Posters

Poster presentations at ISMB 2020 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. All registered conference participants will have access to the poster and presentation through the conference and content until October 31, 2020. There are Q&A opportunities through a chat function to allow interaction between presenters and participants.

Preliminary information on preparing your poster and poster talk are available at: https://www.iscb.org/ismb2020-general/presenterinfo#posters

Ideally authors should be available for interactive chat during the times noted below:

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Poster Session A: July 13 & July 14 7:45 am - 9:15 am Eastern Daylight Time	Session B: July 15 and July 16 between 7:45 am - 9:15 am Eastern Daylight Time
Bioinfo-core CAMDA COSI COVID-19 Education COSI EvoCompGen COSI Function / CAFA 4 MLCSB COSI SCANGEN (Special Session) SysMod COSI Systems Immunology (Special Session) Text Mining July 14 between 10:40 am - 2:00 pm EDT iRNA COSI	3DSIG COSI Bio-Ontologies COSI BioVis COSI CompMS COSI COVID-19 EvoCompGen COSI HitSeq COSI MICROBIOME COSI NetBio COSI RegSys COSI TransMed COSI VarI COSI General Comp Bio

Short Abstract: Exploring the relationship between genes, brain circuitry, and behavior is a key element in neuroscience research. This requires joint analysis of heterogeneous spatial brain data, including 3D imaging data, anatomical data, and brain networks at varying scales, resolutions, and modalities. Current analytical workflows in neuroscience involve time-consuming manual aggregation of the data and only sparsely incorporate spatial context to operate continuously on multiple scales. Incorporating techniques for handling spatial brain data is therefore a necessity.
We present a novel web-based framework to explore heterogeneous neurobiological spatial data of different types, modalities and scale for interactive visual analytics workflows. It enables domain experts to combine data from large-scale brain initiative by utilizing the hierarchical and spatial organization of the data. Connectivity data at different resolutions, such as mesoscale structural connectivity and region-wise functional connectivity can be accessed on a common hierarchical reference space. On-demand queries on volumetric gene expression and connectivity data enable an interactive dissection of networks, with billions of edges, in real-time.
We demonstrate the relevance of our approach by reproducing findings of known microcircuits in fear and reward-system related functional neuroanatomy in mice. Further, we show its versatility by comparing multimodal brain networks linked to autism.

Short Abstract: With high throughput sequencing, quick insight into data separability for custom sequence datasets is desired to focus experiments on promising candidates. Recently, language models (LMs) have been adapted from use in natural language to work with protein sequences instead. Protein LMs show enormous potential in generating descriptive features for proteins from just their sequences at a fraction of the time of previous approaches. Protein LMs offer to convert amino acid sequences into embeddings that can also be used in combination with dimensionality reduction techniques (e.g. UMAP) to quickly span and visualize protein spaces (e.g. via scatter plots). On 3D scatter plots, proteins can be annotated with known properties to visually gain an intuition, even prior to training supervised models, about the separability of data. Additionally, conclusions can be drawn about proteins without annotation by putting them into the context of labelled proteins. The bio_embeddings pipeline offers an interface to simply and quickly embed large protein sets using protein LMs, to project the embeddings in lower dimensional spaces, and to visualize proteins in these spaces on interactive 3D scatter plots. The pipeline is accompanied by a web server that offers to visualize small protein datasets without the need to install software.

Short Abstract: Epigenomics data analysis frequently involves looking into associated metadata such as using tissue type annotations to find tissue-specific cis-regulatory elements. However, existing visual analytics tools often fail to support these analyses as they do not support visualization of metadata nor scale to large datasets with hundreds of samples. We designed and implemented Cistrome Explorer, a web-based interactive visualization tool for exploring epigenomics data with associated metadata. Cistrome Explorer is built around HiGlass—a visualization library for large heatmaps and genome browser tracks—to enable scalable visual exploration of epigenomics data. For flexible and comprehensive analysis with metadata, Cistrome Explorer provides dedicated visualization tracks for metadata using bar charts and dendrograms. These support diverse user interactions, such as filtering and rearranging samples, which can be useful for narrowing down the analysis or identifying consistent/contradictory patterns based on metadata. Integrated with CistromeDB Toolkit, our tool also supports querying for the list of transcription factors most likely to bind in a user-selected genomic region, based on thousands of ChIP-seq, DNase-seq, and ATAC-seq samples available in CistromeDB. As future work, we plan to implement further integrations with CistromeDB Toolkit and to conduct case studies to assess the usefulness of our tool in real-world scenarios.

