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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

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.

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.

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Institution: Institut Pasteur

Developed in Java using Eclipse RCP/RAP API

 

Supramap

Supramap is a completely new method of generating and sharing knowledge about evolution and biogeography.  A supramap gives people a quick and easy way to integrate genotypic and phenotypic data in a geospatial context. When viewed in a virtual globe (e.g. Google Earth or NASA WorldWind), the user has an interactive map of the spread of various lineages of organisms (e.g. strains of pathogens) over the earth.

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Institution: UNC Charlotte

Epiviz

Visualization is an integral aspect of genomics data analysis. Algorithmic-statistical analysis and interactive visualization are most effective when used iteratively. Epiviz, a web-based genome browser, and the Epivizr Bioconductor package allow interactive, extensible and reproducible visualization within a state-of-the-art data-analysis platform.

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Institution: University of Maryland, USA

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.

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

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