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Sandrine Pilbout

Bio: Sandrine Pilbout is an academic researcher from University of Basel. The author has contributed to research in topics: UniProt & Universal Protein Resource. The author has an hindex of 8, co-authored 8 publications receiving 10949 citations.

Papers
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Journal ArticleDOI
Alex Bateman, Maria Jesus Martin, Claire O'Donovan, Michele Magrane, Rolf Apweiler, Emanuele Alpi, Ricardo Antunes, Joanna Arganiska, Benoit Bely, Mark Bingley, Carlos Bonilla, Ramona Britto, Borisas Bursteinas, Gayatri Chavali, Elena Cibrian-Uhalte, Alan Wilter Sousa da Silva, Maurizio De Giorgi, Tunca Doğan, Francesco Fazzini, Paul Gane, Leyla Jael Garcia Castro, Penelope Garmiri, Emma Hatton-Ellis, Reija Hieta, Rachael P. Huntley, Duncan Legge, W Liu, Jie Luo, Alistair MacDougall, Prudence Mutowo, Andrew Nightingale, Sandra Orchard, Klemens Pichler, Diego Poggioli, Sangya Pundir, Luis Pureza, Guoying Qi, Steven Rosanoff, Rabie Saidi, Tony Sawford, Aleksandra Shypitsyna, Edward Turner, Vladimir Volynkin, Tony Wardell, Xavier Watkins, Hermann Zellner, Andrew Peter Cowley, Luis Figueira, Weizhong Li, Hamish McWilliam, Rodrigo Lopez, Ioannis Xenarios, Lydie Bougueleret, Alan Bridge, Sylvain Poux, Nicole Redaschi, Lucila Aimo, Ghislaine Argoud-Puy, Andrea H. Auchincloss, Kristian B. Axelsen, Parit Bansal, Delphine Baratin, Marie Claude Blatter, Brigitte Boeckmann, Jerven Bolleman, Emmanuel Boutet, Lionel Breuza, Cristina Casal-Casas, Edouard de Castro, Elisabeth Coudert, Béatrice A. Cuche, M Doche, Dolnide Dornevil, Séverine Duvaud, Anne Estreicher, L Famiglietti, Marc Feuermann, Elisabeth Gasteiger, Sebastien Gehant, Vivienne Baillie Gerritsen, Arnaud Gos, Nadine Gruaz-Gumowski, Ursula Hinz, Chantal Hulo, Florence Jungo, Guillaume Keller, Vicente Lara, P Lemercier, Damien Lieberherr, Thierry Lombardot, Xavier D. Martin, Patrick Masson, Anne Morgat, Teresa Batista Neto, Nevila Nouspikel, Salvo Paesano, Ivo Pedruzzi, Sandrine Pilbout, Monica Pozzato, Manuela Pruess, Catherine Rivoire, Bernd Roechert, Michel Schneider, Christian J. A. Sigrist, K Sonesson, S Staehli, Andre Stutz, Shyamala Sundaram, Michael Tognolli, Laure Verbregue, Anne Lise Veuthey, Cathy H. Wu, Cecilia N. Arighi, Leslie Arminski, Chuming Chen, Yongxing Chen, John S. Garavelli, Hongzhan Huang, Kati Laiho, Peter B. McGarvey, Darren A. Natale, Baris E. Suzek, C. R. Vinayaka, Qinghua Wang, Yuqi Wang, Lai-Su L. Yeh, Meher Shruti Yerramalla, Jian Zhang 
TL;DR: An annotation score for all entries in UniProt is introduced to represent the relative amount of knowledge known about each protein to help identify which proteins are the best characterized and most informative for comparative analysis.
Abstract: UniProt is an important collection of protein sequences and their annotations, which has doubled in size to 80 million sequences during the past year. This growth in sequences has prompted an extension of UniProt accession number space from 6 to 10 characters. An increasing fraction of new sequences are identical to a sequence that already exists in the database with the majority of sequences coming from genome sequencing projects. We have created a new proteome identifier that uniquely identifies a particular assembly of a species and strain or subspecies to help users track the provenance of sequences. We present a new website that has been designed using a user-experience design process. We have introduced an annotation score for all entries in UniProt to represent the relative amount of knowledge known about each protein. These scores will be helpful in identifying which proteins are the best characterized and most informative for comparative analysis. All UniProt data is provided freely and is available on the web at http://www.uniprot.org/.

