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Semantic Body Browser

The Semantic Body Browser is a web application for intuitively exploring the body of an organism from the organ to the subcellular level and visualising expression profiles by means of semantically annotated anatomical illustrations. It is used to comprehend biological and medical data related to the different body structures while relying on the strong pattern recognition capabilities of human users.

Main web applicationsbb.cellfinder.org

Project websitesemantic-body-browser.lekschas.de

Source codegithub.com/flekschas/sbb and github.com/flekschas/sbi

PublicationLekschas et al. (2015) Semantic Body Browser: Graphical exploration of an organism and spatially resolved expression data visualisation. Bioinformatics, Volume 31, Issue 5, 1 March 2015, Pages 794–796.

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Institution: Charité University Hospital Berlin

The Semantic Body Browser is implemented in JavaScript and SVG and backed by the CellFinder database. The source code is available on GitHub. The anatomical images are released under a permissive license on a separate GitHub repository as well.

Peax

Peax is a tool for interactive visual pattern search in epigenomic data that is based on unsupervised deep representation learning for similarity search. Visually searching for epigenomic patterns by similarity is challenging when the large search space, the visual patterns are complex, or the search target is not well defined. To overcome these challenges we have developed a convolutional autoencoder model for unsupervised representation learning of regions in epigenomic data that can capture more visual details of complex patterns compared to existing similarity measures. Using this learned representation as features of regions of epigenomic data, Peax enables interactive relevance feedback-driven adjustments of the pattern search to adapt to the users' perceived similarity. The binary relevance feedback, which is provided by labeling sampled regions as either mathing the search target or not matching the search target, is used to interactively train a binary classifier. The goal of this classifier is to learn the importance of the different dimensions of the region's learned representation and ultimately find regions in the genome that are perceived similar by the analyst. We employ an active learning strategy to focus the labeling process on regions that will improve the classifier in subsequent training.

Project websitepeax.lekschas.de

Introductory video: youtu.be/FlzTdFUVE-M

Source codegithub.com/novartis/peax (Released under Apache 2.0)

PublicationLekschas et al. (2020) Peax: Interactive Visual Pattern Search in Sequential Data Using Unsupervised Deep Representation Learning. Computer Graphics Forum (EuroVis).

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Institution: Harvard University

We implemented Peax as local web-based tool running only on your machine. Peax itself is written in JavaScript and Python and uses HiGlass, a flexible web application for viewing large tile-based genomic datasets, for visualizing epigenomic data. Peax currently works with DNase-seq and histone mod. ChIP-seq data. The source code is available on GitHub (released under Apache 2.0) and includes several examples.

HiGlass

HiGlass is a web-based tool for visually exploring and comparing 2D genomic contact matrices, 1D genomic tracks, or other datasets too large to view at once. It features synchronized navigation of multiple views as well as continuous zooming and panning for navigation across genomic loci and resolutions. It supports visual comparison of genomic (e.g., Hi-C, ChIP-seq, or bed annotations) and other data (e.g., geographic maps, gigapixel images, or abstract 1D and 2D sequential data) from different experimental conditions and can be used to efficiently identify salient outcomes of experimental perturbations, generate new hypotheses, and share the results with the community.

Project website: higlass.io

Source code: github.com/higlass/higlass

PublicationKerpedjiev et al. (2018) HiGlass: Web-based visual comparison and exploration of genome interaction maps. Genome Biology, 19:125.

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Institution: Harvard Medical School

HiGlass is a fast visualization tool for large Hi-C and other genomic data sets. It was created by Peter Kerpedjiev at the Gehlenborg Lab at Harvard Medical School in close collaboration with the Visual Computing Group at Harvard John A. Paulson School of Engineering and Applied Sciences, and Mirny Lab at Massachusetts Institute of Technology as part of the 4D Nucleome Project's Data Coordination and Integration Center.

Scalable Insets

Scalable Insets is a new technique for interactively exploring and navigating large numbers of annotated patterns in multiscale visual spaces such as genome interaction maps from Hi-C experiments. Our technique visualizes annotated features, such as loops or TADs, too small to be identifiable at certain zoom levels using insets, i.e., magnified thumbnail views of the features. Insets are dynamically placed either within the viewport or along the boundary of the viewport to offer a compromise between locality and context preservation. Annotated features are interactively clustered by location and type. They are visually represented as an aggregated inset to provide scalable exploration within a single viewport. Finds out more in the project page and our 5-mins introductory video.

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Institution: Harvard University

We implemented Scalable Insets as an extension to HiGlass, a flexible web application for viewing large tile-based genomic datasets. Besides genome interaction maps, our implementation currently supports gigapixel images and geographic maps too. The tool can easily be applied to existing BEDPE annotation files. The source code is available on GitHub

karyoploteR

karyoploteR is an R/Bioconductor package to plot genomic data along the genome. It implements a genomic coordinates version of most R graphical primitives facilitating the creation of rich and powerful genome visualizations. Since karyoploteR does not try to "understand" the data it is plotting, it can plot almost anything, any data type,  as long as it is positioned on the genome. In addition, while the package includes data for some of the most used genomes, it can automatically download genome information from external sources and accepts custom genomes directly from the user, thus making it possible to "plot anything on any genome". karyoploteR covers the whole zoom range, going from single base to whole genome changing a single parameter in a function call.  There are additional higher level functions to plot specific types of data, for example one to compute and plot the density of features along the genome, another to plot the coverage level directly from a BAM file or a third one to plot links between genomic regions. 

