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Featured researches published by Sarah Hunter.


Nucleic Acids Research | 2009

InterPro: the integrative protein signature database

Sarah Hunter; Rolf Apweiler; Teresa K. Attwood; Amos Marc Bairoch; Alex Bateman; David Binns; Peer Bork; Ujjwal Das; Louise Daugherty; Lauranne Duquenne; Robert D. Finn; Julian Gough; Daniel H. Haft; Nicolas Hulo; Daniel Kahn; Elizabeth Kelly; Aurélie Laugraud; Ivica Letunic; David M. Lonsdale; Rodrigo Lopez; John Maslen; Craig McAnulla; Jennifer McDowall; Jaina Mistry; Alex L. Mitchell; Nicola Mulder; Darren A. Natale; Christine A. Orengo; Antony F. Quinn; Jeremy D. Selengut

The InterPro database (http://www.ebi.ac.uk/interpro/) integrates together predictive models or ‘signatures’ representing protein domains, families and functional sites from multiple, diverse source databases: Gene3D, PANTHER, Pfam, PIRSF, PRINTS, ProDom, PROSITE, SMART, SUPERFAMILY and TIGRFAMs. Integration is performed manually and approximately half of the total ∼58 000 signatures available in the source databases belong to an InterPro entry. Recently, we have started to also display the remaining un-integrated signatures via our web interface. Other developments include the provision of non-signature data, such as structural data, in new XML files on our FTP site, as well as the inclusion of matchless UniProtKB proteins in the existing match XML files. The web interface has been extended and now links out to the ADAN predicted protein–protein interaction database and the SPICE and Dasty viewers. The latest public release (v18.0) covers 79.8% of UniProtKB (v14.1) and consists of 16 549 entries. InterPro data may be accessed either via the web address above, via web services, by downloading files by anonymous FTP or by using the InterProScan search software (http://www.ebi.ac.uk/Tools/InterProScan/).


Bioinformatics | 2014

InterProScan 5: genome-scale protein function classification

Philip Jones; David Binns; Hsin-Yu Chang; Matthew Fraser; Weizhong Li; Craig McAnulla; Hamish McWilliam; John Maslen; Alex L. Mitchell; Gift Nuka; Sebastien Pesseat; Antony F. Quinn; Amaia Sangrador-Vegas; Maxim Scheremetjew; Siew-Yit Yong; Rodrigo Lopez; Sarah Hunter

Motivation: Robust large-scale sequence analysis is a major challenge in modern genomic science, where biologists are frequently trying to characterize many millions of sequences. Here, we describe a new Java-based architecture for the widely used protein function prediction software package InterProScan. Developments include improvements and additions to the outputs of the software and the complete reimplementation of the software framework, resulting in a flexible and stable system that is able to use both multiprocessor machines and/or conventional clusters to achieve scalable distributed data analysis. InterProScan is freely available for download from the EMBl-EBI FTP site and the open source code is hosted at Google Code. Availability and implementation: InterProScan is distributed via FTP at ftp://ftp.ebi.ac.uk/pub/software/unix/iprscan/5/ and the source code is available from http://code.google.com/p/interproscan/. Contact: http://www.ebi.ac.uk/support or [email protected] or [email protected]


Nucleic Acids Research | 2012

InterPro in 2011: new developments in the family and domain prediction database

Sarah Hunter; P. D. Jones; Alex L. Mitchell; Rolf Apweiler; Teresa K. Attwood; Alex Bateman; Thomas Bernard; David Binns; Peer Bork; Sarah W. Burge; Edouard de Castro; Penny Coggill; Matthew Corbett; Ujjwal Das; Louise Daugherty; Lauranne Duquenne; Robert D. Finn; Matthew Fraser; Julian Gough; Daniel H. Haft; Nicolas Hulo; Daniel Kahn; Elizabeth Kelly; Ivica Letunic; David M. Lonsdale; Rodrigo Lopez; John Maslen; Craig McAnulla; Jennifer McDowall; Conor McMenamin

InterPro (http://www.ebi.ac.uk/interpro/) is a database that integrates diverse information about protein families, domains and functional sites, and makes it freely available to the public via Web-based interfaces and services. Central to the database are diagnostic models, known as signatures, against which protein sequences can be searched to determine their potential function. InterPro has utility in the large-scale analysis of whole genomes and meta-genomes, as well as in characterizing individual protein sequences. Herein we give an overview of new developments in the database and its associated software since 2009, including updates to database content, curation processes and Web and programmatic interfaces.