4,050 citations

Journal ArticleDOI
Alex Bateman, Maria Jesus Martin, Sandra Orchard, Michele Magrane, Rahat Agivetova, Shadab Ahmad, Emanuele Alpi, Emily H Bowler-Barnett, Ramona Britto, Borisas Bursteinas, Hema Bye-A-Jee, Ray Coetzee, Austra Cukura, Alan Wilter Sousa da Silva, Paul Denny, Tunca Doğan, ThankGod Ebenezer, Jun Fan, Leyla Jael Garcia Castro, Penelope Garmiri, George Georghiou, Leonardo Gonzales, Emma Hatton-Ellis, Abdulrahman Hussein, Alexandr Ignatchenko, Giuseppe Insana, Rizwan Ishtiaq, Petteri Jokinen, Vishal Joshi, Dushyanth Jyothi, Antonia Lock, Rodrigo Lopez, Aurelien Luciani, Jie Luo, Yvonne Lussi, Alistair MacDougall, Fábio Madeira, Mahdi Mahmoudy, Manuela Menchi, Alok Mishra, Katie Moulang, Andrew Nightingale, Carla Susana Oliveira, Sangya Pundir, Guoying Qi, Shriya Raj, Daniel Rice, Milagros Rodriguez Lopez, Rabie Saidi, Joseph Sampson, Tony Sawford, Elena Speretta, Edward Turner, Nidhi Tyagi, Preethi Vasudev, Vladimir Volynkin, Kate Warner, Xavier Watkins, Rossana Zaru, Hermann Zellner, Alan Bridge, Sylvain Poux, Nicole Redaschi, Lucila Aimo, Ghislaine Argoud-Puy, Andrea H. Auchincloss, Kristian B. Axelsen, Parit Bansal, Delphine Baratin, Marie-Claude Blatter, Jerven Bolleman, Emmanuel Boutet, Lionel Breuza, Cristina Casals-Casas, Edouard de Castro, Kamal Chikh Echioukh, Elisabeth Coudert, Béatrice A. Cuche, M Doche, Dolnide Dornevil, Anne Estreicher, Maria Livia Famiglietti, Marc Feuermann, Elisabeth Gasteiger, Sebastien Gehant, Vivienne Baillie Gerritsen, Arnaud Gos, Nadine Gruaz-Gumowski, Ursula Hinz, Chantal Hulo, Nevila Hyka-Nouspikel, Florence Jungo, Guillaume Keller, Arnaud Kerhornou, Vicente Lara, Philippe Le Mercier, Damien Lieberherr, Thierry Lombardot, Xavier D. Martin, Patrick Masson, Anne Morgat, Teresa Batista Neto, Salvo Paesano, Ivo Pedruzzi, Sandrine Pilbout, Lucille Pourcel, Monica Pozzato, Manuela Pruess, Catherine Rivoire, Christian J. A. Sigrist, K Sonesson, Andre Stutz, Shyamala Sundaram, Michael Tognolli, Laure Verbregue, Cathy H. Wu, Cecilia N. Arighi, Leslie Arminski, Chuming Chen, Yongxing Chen, John S. Garavelli, Hongzhan Huang, Kati Laiho, Peter B. McGarvey, Darren A. Natale, Karen E. Ross, C. R. Vinayaka, Qinghua Wang, Yuqi Wang, Lai-Su L. Yeh, Jian Zhang, Patrick Ruch, Douglas Teodoro 
TL;DR: The UniProtKB responded to the COVID-19 pandemic through expert curation of relevant entries that were rapidly made available to the research community through a dedicated portal and a credit-based publication submission interface was developed.
Abstract: Abstract The aim of the UniProt Knowledgebase is to provide users with a comprehensive, high-quality and freely accessible set of protein sequences annotated with functional information. In this article, we describe significant updates that we have made over the last two years to the resource. The number of sequences in UniProtKB has risen to approximately 190 million, despite continued work to reduce sequence redundancy at the proteome level. We have adopted new methods of assessing proteome completeness and quality. We continue to extract detailed annotations from the literature to add to reviewed entries and supplement these in unreviewed entries with annotations provided by automated systems such as the newly implemented Association-Rule-Based Annotator (ARBA). We have developed a credit-based publication submission interface to allow the community to contribute publications and annotations to UniProt entries. We describe how UniProtKB responded to the COVID-19 pandemic through expert curation of relevant entries that were rapidly made available to the research community through a dedicated portal. UniProt resources are available under a CC-BY (4.0) license via the web at https://www.uniprot.org/.