Short Abstract: An organism’s metabolome contains all the compounds that are produced by its metabolism. In all multicellular organisms, the metabolomes of different tissues are likely to differ significantly, reflecting the different specialised jobs they perform. Using Drosophila as a model, we have produced an atlas of 19 reference tissue metabolomes, obtained by separately micro-dissection of adult (male and female) and larval Drosophila melanogaster. Using liquid chromatography-mass spectrometry (LC-MS) a snapshot of each tissue’s metabolome was taken in the form of a list of LC-MS peaks, with each peak concisely represented as a tuple of mass-to-charge (m/z), retention time and intensity values.
To present this complex data, we have developed FlyMet (www.flymet.org): a database and Web application that provides user-friendly visualisation of metabolite profiles across Drosophila tissues. To ensure correct data interpretation, FlyMet uses a traffic light system of buttons to show confidence levels for identified metabolites and reveals all of the associated peaks. Users can investigate comprehensive tissue data as peaks, metabolites or pathways (via the Explorer pages) with the Pathway Explorer also ranking differentially-expressed metabolic pathways. FlyMet also allows users to search for individual pathways, metabolites and/or tissues and examine the data associated with each individual attribute.

Short Abstract: Visualization is the primary method in the exploration of genomic aberrations of cancer samples. However, the current state-of-the-art interactive genomic visualization tools (specifically genome browsers) have been designed for specific data formats, which are displayed in very definite, often rigid ways. Adapting the tools for atypical data and rich visual representations is difficult.

Here, we present GenomeSpy, a declarative, grammar-based approach for specifying interactive genomic visualizations that use the graphics processing unit (GPU) in rendering. With combinatorial building blocks such as graphical marks, scales, view composition operators, and transformations, the user can create novel, more appropriate visualizations. Fluid interactions, along with high rendering performance, help the user stay in the flow of exploration.

We demonstrate GenomeSpy with two case studies involving high-grade serous ovarian cancer data. First, we used GenomeSpy to create a tool for scrutinizing raw copy-number variation (CNV) data along with segmentation results. Second, we used the CNV results and point mutations in a multi-sample visualization that allows for exploring and comparing both multiple data dimensions and samples at the same time.

Although our focus has been on cancer research, we envision GenomeSpy being applied to other domains as well. The software is available at genomespy.app/.

Short Abstract: Single-cell transcriptomics has become an increasingly common technique for understanding complex biological systems. Due to the high-dimensional data that is generated by such experiments, algorithms for dimensionality reduction, such as principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), and uniform manifold approximation and project (UMAP), attempt to overcome these problems through projection of the high-dimensional findings into a lower-dimensional space.

As the output of these methods are commonly presented as scatterplots, they often suffer from overplotting, making nearby points difficult to distinguish. Since the spacing between points can be non-uniform, estimation of the relative proportions of cells in each cluster is not practical. Lack of consistent boundaries between points can also make it challenging to interpret cluster membership along with additional parameters, such as gene expression, in the same plot.

Here we present a complementary method to these clustering techniques for single cell data. By using linear assignment to map these projections to an appropriately-sized grid, it becomes possible to preserve overall cell-cell relationships in space, while condensing the space used for the figure, avoiding overplotting, and allowing for easy boundary annotation that permits overlay of additional data

Short Abstract: Tissue functionality is determined by the characteristics of tissue-resident cells and their interactions within their microenvironment. Imaging Mass Cytometry offers the opportunity to distinguish cell types with high precision and link them to their spatial location in intact tissues at sub-cellular resolution. This technology produces large amounts of spatially-resolved high-dimensional data, which constitutes a serious challenge for the data analysis. We present an interactive visual analysis workflow for the end-to-end analysis of Imaging Mass Cytometry data that was developed in close collaboration with domain expert partners. We implemented the presented workflow in an interactive visual analysis tool; ImaCytE. Our workflow is designed to allow the user to discriminate cell types according to their protein expression profiles and analyze their cellular microenvironments, aiding in the formulation or verification of hypotheses on tissue architecture and function. Finally, we show the effectiveness of our workflow and ImaCytE through a case study performed by a collaborating specialist. ImaCytE is open source and the code and binaries are available at github.com/biovault/ImaCytE.

Short Abstract: 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. 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. 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.

