throughput microscopy in within the area of genetic research. However Zegami has many uses outside of just microscopy, some of which include:

  • X-ray and MRI scans
  • Phenomics
  • Security
  • Asset management
  • Document management
  • Shopping
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Institution: Oxford University

OMERO

From the microscope to publication, OMERO handles all your images in a secure central repository. You can view, organize, analyze and share your data from anywhere you have internet access. Work with your images from a desktop app (Windows, Mac or Linux), from the web or from 3rd party software. Over 130 image file formats supported, including all major microscope formats.

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Institution: University of Dundee

http://www.openmicroscopy.org/site/about/development-teams/jason

Aequatus

We present the Aequatus, a web-based tool with novel rendering approaches to visualise homologous, orthologous and paralogous gene structures among differing species or subtypes of a common species. The Aequatus utilises web technologies to provide a fast and intuitive browsing experience over complex comparative data. The Aequatus processes and visualises data directly from the Ensembl Compara and Ensembl Core schema databases by using precalculated genomic alignments from Ensembl Compara and relating them to Ensembl Core to gather genomic feature information, visualising phylogenetic and structural relationships among them via CIGAR strings. Whilst applicable to species with high-quality gold-standard reference genomes such as human or mouse, the Aequatus was designed with large fragmented genome references in mind,  e.g. polyploid plants. The ultimate goal of the Aequatus is to provide a unique and informative way to render and explore complex relationships between genes from various species.

Source Code: https://github.com/TGAC/Aequatus

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Institution: Earlham Institute

TGAC Browser

We present the TGAC Browser with novel rendering, annotation and analysis capabilities designed to overcome the shortcomings in available approaches. TGAC Browser, being a web-based client, utilises JavaScript libraries to provide a fast and intuitive genome browsing experience. We focus on harnessing Internet architectures as well as localised HPC hardware, concentrating on improved, more productive interfaces and analytical capabilities.

  • User-friendly: Live data searching, track modification, and drag and drop selection; actions that are seamlessly powered by modern web browser
  • Responsiveness: Client-side rendering and caching, based on JSON fragments generated by server logic, helps decrease the server load and improves user experience
    • TGAC Browser visualises genomic data in different ways, based on the type and amount of data, which is more informative to the user and memory efficien
  • Analysis Integration: The ability to carry out heavyweight analysis tasks, using tools such as BLAST, via a dedicated extensible daemon
  • Annotation: Users can edit annotations which can be persisted on the server, reloaded, and shared at a later date
  • Off-the-shelf Installation: The only prerequisites are a web application container, such as Jetty or Tomcat, and a standard Ensembl database to host sequence features
  • Extensible: Adaptable modular design to enable interfacing with other databases, e.g. GMOD
  • Data format: TGAC Browser processes and visualises data directly from the Ensembl core schema as well as next-generation sequencing (NGS) data output, i.e. BAM/SAM, BigWig/wig, GFF, and VCF.
  • Manual Annotation: Live editing of Genomics annotations within TGAC Browser, saved with versions and can be reverted, exported in Genomics data format for curator to edit datasets (Under development).

Email: Anil.Thanki@earlham.ac.uk

Source Code: https://github.com/tgac/tgacbrowser

 

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Institution: Earlham Institute

Ensembl Genome Browser

The Ensembl Genome Browser brings together gene models, comparative genomics, variation and regulatory elements in a single website across the chordate species space.

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BioFabric

BioFabric uses a novel network visualization technique that depicts nodes as one-dimensional horizontal lines arranged in unique rows. This is in distinct contrast to the traditional approach that represents nodes as discrete symbols that behave essentially as zero-dimensional points. BioFabric then depicts each edge in the network using a vertical line assigned to its own unique column, which spans between the source and target rows, i.e. nodes. This method of displaying the network allows a full-scale view to be organized in a rational fashion; interesting network structures, such as sets of nodes with similar connectivity, can be quickly scanned and visually identified in the full network view, even in networks with well over 100,000 edges. This approach means that the network is being represented as a fundamentally linear, sequential entity, where the horizontal scroll bar provides the basic navigation tool for browsing the entire network.

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Institution: Institute for Systems Biology

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