A taxonomy of visualization tasks for the analysis of biological pathway data

Towards a hybrid user interface for the visual exploration of large biomolecular networks using virtual reality

Biomolecular networks, including genome-scale metabolic models (GSMMs), assemble the knowledge regarding the biological processes that happen inside specific organisms in a way that allows for analysis, simulation, and exploration. With the increasing availability of genome annotations and the development of powerful reconstruction tools, biomolecular networks continue to grow ever larger. While visual exploration can facilitate the understanding of such networks, the network sizes represent a major challenge for current visualisation systems. Building on promising results from the area of immersive analytics, which among others deals with the potential of immersive visualisation for data analysis, we present a concept for a hybrid user interface that combines a classical desktop environment with a virtual reality environment for the visual exploration of large biomolecular networks and corresponding data. We present system requirements and design considerations, describe a resulting concept, an envisioned technical realisation, and a systems biology usage scenario. Finally, we discuss remaining challenges.

Uncertainty Visualization: Concepts, Methods, and Applications in Biological Data Visualization

This paper provides an overview of uncertainty visualization in general, along with specific examples of applications in bioinformatics. Starting from a processing and interaction pipeline of visualization, components are discussed that are relevant for handling and visualizing uncertainty introduced with the original data and at later stages in the pipeline, which shows the importance of making the stages of the pipeline aware of uncertainty and allowing them to propagate uncertainty. We detail concepts and methods for visual mappings of uncertainty, distinguishing between explicit and implict representations of distributions, different ways to show summary statistics, and combined or hybrid visualizations. The basic concepts are illustrated for several examples of graph visualization under uncertainty. Finally, this review paper discusses implications for the visualization of biological data and future research directions.

Machine Learning to Support the Presentation of Complex Pathway Graphs

Visualization of biological mechanisms by means of pathway graphs is necessary to better understand the often complex underlying system. Manual layout of such pathways or maps of knowledge is a difficult and time consuming process. Node duplication is a technique that makes layouts with improved readability possible by reducing edge crossings and shortening edge lengths in drawn diagrams. In this article, we propose an approach using Machine Learning (ML) to facilitate parts of this task by training a Support Vector Machine (SVM) with actions taken during manual biocuration. Our training input is a series of incremental snapshots of a diagram describing mechanisms of a disease, progressively curated by a human expert employing node duplication in the process. As a test of the trained SVM models, they are applied to a single large instance and 25 medium-sized instances of hand-curated biological pathways. Finally, in a user validation study, we compare the model predictions to the outcome of a node duplication questionnaire answered by users of biological pathways with varying experience. We successfully predicted nodes for duplication and emulated human choices, demonstrating that our approach can effectively learn human-like node duplication preferences to support curation of pathway diagrams in various contexts.

RainBio: Proportional Visualization of Large Sets in Biology

Set visualization is a well-known task in information visualization. In biology, it is used for comparing visually sets of genes or proteins, typically using Venn diagrams. However, limitations of the Venn diagram are well-known: they are limited to 6 sets and difficult to read above 4. Many other set visualization techniques have been proposed, but they have never been widely used in biology. In this paper, we introduce RainBio, a technique for visualizing sets in biology and aimed at providing a global overview showing the size of the main intersections, in a proportional way, and the similarities between sets. We adapt rainbow boxes, a technique for visualizing small datasets, to the visualization of larger sets, using element aggregation and intersection clustering. We present the application of RainBio to three datasets, with 5, 6 and 12 sets. We also describe a small user study comparing RainBio with Venn diagrams, involving 30 students in biology. Results showed that RainBio led to significantly fewer errors on 6-set dataset, and that the majority of students preferred RainBio. RainBio is proposed as a web-based tool for up to 15 sets.

Interactive Multiresolution Visualization of Cellular Network Processes

Visualization plays a central role in the analysis of biochemical network models to identify patterns that arise from reaction dynamics and perform model exploratory analysis. To facilitate these analyses, we developed PyViPR, a visualization tool that generates static and dynamic representations of biochemical network processes within a Python-based environment. PyViPR embeds network visualizations within Jupyter notebooks, thus enabling integration with modeling, simulation, and analysis workflows. To present the capabilities of PyViPR, we explore execution mechanisms of extrinsic apoptosis in HeLa cells. We show that community-detection algorithms identify groups of molecular species that capture key biological functions and ease exploration of the apoptosis network. We then show how different kinetic parameter sets that fit the experimental data equally well exhibit significantly different signal-execution dynamics as the system progresses toward mitochondrial outer-membrane permeabilization. Therefore, PyViPR aids the conceptual understanding of dynamic network processes and accelerates hypothesis generation for further testing and validation.