Short Abstract: Vitessce (Visual Integration Tool for Exploration of Spatial Single-Cell Experiments) (vitessce.io/) is an open-source, web-based viewer for spatial single-cell omics data. With the rise of single-cell methods, consortia like NIH Human BioMolecular Atlas Program are leading efforts to integrate these technologies, creating complex datasets and new challenges for visualization. Specifically, these datasets describe relationships both in 2D/3D physical space as well as in high-dimensional spaces, e.g., gene expression.

Many of the current visualization tools in this space require specialized server-side software or local storage of large datasets. We’ve leveraged recent advances in web-based technologies, including GPU programming and HTTP/2, to enable computation and rendering in the browser. In addition, we’ve designed Vitessce to work directly with remote cloud storage, which enables researchers to access large datasets via just a URL.

Vitessce uses React with a modular event-bus architecture. The event system is built around biological entities like cells and genes. By trading generality for specific affordances, component authors can accommodate higher-level domain-specific interactions with the data. Core components are built-in, while specialized visualizations are developed independently. For example, Viv, the Vitessce Image Viewer, is developed and packaged separately, and can be used independently of Vitessce.

Short Abstract: Biologists often use computer graphics to visualize structures, which due to physical limitations are not possible to imagewith a microscope. One example for such structures are microtubules, which are present in every eukaryotic cell. They are part ofthe cytoskeleton maintaining the shape of the cell and playing a key role in the cell division. In this paper, we propose a scientifically-accurate multi-scale procedural model of microtubule dynamics as a novel application scenario for procedural animation, which cangenerate visualizations of their overall shape, molecular structure, as well as animations of the dynamic behaviour of their growth anddisassembly. The model is spanning from tens of micrometers down to atomic resolution. All the aspects of the model are driven byscientific data. The advantage over a traditional, manual animation approach is that when the underlying data change, for instance dueto new evidence, the model can be recreated immediately. The procedural animation concept is presented in its generic form, withseveral novel extensions, facilitating an easy translation to other domains with emergent multi-scale behavior.

Short Abstract: Pantograph is an open source browsable pangenome visualization for graph genomes. It allows researchers to see the full genetic diversity in large populations. Graph genomes naturally express genome rearrangements, SNPs, and indels. Using a graph structure provides integration with knowledge graphs containing annotations, geographical locations, and patient outcomes. This makes Pantograph an ideal tool for tracking viral strains of SARS-CoV-2. Without the constraints of a reference genome, viral strains can be smoothly integrated as they are sequenced.

Pantograph is a data and visualization solution that scales to thousands of individuals while preserving all types of sequence variation. Other tools either are not scalable or discard all genome rearrangements. Pantograph achieves scalability by identifying syntenic blocks and interconnecting them with nonlinear variants.

Pantograph’s application to the COVID-19 pandemic is driven by the unique evolutionary scenario we are facing. The infected population size is a multiplier for the number of mutations available for selection. Vaccinating the population during a pandemic is a selection sweep for resistant viral mutations. This is the biggest viral selection sweep in human history. Pantograph can help predict vaccine effectiveness in different regions of the world by integrating our total knowledge of genetic diversity.

Short Abstract: Intuitive visualisation of microscopy data is important for facilitating results interpretation, and identification of interesting patterns. Existing visualisation approaches, such as bar charts and heat maps, do not accommodate the complexity of visual information present in microscopy data. Previously we developed PhenoPlot; the first tool of its kind for visualising cellular imaging data. Here we develop PhenoPlot-v2 that allows visualisation of various structural entities at the molecular, cellular, and/or tissue level. PhenoPlot allows representing the hierarchy or interaction between various objects such as tissue, cells, and nuclei. The user can add multiple objects and define their shapes. For example, an ellipse can be used to represent a cell and a rectangle can represent a tube. If required, the user can draw their object for more complex shapes such as neurons. Multiple visual elements can be defined for each object including object dimensions, colours, pattern filling, line features, and extrusions. The user can map the measured image features to a selection of visual elements resulting in a pictorial representation of these objects. We demonstrate that PhenoPlot aids the identification of interesting patterns when applied to datasets describing phenotypes at the cellular and tissue level.

