Posters - Schedules
Poster presentations at ISMB/ECCB 2021 will be presented virtually. Authors will pre-record their poster talk (5-7
minutes) and will upload it to the virtual conference platform site along with a PDF of their poster beginning July 19
and no later than July 23. All registered conference participants will have access to the poster and presentation
through the conference and content until October 31, 2021. There are Q&A opportunities through a chat
function and poster presenters can schedule small group discussions with up to 15 delegates during the conference.
Information on preparing your poster and poster talk are available at:
https://www.iscb.org/ismbeccb2021-general/presenterinfo#posters
Ideally authors should be available for interactive chat during the times noted below:
View Posters By Category
Session A: Sunday, July 25 between 15:20 - 16:20 UTC |
Session B: Monday, July 26 between 15:20 - 16:20 UTC |
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Session C: Tuesday, July 27 between 15:20 - 16:20 UTC |
Session D: Wednesday, July 28 between 15:20 - 16:20 UTC |
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Session E: Thursday, July 29 between 15:20 - 16:20 UTC |
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Short Abstract: Pangenomes are complex and malleable entities, listing common and unique genomic content within a group of genomes. As repertoires of present and absent genes they apply well to bacteria which have almost no ‘wasted’ genomic material. As inventories of all available sequences instead they might be better for more complex genomes (human, plants…) whose intergenic spaces and structural variations have multiple effects on said genes and their expression. Added to this is a diversity of inner properties (most represented parts, ‘openness’…) and usages (as references, genome storage …).
Visualizing pangenomes inherits this complexity, with additional challenges of data-to-available-space ratio and understandability, among others. Earlier representations showed pangenomes as genes shared between sets in Venn diagrams, or presence absence matrix of genes, with scalability issues or no support for a sequence centric definition of pangenomes. Recent efforts describe pangenomes as graphs of sequences, with genomes as paths within them. While faithful to the underlying sequences they are not easily readable for humans, especially when involving a large amount of data.
In exploring possible visualizations, we worked on linearized representations to enhance readability and explorability. We created Panache, our web-based viewer for browsing through linearized pangenomes.
Short Abstract: Whether engaging in drug discovery or explaining molecular interactions, biologists grapple with large multi-scale graphs to find relevant subgraphs for answering a range of questions. Existing biological visualization approaches are broadly of two types: static overviews of the entire graph or interactive small-scale views of subgraphs. Visualization theory suggests, however, that multi-scale graph sense-making is too complex a task for any single view. To this end, we propose an approach for scalable graph analysis wherein two coordinated views enable biologists to interactively explore, query, and analyze graphs at different scales. A 'global' view of the whole graph organizes nodes hierarchically in a biomedical ontology using a circle packing layout, with hyper-edges bundled according to zoom level. Real-time graph queries - from metadata search to complex path-finding - produce results as highlighted graph sections. The biologist can query the global view iteratively and extract subgraphs of interest into a 'local' view with a flow-layout for further examination. Local subgraphs can be interactively expanded via machine-assisted suggestions, and specific graph elements can be interrogated for source knowledge from scientific literature. Early interactions with computational biologists have validated our approach for use cases involving biological graphs with 50,000 nodes and 500,000 edges.
Short Abstract: Understanding complex biological mechanisms of carcinogenesis using genomic and clinical data is vital, to develop new treatment for patients, and improve survival prognosis. High dimensional single-cell data poses challenges in terms of visualization and interpretability. In studying the performance of the most used linear, nonlinear, and neural network methods, we propose a robust analytical pipeline suitable for benchmarking dimensionality reduction methods for targeted biological questions. We define a multivariate metric for good visualization and interpretability by optimizing five features, characterizing the quality of projection in terms of fidelity of good coverage, uniform spread of the projected data, preserving structure of the original dataset, time dependency of the projected data, and robustness to outliers of dense clusters. To account for dependency of these features respecting the accuracy of a method, we build a Bayesian regression model of independent variables of metrics’ features and dependent variable of accuracy. The model predicts the conditional effect of metrics which is summarized as performance measures of good projection. By comparing the performance of six models applied to single-cell-based dynamic processes, we confirm that optimizing variational autoencoders preserves the most meaningful properties of a given biological process after data reduction and provide better visualization and interpretability.
Short Abstract: To visualize intersects between datasets, two main types of plots are available: Venn diagrams and UpSet plots. Although Venn diagrams provide a common solution to visualize the relation between 2-3 datasets, application in the context of ≥5 sets is usually not recommended. By contrast, UpSet plots are able to clearly visualize intersects between ≥5 datasets. However, the more sets and intersects are present, the more difficult it gets to visualize all information in a single plot. Furthermore, information on a second time point cannot be added.