To know more about the functionality of karyoploteR you can check the package vignette or head to the karyoploteR tutorial page, were you will find a step-by-step tutorial on how to use the package as well as some more involved examples with detailed explanations including how to use karyoploteR to plot different standard data types: RNA-seq differential expression results, SNP-array data, somatic mutation distance using rainfall plots.

 

Bioconductor landing page: http://bioconductor.org/packages/karyoploteR/

Tuorial and Examples: https://bernatgel.github.io/karyoploter_tutorial/

Source code at github: https://github.com/bernatgel/karyoploteR

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Institution: Germans Trias i Pujol Research Institute, IGTP

BioBlox

Goldsmiths and Imperial announce the launch of BioBlox2D and BioBlox3D molecular docking games.
 
BioBlox2D is free to download from the App store and Google Play.
 
BioBlox2D turns the science of how proteins fit together with smaller molecules, such as medicines and vitamins, into a Tetris-style puzzle game and quiz.

BioBlox3D is a free and fully accessible web game and is playable on the website aimed at the specialist user enabling them to dock 3D proteins.

Best Regards,
Mike, Frederic and William.

Project Leads
Prof Mike Sternberg. Imperial College.
Prof Frederic Leymarie. Goldsmiths.
Prof William Latham. Goldsmiths.
Email: info@bioblox.org

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Institution: Goldsmiths, University of London and Imperial College London

Gamification of the scientific problem of how biological molecules dock.

 

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.

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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.

HiPiler

HiPiler an interactive visualization interface for the exploration and visualization of regions-of-interest in large genome interaction matrices. Genome interaction matrices approximate the physical distance of pairs of genomic regions to each other and can contain up to 3 million rows and columns with many sparse regions. Traditional matrix aggregation or pan-and-zoom interfaces largely fail in supporting search, inspection, and comparison of local regions-of-interest (ROIs). ROIs can be defined, e.g., by sets of adjacent rows and columns, or by specific visual patterns in the matrix. ROIs are first-class objects in HiPiler, which represents them as thumbnail-like “snippets”. Snippets can be laid out automatically based on their data and meta attributes. They are linked back to the matrix and can be explored interactively. The design of HiPiler is based on a series of semi-structured interviews with 10 domain experts involved in the analysis and interpretation of genome interaction matrices. In the paper we describe six exploration tasks that are crucial for analysis of interaction matrices and demonstrate how HiPiler supports these tasks. We report on a user study with a series of data exploration sessions with domain experts to assess the usability of HiPiler as well as to demonstrate respective findings in the data.

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Institution: Harvard University

HiPiler is implemented as a web application consisting of a frontend interface for the visualizations and a server-side component that provides the data. The frontend is entirely written in JavaScript utilizing Aurelia as its application framework and Redux for fine-grained, history-aware state management. The matrix snippets are visualized with WebGL using Three.js as a middleware. Finally, HiGlass is integrated as a library for displaying the interaction matrix and genomic tracks. The server-side backend serves data to HiGlass and provides the matrix snippets. The backend is implemented in Python and uses Django as its application framework. The contact matrices are accessed through Cooler, a Python-based service library for storing and querying of Hi-C data. The front and backend are two separate applications that can be decoupled to load different data types. HiPiler is open source and available on GitHub.

MARender

MARender is a JavaScript 3D rendering system based on three.js (http://threejs.org/).

The rendering system is centred around a JavaScript class MARenderer and aimed at simple web-based visualisation of 3D bio-medical datasets, with particular emphasis on anatomy and mapped spatial data (eg gene expression).

Typical uses combine surface, section and point cloud renderings. Surfaces and point clouds are most readily read from VTK format files using the modified VTK loader https://github.com/ma-tech/three.js/blob/master/examples/js/loaders/MAVTKLoader.js and sections either from static images or from an IIP3D server (https://github.com/ma-tech/WlzIIPSrv).

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Institution: MRC Institute of Genetics & Molecular Medicine, The University of Edinburgh

HaptiMOL

The HaptiMOL suite enables interaction with protein structures using force feedback, through the use of a haptic feedback device:

  • HaptiMOL ISAS enables users to interact with the solvent accessible surface of biomolecules, by probing the surface with a sphere. 
  • HaptiMOL ENM enables users to apply forces to atoms in an elastic network model and to observe the resulting deformation. (A mouse version is also available).
  • HaptiMOL RD (coming soon) will be designed for rigid molecular docking. 
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Institution: University of East Anglia

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