Infrastructure for the life sciences: design and implementation of the UniProt website

By E. Jain, Amos Bairoch, S. Duvaud, I. Phan, Nicole Redaschi, Baris E. Suzek, M. Martin, Peter B. McGarvey, Elisabeth Gasteiger. BMC Bioinformatics, 2009

471 citations recorded by the source corpus.

BackgroundThe UniProt consortium was formed in 2002 by groups from the Swiss Institute of Bioinformatics (SIB), the European Bioinformatics Institute (EBI) and the Protein Information Resource (PIR) at Georgetown University, and soon afterwards the website http://www.uniprot.org was set up as a central entry point to UniProt resources. Requests to this address were redirected to one of the three organisations' websites. While these sites shared a set of static pages with general information about UniProt, their pages for searching and viewing data were different. To provide users with a consistent view and to cut the cost of maintaining three separate sites, the consortium decided to develop a common website for UniProt. Following several years of intense development and a year of public beta testing, the http://www.uniprot.org domain was switched to the newly developed site described in this paper in July 2008.DescriptionThe UniProt consortium is the main provider of protein sequence and annotation data for much of the life sciences community. The http://www.uniprot.org website is the primary access point to this data and to documentation and basic tools for the data. These tools include full text and field-based text search, similarity search, multiple sequence alignment, batch retrieval and database identifier mapping. This paper discusses the design and implementation of the new website, which was released in July 2008, and shows how it improves data access for users with different levels of experience, as well as to machines for programmatic access.http://www.uniprot.org/ is open for both academic and commercial use. The site was built with open source tools and libraries. Feedback is very welcome and should be sent to help@uniprot.org.ConclusionThe new UniProt website makes accessing and understanding UniProt easier than ever. The two main lessons learned are that getting the basics right for such a data provider website has huge benefits, but is not trivial and easy to underestimate, and that there is no substitute for using empirical data throughout the development process to decide on what is and what is not working for your users.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/2b5c603906c553a33d10aa116810f6ad1ec949bc

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

Citations in the corpus

Protein–Protein Interactions Essentials: Key Concepts to Building and Analyzing Interactome Networks (2010, unknown): “The second network was built using APID2NET and Cytoscape, retrieving the proteins that interact with NOTCH1, NOTCH2, NOTCH3, or NOTCH4 (UniProt IDs: P46531, Q04721, Q9UM47, Q99466) in interactions demonstrated by at least two different experiments (Figure 3B).”
Update on activities at the Universal Protein Resource (UniProt) in 2013 (2012, background): “org website (18) is the primary access point to our data and documentation and offers tools, such as full text and field-based text search, sequence similarity search, multiple sequence alignment, batch retrieval and database identifier mapping.”
Molecular Systems Biology Peer Review Process File Indi: a Computational Framework for Inferring Drug Interactions and Their Associated Recommendations Transaction Report (unknown): “…of the drugs, the latter highlights suspected CYPs, allowing the consideration of 8 Molecular Systems Biology 2012 patient-specific genetic polymorphisms related to certain CYP enzymes (Bertilsson, 1995; Goshman et al, 1999) (e.g., patients who are CYP2D6-deficient (Ingelman-Sundberg, 2004)).”
PREDICT: a method for inferring novel drug indications with application to personalized medicine (2011, unknown): “Ashburner et al , 2000 ) were downloaded from UniProt”
Similarity-based machine learning methods for predicting drug-target interactions: a brief review (2014, background): “…such as KEGG GENES [9]; (2) protein– protein interaction (PPI) network-based similarity can be derived from the distance between two targets in the PPI network [46–50] and (3) Gene Ontology (GO) semantic similarity can be computed using GO annotations [51] in some databases such as UniProt [52].”
Reorganizing the protein space at the Universal Protein Resource (UniProt) (2011, unknown): “org website (8) is the primary access point to our data and documentation and to tools such as full text and field-based text search, sequence similarity search, multiple sequence alignment, batch retrieval and database identifier mapping.”
The UCSC Genome Browser database: update 2010 (2009, background): “The OMIM Genes track shows the locations of UCSC Genes that have associated OMIM identifiers mapped by RefSeq (4) and UniProt (28,29).”
ALSoD: A user‐friendly online bioinformatics tool for amyotrophic lateral sclerosis genetics (2012, background): “, ProtScale [Gasteiger et al., 2005], KEGG [Kanehisa et al., 2000], Uniprot [Jain et al., 2009], iHop [Hoffmann and Valencia, 2004], Pathway in KEGG[Kanehisaetal.,2000],GeneTest[Pagonetal.,2002],AmiGO [Carbonetal.,2009],Ensembl[Hubbardetal.,2009],NCBI[Sherry etal.,2001],LifeScienceDB(Japan)[Yoshidae”
Ongoing and future developments at the Universal Protein Resource (2010, background): “org website (21) is the primary access point to our data and documentation and to tools such as full-text and field-based-text search, sequence-similarity search, multiple sequence alignment, batch retrieval and database identifier mapping.”
The Universal Protein Resource (UniProt) in 2010 (2009, methodology): “The http://www.uniprot.org website [17] is the primary access point to our data and documentation and to tools such as full text and ﬁeld-based text search, sequence similarity search, multiple sequence alignment, batch retrieval and database identiﬁer mapping.”
Network-based methods for human disease gene prediction. (2011, methodology): “They obtained unweighted PPI networks from HPRD and the Online Predicted Human Interaction Database (OPHID), grouped diseases from the OMIM morbid-map file based on their names and extracted loci for the associated genes from UniProt [40, 41].”