An overview of designing and selection of sgRNAs for precise genome editing by the CRISPR-Cas9 system in plants

A large number of computational tools have been documented in recent years for identification of target-specific valid single-guide (sg) RNAs (18-20 nucleotide long sequence) that is an important component for the efficient utilization of the CRISPR-Cas9 (clustered regularly interspaced short palindromic repeats-CRISPR-associated Protein) system. Despite optimization of Cas9, other major concerns are on-target efficiency and off-target activity that depend upon the sequence(s) of target-specific sgRNA(s). However, a very little attention has been paid for identification of the best-hit sgRNA for precise targeting as well as minimizing the off-target effects. The aim of this present work is to offer comparative insight into existing CRISPR software tools with their unique features (including targeted genome) and utilities. These available web tools were found to be designed based upon only a few limited mathematical models. Among all these available web tools, three (Benchling, Desktop and CRISPR-P) have been curated as exclusively available for plant genome-editing purpose. These three software tools have been comprehensively described and analyzed with single same target enquiry from two randomly selected genes (IDM2 and IDM3 from Arabidopsis thaliana). Interestingly, all these selected tools generated different results (sgRNAs) even for the same query. In fact, the sequence of sgRNA is considered an important parameter to determine the efficiency and specificity of sgRNAs for precise genome editing. Thus, there is an urgent requirement to pay attention for a validated sgRNA-designing tool for precise DNA editing in plants. In conclusion, this work will encourage building up a consensus for developing a universal valid sgRNA designing for different organisms including plants.

Metabopolis: scalable network layout for biological pathway diagrams in urban map style

Background

Biological pathways represent chains of molecular interactions in biological systems that jointly form complex dynamic networks. The network structure changes from the significance of biological experiments and layout algorithms often sacrifice low-level details to maintain high-level information, which complicates the entire image to large biochemical systems such as human metabolic pathways.

Results

Our work is inspired by concepts from urban planning since we create a visual hierarchy of biological pathways, which is analogous to city blocks and grid-like road networks in an urban area. We automatize the manual drawing process of biologists by first partitioning the map domain into multiple sub-blocks, and then building the corresponding pathways by routing edges schematically, to maintain the global and local context simultaneously. Our system incorporates constrained floor-planning and network-flow algorithms to optimize the layout of sub-blocks and to distribute the edge density along the map domain. We have developed the approach in close collaboration with domain experts and present their feedback on the pathway diagrams based on selected use cases.

Conclusions

We present a new approach for computing biological pathway maps that untangles visual clutter by decomposing large networks into semantic sub-networks and bundling long edges to create space for presenting relationships systematically.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2779-4) contains supplementary material, which is available to authorized users.

Integrating bioinformatics approaches for a comprehensive interpretation of metabolomics datasets

Access to high quality metabolomics data has become a routine component for biological studies. However, interpreting those datasets in biological contexts remains a challenge, especially because many identified metabolites are not found in biochemical pathway databases. Starting from statistical analyses, a range of new tools are available, including metabolite set enrichment analysis, pathway and network visualization, pathway prediction, biochemical databases and text mining. Integrating these approaches into comprehensive and unbiased interpretations must carefully consider both caveats of the metabolomics dataset itself as well as the structure and properties of the biological study design. Special considerations need to be taken when adopting approaches from genomics for use in metabolomics. R and Python programming language are enabling an easier exchange of diverse tools to deploy integrated workflows. This review summarizes the key ideas and latest developments in regards to these approaches.

Communicating Genome Architecture: Biovisualization of the Genome, from Data Analysis and Hypothesis Generation to Communication and Learning

Genome discoveries at the core of biology are made by visual description and exploration of the cell, from microscopic sketches and biochemical mapping to computational analysis and spatial modeling. We outline the experimental and visualization techniques that have been developed recently which capture the three-dimensional interactions regulating how genes are expressed. We detail the challenges faced in integration of the data to portray the components and organization and their dynamic landscape. The goal is more than a single data-driven representation as interactive visualization for de novo research is paramount to decipher insights on genome organization in space.

