42-Issue 3

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https://diglib.eg.org/handle/10.2312/2633338

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Browsing 42-Issue 3 by Subject "Bioinformatics"

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    GO-Compass: Visual Navigation of Multiple Lists of GO terms
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) Harbig, Theresa; Witte Paz, Mathias; Nieselt, Kay; Bujack, Roxana; Archambault, Daniel; Schreck, Tobias
    Analysis pipelines in genomics, transcriptomics, and proteomics commonly produce lists of genes, e.g., differentially expressed genes. Often these lists overlap only partly or not at all and contain too many genes for manual comparison. However, using background knowledge, such as the functional annotations of the genes, the lists can be abstracted to functional terms. One approach is to run Gene Ontology (GO) enrichment analyses to determine over- and/or underrepresented functions for every list of genes. Due to the hierarchical structure of the Gene Ontology, lists of enriched GO terms can contain many closely related terms, rendering the lists still long, redundant, and difficult to interpret for researchers. In this paper, we present GO-Compass (Gene Ontology list comparison using Semantic Similarity), a visual analytics tool for the dispensability reduction and visual comparison of lists of GO terms. For dispensability reduction, we adapted the REVIGO algorithm, a summarization method based on the semantic similarity of GO terms, to perform hierarchical dispensability clustering on multiple lists. In an interactive dashboard, GO-Compass offers several visualizations for the comparison and improved interpretability of GO terms lists. The hierarchical dispensability clustering is visualized as a tree, where users can interactively filter out dispensable GO terms and create flat clusters by cutting the tree at a chosen dispensability. The flat clusters are visualized in animated treemaps and are compared using a correlation heatmap, UpSet plots, and bar charts. With two use cases on published datasets from different omics domains, we demonstrate the general applicability and effectiveness of our approach. In the first use case, we show how the tool can be used to compare lists of differentially expressed genes from a transcriptomics pipeline and incorporate gene information into the analysis. In the second use case using genomics data, we show how GO-Compass facilitates the analysis of many hundreds of GO terms. For qualitative evaluation of the tool, we conducted feedback sessions with five domain experts and received positive comments. GO-Compass is part of the Tue- Vis Visualization Server as a web application available at https://go-compass-tuevis.cs.uni-tuebingen.de/
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    visMOP - A Visual Analytics Approach for Multi-omics Pathways
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) Brich, Nicolas; Schacherer, Nadine; Hoene, Miriam; Weigert, Cora; Lehmann, Rainer; Krone, Michael; Bujack, Roxana; Archambault, Daniel; Schreck, Tobias
    We present an approach for the visual analysis of multi-omics data obtained using high-throughput methods. The term ''omics'' denotes measurements of different types of biologically relevant molecules, like the products of gene transcription (transcriptomics) or the abundance of proteins (proteomics). Current popular visualization approaches often only support analyzing each of these omics separately. This, however, disregards the interconnectedness of different biologically relevant molecules and processes. Consequently, it describes the actual events in the organism suboptimally or only partially. Our visual analytics approach for multi-omics data provides a comprehensive overview and details-on-demand by integrating the different omics types in multiple linked views. To give an overview, we map the measurements to known biological pathways and use a combination of a clustered network visualization, glyphs, and interactive filtering. To ensure the effectiveness and utility of our approach, we designed it in close collaboration with domain experts and assessed it using an exemplary workflow with real-world transcriptomics, proteomics, and lipidomics measurements from mice.