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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

panX

panX is a powerful interactive platform for pan-genome exploration. It provides multiple different views on annotated genomes and allows rapid search by gene name, diversity, duplications, etc. Strain-specific meta data is integrated into the phylogenetic tree viewer such that associations between gene presence and phenotypes can be spotted.

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Institution: Max Planck Institute for Developmental Biology, Tuebingen, Germany

iobio

iobio uses immediate visual feedback to make understanding complex genomic datasets more intuitive, and analysis more interactive.  Applications include:

  • gene.iobio.io: a web app for investigating potential disease-causing variants
  • taxonomer.iobio.io: an ultra fast metagenomics classification and analysis app
  • bam.iobio.io: an alignment data inspector tool that quickly samples bam files and visualizes a series of metrics
  • vcf.iobio.io: a variant data inspector tool that quickly samples vcf files and visualizes a series of metrics
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Institution: Marth lab, USTAR Center for Genetic Discovery, University of Utah

EK3D

The inherent conformation of polysaccharides possess major challenge to determine/predict their 3D structure; despite their importance in various biological processes in all the organisms. Capsular polysaccharides (K antigen) are long-chain polysaccharides that make up the capsules of encapsulated Gram-negative bacteria. They help bacteria to escape from host defense mechanisms. We have developed a manually curated 3D structures’ database for 72 K antigens from various serotypes of Escherichia coli and developed an organized repository namely EK3D, which can be accessed by www.iith.ac.in/EK3D/. Using the Cartesian coordinate’s translation method, multimers of all 72 K antigens of E. coli can be generated of any desired length and torsion angles. Subsequently, generated modelscan be downloaded and used in docking studies or as starting model for NMR structure refinement.

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Institution: Department of Biotechnology, Indian Institute of Technology Hyderabad, Kandi, Telangana, India

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