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      DrugComb update: a more comprehensive drug sensitivity data repository and analysis portal

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          Abstract

          Combinatorial therapies that target multiple pathways have shown great promises for treating complex diseases. DrugComb ( https://drugcomb.org/) is a web-based portal for the deposition and analysis of drug combination screening datasets. Since its first release, DrugComb has received continuous updates on the coverage of data resources, as well as on the functionality of the web server to improve the analysis, visualization and interpretation of drug combination screens. Here, we report significant updates of DrugComb, including: (i) manual curation and harmonization of more comprehensive drug combination and monotherapy screening data, not only for cancers but also for other diseases such as malaria and COVID-19; (ii) enhanced algorithms for assessing the sensitivity and synergy of drug combinations; (iii) network modelling tools to visualize the mechanisms of action of drugs or drug combinations for a given cancer sample and (iv) state-of-the-art machine learning models to predict drug combination sensitivity and synergy. These improvements have been provided with more user-friendly graphical interface and faster database infrastructure, which make DrugComb the most comprehensive web-based resources for the study of drug sensitivities for multiple diseases.

          Graphical Abstract

          Graphical Abstract

          A schematic overview of the DrugComb portal.

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          Most cited references52

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          UniProt: a worldwide hub of protein knowledge

          (2018)
          Abstract The UniProt Knowledgebase is a collection of sequences and annotations for over 120 million proteins across all branches of life. Detailed annotations extracted from the literature by expert curators have been collected for over half a million of these proteins. These annotations are supplemented by annotations provided by rule based automated systems, and those imported from other resources. In this article we describe significant updates that we have made over the last 2 years to the resource. We have greatly expanded the number of Reference Proteomes that we provide and in particular we have focussed on improving the number of viral Reference Proteomes. The UniProt website has been augmented with new data visualizations for the subcellular localization of proteins as well as their structure and interactions. UniProt resources are available under a CC-BY (4.0) license via the web at https://www.uniprot.org/.
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            DrugBank 5.0: a major update to the DrugBank database for 2018

            Abstract DrugBank (www.drugbank.ca) is a web-enabled database containing comprehensive molecular information about drugs, their mechanisms, their interactions and their targets. First described in 2006, DrugBank has continued to evolve over the past 12 years in response to marked improvements to web standards and changing needs for drug research and development. This year’s update, DrugBank 5.0, represents the most significant upgrade to the database in more than 10 years. In many cases, existing data content has grown by 100% or more over the last update. For instance, the total number of investigational drugs in the database has grown by almost 300%, the number of drug-drug interactions has grown by nearly 600% and the number of SNP-associated drug effects has grown more than 3000%. Significant improvements have been made to the quantity, quality and consistency of drug indications, drug binding data as well as drug-drug and drug-food interactions. A great deal of brand new data have also been added to DrugBank 5.0. This includes information on the influence of hundreds of drugs on metabolite levels (pharmacometabolomics), gene expression levels (pharmacotranscriptomics) and protein expression levels (pharmacoprotoemics). New data have also been added on the status of hundreds of new drug clinical trials and existing drug repurposing trials. Many other important improvements in the content, interface and performance of the DrugBank website have been made and these should greatly enhance its ease of use, utility and potential applications in many areas of pharmacological research, pharmaceutical science and drug education.
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              KEGG: integrating viruses and cellular organisms

              Abstract KEGG (https://www.kegg.jp/) is a manually curated resource integrating eighteen databases categorized into systems, genomic, chemical and health information. It also provides KEGG mapping tools, which enable understanding of cellular and organism-level functions from genome sequences and other molecular datasets. KEGG mapping is a predictive method of reconstructing molecular network systems from molecular building blocks based on the concept of functional orthologs. Since the introduction of the KEGG NETWORK database, various diseases have been associated with network variants, which are perturbed molecular networks caused by human gene variants, viruses, other pathogens and environmental factors. The network variation maps are created as aligned sets of related networks showing, for example, how different viruses inhibit or activate specific cellular signaling pathways. The KEGG pathway maps are now integrated with network variation maps in the NETWORK database, as well as with conserved functional units of KEGG modules and reaction modules in the MODULE database. The KO database for functional orthologs continues to be improved and virus KOs are being expanded for better understanding of virus-cell interactions and for enabling prediction of viral perturbations.
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                Author and article information

                Contributors
                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                01 June 2021
                01 June 2021
                : gkab438
                Affiliations
                Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki , Helsinki FI-00290, Finland
                Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki , Helsinki FI-00290, Finland
                Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki , Helsinki FI-00290, Finland
                Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki , Helsinki FI-00290, Finland
                Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki , Helsinki FI-00290, Finland
                Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki , Helsinki FI-00290, Finland
                Institute for Molecular Medicine Finland, University of Helsinki , Helsinki FI-00290, Finland
                Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki , Helsinki FI-00290, Finland
                Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki , Helsinki FI-00290, Finland
                Author notes
                To whom correspondence should be addressed. Tel: +35 845 868 9708; Email: jing.tang@ 123456helsinki.fi
                Author information
                https://orcid.org/0000-0001-7480-7710
                Article
                gkab438
                10.1093/nar/gkab438
                8218202
                34060634
                6ad65369-1cfb-498e-afe5-35c4efaa1bdb
                © The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 06 May 2021
                : 18 April 2021
                : 24 March 2021
                Page count
                Pages: 11
                Funding
                Funded by: European Research Council, DOI 10.13039/100010663;
                Award ID: 716063
                Funded by: European Commission H2020;
                Award ID: 824087
                Funded by: Academy of Finland, DOI 10.13039/501100002341;
                Award ID: 317680
                Funded by: Sigrid Jusélius Foundation, DOI 10.13039/501100006306;
                Funded by: University of Helsinki, DOI 10.13039/100007797;
                Funded by: K. Albin Johanssons Stiftelse, DOI 10.13039/501100009067;
                Categories
                AcademicSubjects/SCI00010
                Web Server Issue
                Custom metadata
                PAP

                Genetics
                Genetics

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