4,001 citations

Journal ArticleDOI
TL;DR: The SWISS-PROT protein knowledgebase connects amino acid sequences with the current knowledge in the Life Sciences by providing an interdisciplinary overview of relevant information by bringing together experimental results, computed features and sometimes even contradictory conclusions.
Abstract: The SWISS-PROT protein knowledgebase (http://www.expasy.org/sprot/ and http://www.ebi.ac.uk/swissprot/) connects amino acid sequences with the current knowledge in the Life Sciences. Each protein entry provides an interdisciplinary overview of relevant information by bringing together experimental results, computed features and sometimes even contradictory conclusions. Detailed expertise that goes beyond the scope of SWISS-PROT is made available via direct links to specialised databases. SWISS-PROT provides annotated entries for all species, but concentrates on the annotation of entries from human (the HPI project) and other model organisms to ensure the presence of high quality annotation for representative members of all protein families. Part of the annotation can be transferred to other family members, as is already done for microbes by the High-quality Automated and Manual Annotation of microbial Proteomes (HAMAP) project. Protein families and groups of proteins are regularly reviewed to keep up with current scientific findings. Complementarily, TrEMBL strives to comprise all protein sequences that are not yet represented in SWISS-PROT, by incorporating a perpetually increasing level of mostly automated annotation. Researchers are welcome to contribute their knowledge to the scientific community by submitting relevant findings to SWISS-PROT at swiss-prot@expasy.org.

3,440 citations

Journal ArticleDOI
Rolf Apweiler, Alex Bateman, Maria Jesus Martin, Claire O'Donovan, Michele Magrane, Yasmin Alam-Faruque, Emanuele Alpi, Ricardo Antunes, J Arganiska, EB Casanova, Benoit Bely, M Bingley, Carlos Bonilla, Ramona Britto, Borisas Bursteinas, WM Chan, Gayatri Chavali, Elena Cibrian-Uhalte, A Da Silva, M De Giorgi, Tunca Doğan, F. Fazzini, Paul Gane, Leyla Jael Garcia Castro, Penelope Garmiri, Emma Hatton-Ellis, Reija Hieta, Rachael P. Huntley, Duncan Legge, W Liu, Jie Luo, Alistair MacDougall, Prudence Mutowo, Andrew Nightingale, Sandra Orchard, Klemens Pichler, Diego Poggioli, Sangya Pundir, L Pureza, Guoying Qi, S. Rosanoff, Rabie Saidi, Tony Sawford, Aleksandra Shypitsyna, Edd Turner, Volynkin, Tony Wardell, Xavier Watkins, Hermann Zellner, Matthew Corbett, M Donnelly, P van Rensburg, Mickael Goujon, Hamish McWilliam, Rodrigo Lopez, Ioannis Xenarios, Lydie Bougueleret, Alan Bridge, Sylvain Poux, Nicole Redaschi, Lucila Aimo, Andrea H. Auchincloss, Kristian B. Axelsen, Parit Bansal, Delphine Baratin, P-A Binz, M. C. Blatter, Brigitte Boeckmann, Jerven Bolleman, Emmanuel Boutet, Lionel Breuza, C Casal-Casas, E de Castro, Lorenzo Cerutti, Elisabeth Coudert, Béatrice A. Cuche, M Doche, Dolnide Dornevil, Séverine Duvaud, Anne Estreicher, L Famiglietti, M Feuermann, Elisabeth Gasteiger, Sebastien Gehant, Gerritsen, Arnaud Gos, Nadine Gruaz-Gumowski, Ursula Hinz, Chantal Hulo, J. James, Florence Jungo, Guillaume Keller, Lara, P Lemercier, J Lew, Damien Lieberherr, Thierry Lombardot, Xavier D. Martin, Patrick Masson, Anne Morgat, Teresa Batista Neto, Salvo Paesano, Ivo Pedruzzi, Sandrine Pilbout, Monica Pozzato, Manuela Pruess, Catherine Rivoire, Bernd Roechert, Maria Victoria Schneider, Christian J. A. Sigrist, K Sonesson, S Staehli, Andre Stutz, Shyamala Sundaram, Michael Tognolli, Laure Verbregue, A-L Veuthey, Cathy H. Wu, Cecilia N. Arighi, Leslie Arminski, Chuming Chen, Yongxing Chen, John S. Garavelli, Hongzhan Huang, Kati Laiho, Peter B. McGarvey, Darren A. Natale, Baris E. Suzek, C. R. Vinayaka, Qinghua Wang, Yuqi Wang, L-S Yeh, Yerramalla, Jie Zhang 
TL;DR: The mission of the Universal Protein Resource (UniProt) is to provide the scientific community with a comprehensive, high-quality and freely accessible resource of protein sequences and functional annotation.
Abstract: The mission of the Universal Protein Resource (UniProt) (http://www.uniprot.org) is to provide the scientific community with a comprehensive, high-quality and freely accessible resource of protein sequences and functional annotation. It integrates, interprets and standardizes data from literature and numerous resources to achieve the most comprehensive catalog possible of protein information. The central activities are the biocuration of the UniProt Knowledgebase and the dissemination of these data through our Web site and web services. UniProt is produced by the UniProt Consortium, which consists of groups from the European Bioinformatics Institute (EBI), the SIB Swiss Institute of Bioinformatics (SIB) and the Protein Information Resource (PIR). UniProt is updated and distributed every 4 weeks and can be accessed online for searches or downloads.