Nucleic Acids Research | 2015

The InterPro protein families database: the classification resource after 15 years

Alex L. Mitchell; Hsin-Yu Chang; Louise Daugherty; Matthew Fraser; Sarah Hunter; Rodrigo Lopez; Craig McAnulla; Conor McMenamin; Gift Nuka; Sebastien Pesseat; Amaia Sangrador-Vegas; Maxim Scheremetjew; Claudia Rato; Siew-Yit Yong; Alex Bateman; Marco Punta; Teresa K. Attwood; Christian J. A. Sigrist; Nicole Redaschi; Catherine Rivoire; Ioannis Xenarios; Daniel Kahn; Dominique Guyot; Peer Bork; Ivica Letunic; Julian Gough; Matt E. Oates; Daniel H. Haft; Hongzhan Huang; Darren A. Natale

The InterPro database (http://www.ebi.ac.uk/interpro/) is a freely available resource that can be used to classify sequences into protein families and to predict the presence of important domains and sites. Central to the InterPro database are predictive models, known as signatures, from a range of different protein family databases that have different biological focuses and use different methodological approaches to classify protein families and domains. InterPro integrates these signatures, capitalizing on the respective strengths of the individual databases, to produce a powerful protein classification resource. Here, we report on the status of InterPro as it enters its 15th year of operation, and give an overview of new developments with the database and its associated Web interfaces and software. In particular, the new domain architecture search tool is described and the process of mapping of Gene Ontology terms to InterPro is outlined. We also discuss the challenges faced by the resource given the explosive growth in sequence data in recent years. InterPro (version 48.0) contains 36 766 member database signatures integrated into 26 238 InterPro entries, an increase of over 3993 entries (5081 signatures), since 2012.


Nucleic Acids Research | 2007

New developments in the InterPro database

Nicola Mulder; Rolf Apweiler; Teresa K. Attwood; Amos Marc Bairoch; Alex Bateman; David Binns; Peer Bork; Virginie Buillard; Lorenzo Cerutti; Richard R. Copley; Emmanuel Courcelle; Ujjwal Das; Louise Daugherty; Mark Dibley; Robert D. Finn; Wolfgang Fleischmann; Julian Gough; Daniel H. Haft; Nicolas Hulo; Sarah Hunter; Daniel Kahn; Alexander Kanapin; Anish Kejariwal; Alberto Labarga; Petra S. Langendijk-Genevaux; David M. Lonsdale; Rodrigo Lopez; Ivica Letunic; John Maslen; Craig McAnulla

InterPro is an integrated resource for protein families, domains and functional sites, which integrates the following protein signature databases: PROSITE, PRINTS, ProDom, Pfam, SMART, TIGRFAMs, PIRSF, SUPERFAMILY, Gene3D and PANTHER. The latter two new member databases have been integrated since the last publication in this journal. There have been several new developments in InterPro, including an additional reading field, new database links, extensions to the web interface and additional match XML files. InterPro has always provided matches to UniProtKB proteins on the website and in the match XML file on the FTP site. Additional matches to proteins in UniParc (UniProt archive) are now available for download in the new match XML files only. The latest InterPro release (13.0) contains more than 13 000 entries, covering over 78% of all proteins in UniProtKB. The database is available for text- and sequence-based searches via a webserver (), and for download by anonymous FTP (). The InterProScan search tool is now also available via a web service at .