We introduce CooPer (CO-Ocurrence and PERsistence) plots as a novel approach of visualizing the relation between datasets. CooPer plots are based on the idea of interaction networks: Every set is visualized as a node. The intersect between two sets is visualized by the edges. The size of the node corresponds to the set size. The thickness of the edge corresponds to the number of intersects. By using color coding, information on a second time point may be added to both the nodes and the edges.
Analyzing two examples (initial vs persisting symptoms in COVID-19; mutations in MDS) we show the wide area of applicability of CooPer plots. Our application is freely available at sand-imi-uni-muenster.shinyapps.io/CooPerPlots/.
Short Abstract: The ongoing pandemic caused by SARS-CoV-2 emphasizes the importance of molecular surveillance to understand the evolution of the virus and to monitor and plan the epidemiological responses. Prompt analysis, easy visualization and convenient filtering of viral sequences are essential for this purpose. Here we present CovRadar, a tool for molecular surveillance of the coronavirus spike protein. The spike protein contains the receptor binding domain (RBD) that is used as a target for most vaccine candidates. CovRadar consists of a Snakemake pipeline and a Flask/Dash web application, which combined enable the analysis and visualization of over 1 million sequences in a timely manner. First, CovRadar extracts the regions of interest using local alignment, then builds a codon-aware multiple sequence alignment, infers variants, consensus sequences, phylogenetic trees and finally displays the results in an interactive PDF-like app, making reporting fast, easy and flexible. CovRadar is freely accessible at covradar.net, while the code is open-source and available at gitlab.com/dacs-hpi/covradar.
Short Abstract: Deep learning has demonstrated remarkable potential for identifying novel therapeutic uses of existing drugs (i.e., drug repurposing). However, the black-box nature of deep models can severely hinder their use in drug development.
To enfuse interpretability into deep drug-repurposing models, we combine interactive visualization with explainable machine learning. Targeted at graph neural networks, we propose a visualization method to extract and present explanations that 1) can be easily interpreted in the biomedical context and 2) can scale well with different granularities of analysis.
We first identify and tackle the mismatch between model-generated explanations and human-level explanations. The former can be characterized as a subset of a knowledge graph while the latter can be formalized as a path in the knowledge graph reflecting a biological mechanism. Interactive visualizations are then developed to enable easy switching between model explanations and human explanations.
We further summarize explanations through meta-paths, which are sequences of relation types summarized from individual explanation paths. Explanations using meta-paths enable analysis and comparison of predictions at different granularities (e.g., individual predictions, groups of predictions for similar diseases).
We demonstrate the proposed approach on a repurposing dataset across the entire range of approved drugs and human diseases.
Short Abstract: The combination of diverse data types and analysis tasks in genomics has resulted in the development of a wide range of visualization techniques and tools. However, most existing tools are tailored to a specific problem or data type and offer limited customization, making it challenging to optimize visualizations for new analysis tasks or datasets.
To address this challenge, we designed Gosling—a grammar for interactive and scalable genomics data visualization. Gosling balances expressiveness for comprehensive multi-scale genomics data visualizations with accessibility for domain scientists. For example, Gosling allows creating complex glyph representations (e.g., gene annotations, lollipop plots, and ideograms), use both linear and circular layouts (i.e., using Cartesian and polar coordinates), and link views and interactive brushes for synchronous visual explorations. Our accompanying JavaScript toolkit called Gosling.js provides scalable and interactive rendering. Gosling.js is built on top of an existing platform for web-based genomics data visualization to further simplify the visualization of common genomics data formats.
We re-implemented a variety of real-world examples to demonstrate the expressiveness of the grammar. Furthermore, we show how Gosling supports the design of novel genomics visualizations. An online editor and examples of Gosling.js and its source code are available at gosling.js.org.
Short Abstract: A fundamental task in the analysis of single cell RNA-seq is unsupervised (or semi-supervised) clustering to help identify cell types. A purely computational approach to determine an appropriate number of clusters, and subsequently cell types, is usually unsatisfactory. Users need to specify parameters (number of clusters or resolution) and perform post-hoc analysis to determine the number of clusters. This is an exploratory, interactive analysis and approaches for interactivity are critical for effective analysis.