1,845 citations

Rolf Apweiler, Maria Jesus Martin, Claire O'Donovan, Michele Magrane, Yasmin Alam-Faruque, Ricardo Antunes, Daniel Barrell, Benoit Bely, M Bingley, David Binns, Lynette Bower, Paul Browne, WM Chan, E. Dimmer, Ruth Y. Eberhardt, A. Fedotov, Rebecca E. Foulger, John S. Garavelli, Rachael P. Huntley, Julius O.B. Jacobsen, M. Kleen, Kati Laiho, Rasko Leinonen, Duncan Legge, Quan Lin, W Liu, Jie Luo, Sandra Orchard, Samuel Patient, Diego Poggioli, Manuela Pruess, Matthew Corbett, G di Martino, M Donnelly, P van Rensburg, Amos Marc Bairoch, Lydie Bougueleret, Ioannis Xenarios, S Altairac, Andrea H. Auchincloss, Ghislaine Argoud-Puy, Kristian B. Axelsen, Delphine Baratin, M. C. Blatter, Brigitte Boeckmann, Jerven Bolleman, L. Bollondi, Emmanuel Boutet, SB Quintaje, Lionel Breuza, Alan Bridge, E. Decastro, L Ciapina, D Coral, Elisabeth Coudert, Isabelle Cusin, G Delbard, M Doche, Dolnide Dornevil, Paula Duek Roggli, Séverine Duvaud, Anne Estreicher, L Famiglietti, M Feuermann, Sebastien Gehant, N. Farriol-Mathis, Serenella Ferro, Elisabeth Gasteiger, Alain Gateau, Gerritsen, Arnaud Gos, Nadine Gruaz-Gumowski, Ursula Hinz, Chantal Hulo, Nicolas Hulo, J. James, S. Jimenez, Florence Jungo, T. Kappler, Guillaume Keller, Corinne Lachaize, L Lane-Guermonprez, Petra S. Langendijk-Genevaux, Lara, P Lemercier, Damien Lieberherr, Tdo Lima, Mangold, Xavier D. Martin, Patrick Masson, M. Moinat, Anne Morgat, Anaïs Mottaz, Salvo Paesano, Ivo Pedruzzi, Sandrine Pilbout, Pillet, Sylvain Poux, Monica Pozzato, Nicole Redaschi, Catherine Rivoire, Bernd Roechert, Maria Victoria Schneider, Christian J. A. Sigrist, K Sonesson, S Staehli, Eleanor J Stanley, Andre Stutz, Shyamala Sundaram, Michael Tognolli, Laure Verbregue, A-L Veuthey, L Yip, L Zuletta, Cathy H. Wu, Cecilia N. Arighi, Leslie Arminski, Winona C. Barker, Chuming Chen, Yingfei Chen, Z-Z Hu, Hongzhan Huang, Raja Mazumder, Peter B. McGarvey, Darren A. Natale, Jules Nchoutmboube, Natalia V. Petrova, N Subramanian, Baris E. Suzek, U. Ugochukwu, Sona Vasudevan, C. R. Vinayaka, LS Yeh, Jian Zhang 
01 Jan 2010

961 citations


Cited by
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Journal ArticleDOI
15 Jul 2021-Nature
TL;DR: For example, AlphaFold as mentioned in this paper predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture. But the accuracy is limited by the fact that no homologous structure is available.
Abstract: Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort1–4, the structures of around 100,000 unique proteins have been determined5, but this represents a small fraction of the billions of known protein sequences6,7. Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’8—has been an important open research problem for more than 50 years9. Despite recent progress10–14, existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14)15, demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.

