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      BioMOBS: A multi-omics visual analytics workflow for biomolecular insight generation

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          Abstract

          One of the challenges in multi-omics data analysis for precision medicine is the efficient exploration of undiscovered molecular interactions in disease processes. We present BioMOBS, a workflow consisting of two data visualization tools integrated with an open-source molecular information database to perform clinically relevant analyses ( https://github.com/driesheylen123/BioMOBS). We performed exploratory pathway analysis with BioMOBS and demonstrate its ability to generate relevant molecular hypotheses, by reproducing recent findings in type 2 diabetes UK biobank data. The central visualisation tool, where data-driven and literature-based findings can be integrated, is available within the github link as well. BioMOBS is a workflow that leverages information from multiple data-driven interactive analyses and visually integrates it with established pathway knowledge. The demonstrated use cases place trust in the usage of BioMOBS as a procedure to offer clinically relevant insights in disease pathway analyses on various types of omics data.

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          Most cited references16

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          KEGG: kyoto encyclopedia of genes and genomes.

          M Kanehisa (2000)
          KEGG (Kyoto Encyclopedia of Genes and Genomes) is a knowledge base for systematic analysis of gene functions, linking genomic information with higher order functional information. The genomic information is stored in the GENES database, which is a collection of gene catalogs for all the completely sequenced genomes and some partial genomes with up-to-date annotation of gene functions. The higher order functional information is stored in the PATHWAY database, which contains graphical representations of cellular processes, such as metabolism, membrane transport, signal transduction and cell cycle. The PATHWAY database is supplemented by a set of ortholog group tables for the information about conserved subpathways (pathway motifs), which are often encoded by positionally coupled genes on the chromosome and which are especially useful in predicting gene functions. A third database in KEGG is LIGAND for the information about chemical compounds, enzyme molecules and enzymatic reactions. KEGG provides Java graphics tools for browsing genome maps, comparing two genome maps and manipulating expression maps, as well as computational tools for sequence comparison, graph comparison and path computation. The KEGG databases are daily updated and made freely available (http://www. genome.ad.jp/kegg/).
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            The reactome pathway knowledgebase 2022

            The Reactome Knowledgebase ( https://reactome.org ), an Elixir core resource, provides manually curated molecular details across a broad range of physiological and pathological biological processes in humans, including both hereditary and acquired disease processes. The processes are annotated as an ordered network of molecular transformations in a single consistent data model. Reactome thus functions both as a digital archive of manually curated human biological processes and as a tool for discovering functional relationships in data such as gene expression profiles or somatic mutation catalogs from tumor cells. Recent curation work has expanded our annotations of normal and disease-associated signaling processes and of the drugs that target them, in particular infections caused by the SARS-CoV-1 and SARS-CoV-2 coronaviruses and the host response to infection. New tools support better simultaneous analysis of high-throughput data from multiple sources and the placement of understudied (‘dark’) proteins from analyzed datasets in the context of Reactome’s manually curated pathways.
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              STITCH: interaction networks of chemicals and proteins

              The knowledge about interactions between proteins and small molecules is essential for the understanding of molecular and cellular functions. However, information on such interactions is widely dispersed across numerous databases and the literature. To facilitate access to this data, STITCH (‘search tool for interactions of chemicals’) integrates information about interactions from metabolic pathways, crystal structures, binding experiments and drug–target relationships. Inferred information from phenotypic effects, text mining and chemical structure similarity is used to predict relations between chemicals. STITCH further allows exploring the network of chemical relations, also in the context of associated binding proteins. Each proposed interaction can be traced back to the original data sources. Our database contains interaction information for over 68 000 different chemicals, including 2200 drugs, and connects them to 1.5 million genes across 373 genomes and their interactions contained in the STRING database. STITCH is available at http://stitch.embl.de/
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Formal analysisRole: MethodologyRole: ResourcesRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Formal analysisRole: MethodologyRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: MethodologyRole: SupervisionRole: VisualizationRole: Writing – review & editing
                Role: ConceptualizationRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: MethodologyRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLOS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                2023
                14 December 2023
                : 18
                : 12
                : e0295361
                Affiliations
                [1 ] Theory Lab, Data Science Institute (DSI), Hasselt University, Diepenbeek, Belgium
                [2 ] Flemish Institute for Technological Research (VITO), Mol, Belgium
                [3 ] Data Science Institute (DSI), Hasselt University, Diepenbeek, Belgium
                [4 ] Visual Data Analysis Lab, Department of Biostystems KU Leuven, Leuven, Belgium
                Boyce Thompson Institute, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0001-7112-9651
                Article
                PONE-D-23-20129
                10.1371/journal.pone.0295361
                10721075
                38096184
                5fb9f2a0-a47f-493f-8c2e-0b88b3f52017
                © 2023 Heylen et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 28 June 2023
                : 19 November 2023
                Page count
                Figures: 4, Tables: 0, Pages: 10
                Funding
                Funded by: Bijzonder Onderzoeksfonds
                Award ID: BOF20OWB29
                Award Recipient :
                Funded by: Bijzonder Onderzoeksfonds
                Award ID: BOF20OWB33
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100007610, Vlaamse Instelling voor Technologisch Onderzoek;
                Award ID: R-11362
                Award Recipient :
                DH and JP are funded through Hasselt University BOF grants (BOF20OWB29 \& BOF20OWB33). D.H. also receives funding from VITO NV (R-11362). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Medicine and Health Sciences
                Endocrinology
                Endocrine Disorders
                Diabetes Mellitus
                Type 2 Diabetes
                Medicine and Health Sciences
                Medical Conditions
                Metabolic Disorders
                Diabetes Mellitus
                Type 2 Diabetes
                Biology and Life Sciences
                Biochemistry
                Biology and Life Sciences
                Biochemistry
                Biomarkers
                Computer and Information Sciences
                Data Management
                Data Visualization
                Engineering and Technology
                Measurement
                Distance Measurement
                Computer and Information Sciences
                Software Engineering
                Source Code
                Engineering and Technology
                Software Engineering
                Source Code
                Medicine and Health Sciences
                Clinical Genetics
                Personalized Medicine
                Precision Medicine
                Medicine and Health Sciences
                Health Care
                Patients
                Custom metadata
                UK Biobank has approval from the North West Multi-centre Research Ethics Committee (MREC) as a Research Tissue Bank (RTB) approval. The data used in this manuscript can be requested via the data access management system of UK biobank: https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access. To complete an application form you will need the following information: - A summary of the research you intend to conduct - The UK Biobank data-fields you require - A description of any new data or variables your research will generate.

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