We present scTreeViz, a Bioconductor package to interactively visualize multi-resolution clusterings by exploiting their hierarchical organization using the facetZoom navigation technique. Users can explore finer or coarser resolutions of the hierarchy, remove clusters not pertinent to their analysis and dynamically aggregate measurements at different resolutions. These interactions update multiple linked interactive visualizations: low dimensional embeddings, heatmaps of gene expression, and boxplots of expression of specific genes across cell types.
scTreeViz integrates Bioconductor’s `Seurat` and `Single Cell Experiment` objects and can be used as part of an analysis workflow. We present examples where scTreeViz is used after clustering to select clusters at multiple resolutions, extract cluster labels, and proceed with downstream analysis, e.g., determining marker genes, using Bioconductor tools.
Short Abstract: The increasing amount of data produced by omics technologies has significantly improved the understanding of how biological information is transferred across different omics layers. Besides data-driven analysis strategies, interactive visualization tools have been developed to make the analysis in the multi-omics field more transparent. However, most state-of-the-art tools do not reconstruct the impact of a given omics layer on the final integration result.
To identify the requirements for a tool addressing this issue we classified omics data focusing on different aspects of multi-omics data sets, such as data type and experimental design. Based on this classification we developed the Omics Trend-comparing Interactive Data Explorer (OmicsTIDE), an interactive visualization tool. The tool consists of an automated part that clusters transcriptomics and proteomics data to determine trends and an interactive visualization. The trends are visualized as profile plots and are connected by a Sankey diagram that allows an interactive pairwise trend comparison to discover concordant and discordant trends. Moreover, large-scale omics data sets are broken down into small subsets within few analysis steps. In future work we plan to extend OmicsTIDE to more omics levels, such as metabolomics.
Short Abstract: Phosphorylation is a vital cellular mechanism, where a kinase protein phosphorylates a substrate protein residue. Recent advances in quantitative proteomics have allowed rapid data generation on multiple cellular states (e.g. time points or perturbations). Each state is typically visualized as a network, where nodes represent the kinases and substrates, and directed edges the phosphorylation events. The phosphorylation statuses of all phosphorylated residues also need to be visualized simultaneously. However, currently available tools are not optimized for visualizing phosphoproteomics networks, necessitating manual parameter adjustment to maximize usefulness, minimize clutter, and improve visualization design. There is a need for a tool that facilitates the user-intuitive and interactive exploration, visualization and communication of phosphoproteomics networks. Here, we present PhosProViz, a tool that enables users to easily generate shareable interactive 3D phosphoproteomics network visualizations with phosphorylation sites at multiple states, by simply uploading a comma-separated data file. Users can explore and query these visualizations based on their protein, phosphorylation site, state, or user-defined variables of interest. The tool is based on JavaScript, is open-source and has been user tested. PhosProViz will significantly lower the barriers for researchers in rapidly generating intuitive and high-quality phosphoproteomics visualizations, empowering investigators to translate rich data into biological insights.
Short Abstract: Low complexity regions (LCRs) in protein sequences are characterized by a low diversity of amino acid composition. These fragments of sequences often play an important role in proteins. For instance, they are responsible for protein binding in protein-protein interactions and DNA binding in transcription factor. Therefore identification of LCRs is an important step to study these regions. Several methods for LCR extraction have been developed in the past years. However, most of them are available as standalone applications only. Furthermore, they provide only a list of discovered regions without any functional annotation.
Here we present PlaToLoCo - PLAtform of TOols for LOw COmplexity. A web server that integrates five state-of-the-art tools for LCR identification: SEG, CAST, fLPS, SIMPLE and GBSC. It shows a given set of sequences with low complexity fragments found by each of selected methods in a comparative way providing separate, consensus and sum results of those methods. It also provides functional annotations like detected domains, transmembrane segments prediction and amino acid frequency. For the first time we provide the community with such a fast and easily accessible tool for the analysis of LCRs. PlaToLoCo is available at: platoloco.aei.polsl.pl
Short Abstract: Vitessce is a web-based interactive visualization framework for multi-modal single-cell omics data. Individual studies and consortia such as the NIH Human BioMolecular Atlas Program generate multi-modal datasets comprising thousands of single cells profiled with assays such as RNA-seq, ATAC-seq, ChIP-seq, and fluorescence in situ hybridization. Integration of data from these technologies creates new challenges for visualization. Specifically, these datasets describe relationships both in 2D/3D physical space as well as in high-dimensional spaces. Many of the current visualization tools in this space limit analysis to one modality, for instance with standalone genome browsers, image viewers, and heatmap viewers. Vitessce overcomes this challenge with a modular architecture, allowing users to build a grid of visualizations which match the data types of interest for multi-modal assays.