Short Abstract: To better understand the molecular basis of cancer, the NCI’s Clinical Proteomics Tumor Analysis Consortium (CPTAC) has been performing comprehensive large-scale proteogenomic characterizations of multiple cancer types. Gene and protein regulatory networks are subsequently being derived based on these proteogenomic profiles, which serve as tools to gain systems-level understanding of the molecular regulatory factories underlying these diseases. On the other hand, it remains a challenge to effectively visualize and navigate the resulting network models, which capture higher order structures in the proteogenomic profiles. There is a pressing need to have a new open community resource tool for intuitive visual exploration, interpretation and communication of these gene/protein regulatory networks by the cancer research community. In this work, we introduce ProNetView-ccRCC (ccrcc.cptac-network-view.org/), an interactive web-based network exploration portal for investigating phosphopeptide co-expression network inferred based on the CPTAC clear cell renal cell carcinoma (ccRCC) phosphoproteomics data. ProNetView-ccRCC enables quick, user-intuitive visual interactions with the ccRCC tumor phosphoprotein co-expression network comprised of 3,614 genes, as well as 30 functional pathway-enriched network modules. Users can interact with the network portal and can conveniently query for association between abundance of each phosphopeptide in the network and clinical variables such as tumor grade.

Short Abstract: Isoform detection and discovery at the single cell resolution are central to improving our understanding of heterogeneity in organs and tissues, and visualization tools would be instrumental in exploring this heterogeneity. However, current interactive transcriptome visualization tools are designed for bulk RNA-Seq data, and have limited utility in analyzing single-cell RNA-seq data. Here, we introduce RNA-Scoop, a visualization tool for single cell transcriptomics.

The input of RNA-Scoop is a single JSON file, which specifies the paths to a GTF file containing the isoforms of interest, a matrix file containing their expression levels in each cell, and files containing labels for the matrix rows and columns. Users can select genes for the isoform view, where all isoforms of selected genes are displayed. A t-SNE plot allows users to zoom in and out of different areas and select cells via lasso selection. Upon selection, displayed isoforms are colored according to their average level of expression in the selected cells. Expression per cluster is visualized through a dot plot. Additionally, isoforms are selectable, enabling users to highlight the cells in which isoforms of interest are expressed. Through these easy-to-use features, RNA-Scoop simplifies the interrogation of isoforms and cell types in thousands of cells.

Short Abstract: Event sequence data are ubiquitous in clinical records and provide promising opportunities to better understand and improve medical care. However, the analysis of clinical sequences suffers from the large number of patients, the high dimensionality of features, and the varying length of sequences. Previous studies aimed to facilitate the analysis of clinical sequences by automatically identifying states (e.g., mild or severe symptoms). While these automatic approaches efficiently summarize sequences and reveal hidden patterns, they are usually hard to interpret.

To address this issue, we propose to support interactive state identification in OncoThreads (oncothreads.gehlenborglab.org), a visual analytics tool originally designed for large scale longitudinal cancer genomics data. In OncoThreads, features in a clinical sequence are categorized into two types, timepoint and event. Timepoint features describe patients' conditions at specific timepoints, such as specimen results. Event features describe any event occurring between timepoints, such as chemotherapy. OncoThreads identifies states by learning from timepoint features and uncovers events related to the state transitions. Users can modify the states and examine how a particular feature relates to state definitions. An initial case study on a glioma dataset demonstrates the effectiveness of our proposed approach.

Short Abstract: SwissBioPics is a freely available library of interactive biological images for the visualization of subcellular location data. It covers cell types from all kingdoms of life – ranging from muscle, neuronal and epithelial cells of animals, to the rods, cocci, clubs, spirals and other more exotic forms of bacteria and archaea. A reusable web component and an API allow users to visualize subcellular location data (in the form of Gene Ontology or UniProtKB annotations) on these images. All the user has to do is provide a taxonomy identifier and a list of subcellular locations (GO or UniProtKB), and the web component will select the appropriate image (using data from the UniProt SPARQL endpoint) and highlight the user-provided annotations. SwissBioPics images in SVG include RDFa schema.org mark up to allow reuse of information. The web component is implemented in native JavaScript and can be styled as desired by defining an HTML5 template. It is available on npm.js and biojs.io.
While originally developed for UniProtKB, we hope other developers will adopt SwissBioPics, and would welcome requests to expand the SwissBioPics image library and enhance programmatic access to it.

Short Abstract: Here we discuss our first prototype of an application for the analysis and visualisation of structural brain image data. The application is part of a processing pipeline to help researchers gain new insights by turning raw anatomical brain data into quantitative 3D representations. The pipeline has been optimised to process anatomical fish brain data obtained at a synchrotron imaging facility. The data is phase-retrieved, reconstructed and segmented before a three-dimensional mesh model is created. After being imported into the application, a model can be visualised in an immersive Virtual Reality environment on the HTC Vive headset. Each model can then be analysed by conducting a series of calibrated distance and volume measurements. The application is accompanied by an ImageJ plug-in, which supports users with image segmentation and model pre-processing.