Nucleic Acids Research | 2014

EBI metagenomics—a new resource for the analysis and archiving of metagenomic data

Sarah Hunter; Matthew Corbett; Hubert Denise; Matthew Fraser; Alejandra Gonzalez-Beltran; Chris Hunter; Philip Jones; Rasko Leinonen; Craig McAnulla; Eamonn Maguire; John Maslen; Alex L. Mitchell; Gift Nuka; Arnaud Oisel; Sebastien Pesseat; Rajesh Radhakrishnan; Philippe Rocca-Serra; Maxim Scheremetjew; Peter Sterk; Daniel Vaughan; Guy Cochrane; Dawn Field; Susanna-Assunta Sansone

Metagenomics is a relatively recently established but rapidly expanding field that uses high-throughput next-generation sequencing technologies to characterize the microbial communities inhabiting different ecosystems (including oceans, lakes, soil, tundra, plants and body sites). Metagenomics brings with it a number of challenges, including the management, analysis, storage and sharing of data. In response to these challenges, we have developed a new metagenomics resource (http://www.ebi.ac.uk/metagenomics/) that allows users to easily submit raw nucleotide reads for functional and taxonomic analysis by a state-of-the-art pipeline, and have them automatically stored (together with descriptive, standards-compliant metadata) in the European Nucleotide Archive.


Database | 2012

Manual GO annotation of predictive protein signatures: the InterPro approach to GO curation

Sarah W. Burge; Elizabeth Kelly; David M. Lonsdale; Prudence Mutowo-Muellenet; Craig McAnulla; Alex L. Mitchell; Amaia Sangrador-Vegas; Siew-Yit Yong; Nicola Mulder; Sarah Hunter

InterPro amalgamates predictive protein signatures from a number of well-known partner databases into a single resource. To aid with interpretation of results, InterPro entries are manually annotated with terms from the Gene Ontology (GO). The InterPro2GO mappings are comprised of the cross-references between these two resources and are the largest source of GO annotation predictions for proteins. Here, we describe the protocol by which InterPro curators integrate GO terms into the InterPro database. We discuss the unique challenges involved in integrating specific GO terms with entries that may describe a diverse set of proteins, and we illustrate, with examples, how InterPro hierarchies reflect GO terms of increasing specificity. We describe a revised protocol for GO mapping that enables us to assign GO terms to domains based on the function of the individual domain, rather than the function of the families in which the domain is found. We also discuss how taxonomic constraints are dealt with and those cases where we are unable to add any appropriate GO terms. Expert manual annotation of InterPro entries with GO terms enables users to infer function, process or subcellular information for uncharacterized sequences based on sequence matches to predictive models. Database URL: http://www.ebi.ac.uk/interpro. The complete InterPro2GO mappings are available at: ftp://ftp.ebi.ac.uk/pub/databases/GO/goa/external2go/interpro2go


Methods of Molecular Biology | 2011

InterPro Protein Classification

Jennifer McDowall; Sarah Hunter

Improvements in nucleotide sequencing technology have resulted in an ever increasing number of nucleotide and protein sequences being deposited in databases. Unfortunately, the ability to manually classify and annotate these sequences cannot keep pace with their rapid generation, resulting in an increased bias toward unannotated sequence. Automatic annotation tools can help redress the balance. There are a number of different groups working to produce protein signatures that describe protein families, functional domains or conserved sites within related groups of proteins. Protein signature databases include CATH-Gene3D, HAMAP, PANTHER, Pfam, PIRSF, PRINTS, ProDom, PROSITE, SMART, SUPERFAMILY, and TIGRFAMs. Their approaches range from characterising small conserved motifs that can identify members of a family or subfamily, to the use of hidden Markov models that describe the conservation of residues over entire domains or whole proteins. To increase their value as protein classification tools, protein signatures from these 11 databases have been combined into one, powerful annotation tool: the InterPro database (http://www.ebi.ac.uk/interpro/) (Hunter et al., Nucleic Acids Res 37:D211-D215, 2009). InterPro is an open-source protein resource used for the automatic annotation of proteins, and is scalable to the analysis of entire new genomes through the use of a downloadable version of InterProScan, which can be incorporated into an existing local pipeline. InterPro provides structural information from PDB (Kouranov et al., Nucleic Acids Res 34:D302-D305, 2006), its classification in CATH (Cuff et al., Nucleic Acids Res 37:D310-D314, 2009) and SCOP (Andreeva et al., Nucleic Acids Res 36:D419-D425, 2008), as well as homology models from ModBase (Pieper et al., Nucleic Acids Res 37:D347-D354, 2009) and SwissModel (Kiefer et al., Nucleic Acids Res 37:D387-D392, 2009), allowing a direct comparison of the protein signatures with the available structural information. This chapter reviews the signature methods found in the InterPro database, and provides an overview of the InterPro resource itself.