An Analysis of Automated Visual Analysis Classification: Interactive Visualization Task Inference of Cancer Genomics Domain Experts

We show how mouse interaction log classification can help visualization toolsmiths understand how their tools are used "in the wild" through an evaluation of MAGI - a cancer genomics visualization tool. Our primary contribution is an evaluation of twelve visual analysis task classifiers, which compares predictions to task inferences made by pairs of genomics and visualization experts. Our evaluation uses common classifiers that are accessible to most visualization evaluators: -nearest neighbors, linear support vector machines, and random forests. By comparing classifier predictions to visual analysis task inferences made by experts, we show that simple automated task classification can have up to 73 percent accuracy and can separate meaningful logs from "junk" logs with up to 91 percent accuracy. Our second contribution is an exploration of common MAGI interaction trends using classification predictions, which expands current knowledge about ecological cancer genomics visualization tasks. Our third contribution is a discussion of how automated task classification can inform iterative tool design. These contributions suggest that mouse interaction log analysis is a viable method for (1) evaluating task requirements of client-side-focused tools, (2) allowing researchers to study experts on larger scales than is typically possible with in-lab observation, and (3) highlighting potential tool evaluation bias.

The Kappa platform for rule-based modeling

Motivation

We present an overview of the Kappa platform, an integrated suite of analysis and visualization techniques for building and interactively exploring rule-based models. The main components of the platform are the Kappa Simulator, the Kappa Static Analyzer and the Kappa Story Extractor. In addition to these components, we describe the Kappa User Interface, which includes a range of interactive visualization tools for rule-based models needed to make sense of the complexity of biological systems. We argue that, in this approach, modeling is akin to programming and can likewise benefit from an integrated development environment. Our platform is a step in this direction.

Results

We discuss details about the computation and rendering of static, dynamic, and causal views of a model, which include the contact map (CM), snaphots at different resolutions, the dynamic influence network (DIN) and causal compression. We provide use cases illustrating how these concepts generate insight. Specifically, we show how the CM and snapshots provide information about systems capable of polymerization, such as Wnt signaling. A well-understood model of the KaiABC oscillator, translated into Kappa from the literature, is deployed to demonstrate the DIN and its use in understanding systems dynamics. Finally, we discuss how pathways might be discovered or recovered from a rule-based model by means of causal compression, as exemplified for early events in EGF signaling.

Availability and implementation

The Kappa platform is available via the project website at kappalanguage.org. All components of the platform are open source and freely available through the authors' code repositories.

Dynamic Influence Networks for Rule-Based Models

We introduce the Dynamic Influence Network (DIN), a novel visual analytics technique for representing and analyzing rule-based models of protein-protein interaction networks. Rule-based modeling has proved instrumental in developing biological models that are concise, comprehensible, easily extensible, and that mitigate the combinatorial complexity of multi-state and multi-component biological molecules. Our technique visualizes the dynamics of these rules as they evolve over time. Using the data produced by KaSim, an open source stochastic simulator of rule-based models written in the Kappa language, DINs provide a node-link diagram that represents the influence that each rule has on the other rules. That is, rather than representing individual biological components or types, we instead represent the rules about them (as nodes) and the current influence of these rules (as links). Using our interactive DIN-Viz software tool, researchers are able to query this dynamic network to find meaningful patterns about biological processes, and to identify salient aspects of complex rule-based models. To evaluate the effectiveness of our approach, we investigate a simulation of a circadian clock model that illustrates the oscillatory behavior of the KaiC protein phosphorylation cycle.

Challenges for visualizing three-dimensional data in genomic browsers

Genomic interactions reveal the spatial organization of genomes and genomic domains, which is known to play key roles in cell function. Physical proximity can be represented as two-dimensional heat maps or matrices. From these, three-dimensional (3D) conformations of chromatin can be computed revealing coherent structures that highlight the importance of nonsequential relationships across genomic features. Mainstream genomic browsers have been classically developed to display compact, stacked tracks based on a linear, sequential, per-chromosome coordinate system. Genome-wide comparative analysis demands new approaches to data access and new layouts for analysis. The legibility can be compromised when displaying track-aligned second dimension matrices, which require greater screen space. Moreover, 3D representations of genomes defy vertical alignment in track-based genome browsers. Furthermore, investigation at previously unattainable levels of detail is revealing multiscale, multistate, time-dependent complexity. This article outlines how these challenges are currently handled in mainstream browsers as well as how novel techniques in visualization are being explored to address them. A set of requirements for coherent visualization of novel spatial genomic data is defined and the resulting potential for whole genome visualization is described.