Enhancer-Gene Visualization

An interactive web-based visualization for browsing enhancer-gene connections genome-wide

The goal of this web application is to provide a scalable visual interface to enhancer-gene connections predicted by the Activity-By-Contact (ABC) model and how they correlate with genetic variants.

Website: https://flekschas.github.io/enhancer-gene-vis/
Source code: https://github.com/flekschas/enhancer-gene-vis

Piling.js

A JavaScript Library For Building Visual Piling Interface

Piling.js is a general framework and library for creating visual piling interfaces to explore and compare large collections of small multiples. Piling.js is built around a data-agnostic WebGL-based rendering pipeline and a declarative view specification to avoid having to write low-level code. For more background information see https://piling.lekschas.de.

Website: https://piling.js.org
Source code: https://github.com/flekschas/piling.js

Peax

Interactive visual pattern search in sequential data using unsupervised deep representation learning

Peax is a novel feature-based technique for interactive visual pattern search in sequential data based on a convolutional autoencoder for unsupervised representation learning of regions in sequential data. Peax enables interactive feedback-driven adjustments of the pattern search to adapt to the users' perceived similarity, for which an active learning strategy is employed to focus the labeling process on useful regions for training a classifier. ScreenIt has been developed in collaboration with Novartis Institute for Biomedical Research.

Website: http://peax.lekschas.de
Source code: https://github.com/novartis/peax

HiGlass

Web-based Visual Comparison And Exploration Of Genome Interaction Maps

HiGlass is a tool for exploring genomic contact matrices and tracks. Please take a look at the examples and documentation for a description of the ways that it can be configured to explore and compare contact matrices at varying scales.

Website: http://higlass.io
Source code: https://github.com/hms-dbmi/higlass

HiPiler

An interactive web application for exploring and visualizing regions-of-interest in large genome interaction matrices.

HiPiler is an interactive visualization interface for the exploration and visualization of regions-of-interest (ROI) in large genome interaction matrices. 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.

Website: http://hipiler.lekschas.de
Source code: https://github.com/flekschas/hipiler

ScreenIt

Visual Analysis of Cellular Screens

ScreenIt enables the navigation and analysis of multivariate data from high-throughput and high-content screening at multiple hierarchy levels and at multiple levels of detail. It integrates the interactive modeling of cell physical states (phenotypes) and the effects of drugs on cell cultures (hits). In addition, quality control is enabled via the detection of anomalies that indicate low-quality data, while providing an interface that is designed according to typical work flows of screening experts. ScreenIt has been developed in collaboration with Novartis Institute for Biomedical Research.

Website: https://vcglab.org/screenit/
Source code: https://github.com/kdinkla/Screenit

Vials

Visualizing Alternative Splicing in Genes

Vials is a novel visual analysis tool that enables analysts to explore the various datasets that scientists use to make judgments about isoforms: the abundance of reads associated with the coding regions of the gene, evidence for junctions, i.e., edges connecting the coding regions, and predictions of isoform frequencies. Vials is scalable as it allows for the simultaneous analysis of many samples in multiple groups.

Website: http://vials.io/info
Source code: https://github.com/Caleydo/vials

Caleydo

Visualization for Molecular Biology

Caleydo is an open source visual analysis framework targeted at biomolecular data. The biggest strength of Caleydo is the visualization of interdependencies between multiple datasets. Caleydo can load tabular data and groupings/clusterings. You can explore relationships between multiple groupings, between different datasets and see how your data maps onto pathways. Caleydo has been successfully used to analyze mRNA, miRNA, methylation, copy number variation, mutation status and clinical data as well as other dataset types.

Website: http://www.caleydo.org
Source code: https://github.com/Caleydo