To facilitate interactive visualization within the Python and R bioinformatics ecosystems, we have created Jupyter and RStudio/Shiny widgets for Vitessce. Alongside these widgets, we have developed programming interfaces for configuring Vitessce and loading data from commonly used single-cell file formats. Together, the Python and R packages currently support visualization of datasets stored in AnnData, Loom, and Seurat formats. The Python and R packages can be installed from the Python Package Index and GitHub, respectively.
Short Abstract: As the year 2020 came to a close, several new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concerns (VOCs) have been reported and new VOCs continue to emerge due to relatively fast spreading and high mutation rate of SARS-CoV-2. However, it is difficult to comprehend the scale, in sequence space, geographical location and time, at which SARS-CoV-2 mutates and evolves in its human hosts. To get an appreciation for the rapid evolution of the coronavirus, we built interactive scalable vector graphics (SVG) maps that show nucleotide variations rapidly accumulating on the SARS-CoV-2 genome compared to that of the initial ground-zero SARS-CoV-2 isolate (Wuhan-Hu-1) sequenced in January 2020. To build our SVG maps, which include specific VOCs sampled from the six most populated continents, we periodically access the GISAID repository, chart nucleotide variations in the GISAID catalogue relative to the Wuhan-Hu-1 reference and organize them by date/jurisdiction and evaluate their predicted effect on the gene product. The information is plotted with interactive SVGs files, which are hosted publicly (bcgsc.github.io/SARS2/) and can be queried with mouse-hover to gain rapid insights into emergence. We think these maps will be of utility to researchers in their exploration of SARS-CoV-2 variants.
Short Abstract: Intuitive visualisation of quantitative microscopy data is crucial for interpreting and discovering new patterns in complex bioimage data. Existing visualisation approaches, such as bar charts, scatter plots and heat maps, do not accommodate the complexity of visual information present in microscopy data. Here we develop ShapoGraphy, a first of its kind method accompanied by a user-friendly web-based application for creating interactive quantitative pictorial representations of phenotypic data and facilitating the understanding and analysis of image datasets (www.shapography.com). ShapoGraphy enables the user to create a structure of interest as a set of shapes. Each shape can encode different variables that are mapped to the shape dimensions, colours, symbols, and stroke features. We illustrate the utility of ShapoGraphy using various image data, including high dimensional multiplexed data. Our results show that ShapoGraphy allows a better understanding of cellular phenotypes and relationships between variables. In conclusion, ShopoGraphy supports scientific discovery and communication by providing a wide range of users with a rich vocabulary to create engaging and intuitive representations of diverse data types.
Short Abstract: Nowadays RNA-seq is the norm for investigating transcriptome-wide gene expression. These data cover the expression of thousands of genes over many different tissues and conditions. Finding the biological story hidden in this massive amount of data is challenging. Moreover, standard data analysis methods using 2D, non-interactive visualization are limiting and daunting. To upgrade to state-of-the-art technology, we developed TranScape VR, a new tool for visualizing transcriptomic landscapes in an immersive, explorative, holistic and entertaining VR (Virtual Reality) space. We used transcriptomic data of seven different organs and tissues of the Venus flytrap (Dionaea muscipula), a deadly carnivorous plant. In our quest to identify tissue specific genes, we represent the data as an interactive network, mapping genes to each tissue core-node, forming gene clusters. Parameters such as gene expression level and tissue specificity are depicted by the gene nodes’ colour, size, distance, and direction relative to the tissue core-nodes within the 3D space. The user can explore and analyse the multidimensional dataset by re-scaling, interacting, retrieving genes’ description as well as filtering or highlighting genes based on functional annotation. Thus, TranScape VR enables to conjure up the treasures hidden in the vast pile of transcriptomic data.
Short Abstract: Multiple genome assemblies from the same species are a powerful tool to explore the diversity and highly selected haplotypes in the process of domestication and improvement. This is of particular importance on crop species, such as wheat (Triticum aestivum), as there are regions of agricultural value that had been purposedly selected; foreign sequence from hybridizations with related species; and there has been a reduction of genetic diversity product of intensive breeding. We developed a haplotype-based approach to identify genetic diversity for crop improvement using genome assemblies from 15 bread wheat cultivars
We developed a relational database and a dynamic visualisation to explore the identical-by-state (IBS) regions in their genomic context. We cover both: pseudomolecules and scaffolds-level assemblies. We use the gene projected genes from the main reference (cultivar Chinese Spring) to anchor the position of scaffolds. We use the same projections to efficiently produce an approximation of the relative coordinate systems between assemblies. The users can identify which IBS regions are shared among cultivars in a specific position or from the point of view of a cultivar.
Our visualisation provides intuitive tools to explore pangenomes in the context of crop breeding, but the principle can be adapted for other organisms.
