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      Expasy, the Swiss Bioinformatics Resource Portal, as designed by its users

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          Abstract

          The SIB Swiss Institute of Bioinformatics ( https://www.sib.swiss) creates, maintains and disseminates a portfolio of reliable and state-of-the-art bioinformatics services and resources for the storage, analysis and interpretation of biological data. Through Expasy ( https://www.expasy.org), the Swiss Bioinformatics Resource Portal, the scientific community worldwide, freely accesses more than 160 SIB resources supporting a wide range of life science and biomedical research areas. In 2020, Expasy was redesigned through a user-centric approach, known as User-Centred Design (UCD), whose aim is to create user interfaces that are easy-to-use, efficient and targeting the intended community. This approach, widely used in other fields such as marketing, e-commerce, and design of mobile applications, is still scarcely explored in bioinformatics. In total, around 50 people were actively involved, including internal stakeholders and end-users. In addition to an optimised interface that meets users' needs and expectations, the new version of Expasy provides an up-to-date and accurate description of high-quality resources based on a standardised ontology, allowing to connect functionally-related resources.

          Graphical Abstract

          Graphical Abstract

          The illustration of Expasy's redesign through a user-centric approach: all the elements that contributed to the process (icons) and the main strengths (stars) of the new implementation.

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          Most cited references31

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          STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

          Abstract Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein–protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein–protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.
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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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              Nextstrain: real-time tracking of pathogen evolution

              Abstract Summary Understanding the spread and evolution of pathogens is important for effective public health measures and surveillance. Nextstrain consists of a database of viral genomes, a bioinformatics pipeline for phylodynamics analysis, and an interactive visualization platform. Together these present a real-time view into the evolution and spread of a range of viral pathogens of high public health importance. The visualization integrates sequence data with other data types such as geographic information, serology, or host species. Nextstrain compiles our current understanding into a single accessible location, open to health professionals, epidemiologists, virologists and the public alike. Availability and implementation All code (predominantly JavaScript and Python) is freely available from github.com/nextstrain and the web-application is available at nextstrain.org.
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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
                02 July 2021
                13 April 2021
                13 April 2021
                : 49
                : W1
                : W216-W227
                Affiliations
                SIB Swiss Institute of Bioinformatics , Quartier Sorge - Bâtiment Amphipôle, CH-1015 Lausanne, Switzerland
                SIB Swiss Institute of Bioinformatics , Quartier Sorge - Bâtiment Amphipôle, CH-1015 Lausanne, Switzerland
                Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, and Computer Science Department, University of Geneva , CH-1227 Geneva, Switzerland
                Section of Biology, University of Geneva , CH-1205 Geneva, Switzerland
                SIB Swiss Institute of Bioinformatics , Quartier Sorge - Bâtiment Amphipôle, CH-1015 Lausanne, Switzerland
                SIB Swiss Institute of Bioinformatics , Quartier Sorge - Bâtiment Amphipôle, CH-1015 Lausanne, Switzerland
                SIB Swiss Institute of Bioinformatics , Quartier Sorge - Bâtiment Amphipôle, CH-1015 Lausanne, Switzerland
                Author notes
                To whom correspondence should be addressed. Tel: +41 21 692 40 50; Email: severine.duvaud@ 123456sib.swiss
                Correspondence may also be addressed to Chiara Gabella. Tel: +41 21 692 40 50; Email: chiara.gabella@ 123456sib.swiss

                The authors wish to be known that, in their opinion, this first two authors should be regarded as joint First Authors.

                Author information
                https://orcid.org/0000-0001-7892-9678
                https://orcid.org/0000-0002-7104-5025
                https://orcid.org/0000-0002-0948-4537
                Article
                gkab225
                10.1093/nar/gkab225
                8265094
                33849055
                5189859d-c949-4803-aa33-1019041c4d45
                © 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
                : 01 April 2021
                : 11 March 2021
                : 02 February 2021
                Page count
                Pages: 12
                Funding
                Funded by: SIB Swiss Institute of Bioinformatics;
                Categories
                AcademicSubjects/SCI00010
                Web Server Issue

                Genetics
                Genetics

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