Nucleic Acids Research | 2012

Genome3D: a UK collaborative project to annotate genomic sequences with predicted 3D structures based on SCOP and CATH domains.

Tony E. Lewis; Ian Sillitoe; Antonina Andreeva; Tom L. Blundell; Daniel W. A. Buchan; Cyrus Chothia; Alison L. Cuff; Jose M. Dana; Ioannis Filippis; Julian Gough; Sarah Hunter; David Jones; Lawrence A. Kelley; Gerard J. Kleywegt; Federico Minneci; Alex L. Mitchell; Alexey G. Murzin; Bernardo Ochoa-Montaño; Owen J. L. Rackham; James C. Smith; Michael J. E. Sternberg; Sameer Velankar; Corin Yeats; Christine A. Orengo

Genome3D, available at http://www.genome3d.eu, is a new collaborative project that integrates UK-based structural resources to provide a unique perspective on sequence–structure–function relationships. Leading structure prediction resources (DomSerf, FUGUE, Gene3D, pDomTHREADER, Phyre and SUPERFAMILY) provide annotations for UniProt sequences to indicate the locations of structural domains (structural annotations) and their 3D structures (structural models). Structural annotations and 3D model predictions are currently available for three model genomes (Homo sapiens, E. coli and baker’s yeast), and the project will extend to other genomes in the near future. As these resources exploit different strategies for predicting structures, the main aim of Genome3D is to enable comparisons between all the resources so that biologists can see where predictions agree and are therefore more trusted. Furthermore, as these methods differ in whether they build their predictions using CATH or SCOP, Genome3D also contains the first official mapping between these two databases. This has identified pairs of similar superfamilies from the two resources at various degrees of consensus (532 bronze pairs, 527 silver pairs and 370 gold pairs).


Standards in Genomic Sciences | 2010

Meeting Report: BioSharing at ISMB 2010

Dawn Field; Susanna Sansone; Edward F. DeLong; Peter Sterk; Iddo Friedberg; Pascale Gaudet; Susanna Lewis; Renzo Kottmann; Lynette Hirschman; George M Garrity; Guy Cochrane; John Wooley; Folker Meyer; Sarah Hunter; Owen White; Brian Bramlett; Susan K. Gregurick; Hilmar Lapp; Sandra Orchard; Philippe Rocca-Serra; Alan Ruttenberg; Nigam H. Shah; Chris F. Taylor; Anne E. Thessen

This report summarizes the proceedings of the one day BioSharing meeting held at the Intelligent Systems for Molecular Biology (ISMB) 2010 conference in Boston, MA, USA This inaugural BioSharing event was hosted by the Genomic Standards Consortium as part of its M3 & BioSharing special interest group (SIG) workshop. The BioSharing event included invited talks from a range of community leaders and a panel discussion at the end of the day. The panel session led to the formal agreement among community leaders to join together to promote cross-community knowledge exchange and collaborations. A key focus of the newly formed Biosharing community will be linking up resources to promote real-world data sharing (virtuous cycle of data) and supporting compliance with data policies through the creation of a one-stop-portal of information. Further information about the newly established BioSharing effort can be found at http://biosharing.org.

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Alex L. Mitchell

European Bioinformatics Institute

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Craig McAnulla

European Bioinformatics Institute

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Matthew Fraser

European Bioinformatics Institute

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Maxim Scheremetjew

European Bioinformatics Institute

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Sebastien Pesseat

European Bioinformatics Institute

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David Binns

European Bioinformatics Institute

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David M. Lonsdale

European Bioinformatics Institute

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John Maslen

European Bioinformatics Institute

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Rodrigo Lopez

European Bioinformatics Institute

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