10,601 citations

Journal ArticleDOI
23 Jan 2015-Science
TL;DR: In this paper, a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level.
Abstract: Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body.

9,745 citations

Journal ArticleDOI
TL;DR: In addition to maintaining the GenBank(R) nucleic acid sequence database, the National Center for Biotechnology Information (NCBI) provides data analysis and retrieval resources for the data in GenBank and other biological data made available through NCBI’s website.
Abstract: In addition to maintaining the GenBank(R) nucleic acid sequence database, the National Center for Biotechnology Information (NCBI) provides data analysis and retrieval resources for the data in GenBank and other biological data made available through NCBI's website. NCBI resources include Entrez, PubMed, PubMed Central, LocusLink, the NCBI Taxonomy Browser, BLAST, BLAST Link (BLink), Electronic PCR, OrfFinder, Spidey, RefSeq, UniGene, HomoloGene, ProtEST, dbMHC, dbSNP, Cancer Chromosome Aberration Project (CCAP), Entrez Genomes and related tools, the Map Viewer, Model Maker, Evidence Viewer, Clusters of Orthologous Groups (COGs) database, Retroviral Genotyping Tools, SARS Coronavirus Resource, SAGEmap, Gene Expression Omnibus (GEO), Online Mendelian Inheritance in Man (OMIM), the Molecular Modeling Database (MMDB), the Conserved Domain Database (CDD) and the Conserved Domain Architecture Retrieval Tool (CDART). Augmenting many of the web applications are custom implementations of the BLAST program optimized to search specialized data sets. All of the resources can be accessed through the NCBI home page at: http://www.ncbi.nlm.nih.gov.

9,604 citations

Journal ArticleDOI
TL;DR: H hierarchical and self-consistent orthology annotations are introduced for all interacting proteins, grouping the proteins into families at various levels of phylogenetic resolution in the STRING database.
Abstract: The many functional partnerships and interactions that occur between proteins are at the core of cellular processing and their systematic characterization helps to provide context in molecular systems biology. However, known and predicted interactions are scattered over multiple resources, and the available data exhibit notable differences in terms of quality and completeness. The STRING database (http://string-db.org) aims to provide a critical assessment and integration of protein-protein interactions, including direct (physical) as well as indirect (functional) associations. The new version 10.0 of STRING covers more than 2000 organisms, which has necessitated novel, scalable algorithms for transferring interaction information between organisms. For this purpose, we have introduced hierarchical and self-consistent orthology annotations for all interacting proteins, grouping the proteins into families at various levels of phylogenetic resolution. Further improvements in version 10.0 include a completely redesigned prediction pipeline for inferring protein-protein associations from co-expression data, an API interface for the R computing environment and improved statistical analysis for enrichment tests in user-provided networks.

8,224 citations

Journal ArticleDOI
TL;DR: Jalview 2 is a system for interactive WYSIWYG editing, analysis and annotation of multiple sequence alignments that employs web services for sequence alignment, secondary structure prediction and the retrieval of alignments, sequences, annotation and structures from public databases and any DAS 1.53 compliant sequence or annotation server.
Abstract: Summary: Jalview Version 2 is a system for interactive WYSIWYG editing, analysis and annotation of multiple sequence alignments. Core features include keyboard and mouse-based editing, multiple views and alignment overviews, and linked structure display with Jmol. Jalview 2 is available in two forms: a lightweight Java applet for use in web applications, and a powerful desktop application that employs web services for sequence alignment, secondary structure prediction and the retrieval of alignments, sequences, annotation and structures from public databases and any DAS 1.53 compliant sequence or annotation server. Availability: The Jalview 2 Desktop application and JalviewLite applet are made freely available under the GPL, and can be downloaded from www.jalview.org Contact: g.j.barton@dundee.ac.uk

7,926 citations