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Summary: We present a new database, GPSDB (Gene and Protein Synonyms DataBase) which collects gene/protein names, in a species specific way, from 14 main biological resources. A web-based search interface gives access to the database: given a gene/protein name, it retrieves all synonyms for this entity and queries Medline with a set of user-selected terms. Availability: GPSDB is freely available from http://biomint.oefai.at/ Contact: [email protected]

Summary: The SwissVar portal provides access to a comprehensive collection of single amino acid polymorphisms and diseases in the UniProtKB/Swiss-Prot database via a unique search engine. In particular, it gives direct access to the newly improved Swiss-Prot variant pages. The key strength of this portal is that it provides a possibility to query for similar diseases, as well as the underlying protein products and the molecular details of each variant. In the context of the recently proposed molecular view on diseases, the SwissVar portal should be in a unique position to provide valuable information for researchers and to advance research in this area. Availability: The SwissVar portal is available at www.expasy.org/swissvar Contact: anais.mottaz@isb-sib.ch; lina.yip@isb-sib.ch Supplementary information: Supplementary data are available at Bioinformatics online.

N-terminal myristoylation is a post-translational modification that causes the addition of a myristate to a glycine in the N-terminal end of the amino acid chain. This work presents neural network (NN) models that learn to discriminate myristoylated and nonmyristoylated proteins. Ensembles of 25 NNs and decision trees were trained on 390 positive sequences and 327 negative sequences. Experiments showed that NN ensembles were more accurate than decision tree ensembles. Our NN predictor evaluated by the leave-one-out procedure, obtained a false positive error rate equal to 2.1%. That was better than the PROSITE pattern for myristoylation for which the false positive error rate was 22.3%. On a recent version of Swiss-Prot (41.2), the NN ensemble predicted 876 myristoylated proteins, while 1150 proteins were predicted by the PROSITE pattern for myristoylation. Finally, compared to the well-known NMT predictor, the NN predictor gave similar results. Our tool is available under http://www.expasy.org/tools/myristoylator/myristoylator.html.

Motivation: Searching relevant publications for manual database annotation is a tedious task. In this paper, we apply a combination of Natural Language Processing (NLP) and probabilistic classification to re-rank documents returned by PubMed according to their relevance to Swiss-Prot annotation, and to identify significant terms in the documents. Results: With a Probabilistic Latent Categoriser (PLC) we obtained 69% recall and 59% precision for relevant documents in a representative query. As the PLC technique provides the relative contribution of each term to the final document score, we used the Kullback-Leibler symmetric divergence to determine the most discriminating words for Swiss-Prot medical annotation. This information should allow curators to understand classification results better. It also has great value for fine-tuning the linguistic pre-processing of documents, which in turn can improve the overall classifier performance. Availability: The medical annotation dataset is available from the authors upon request Contact: Pavel.Dobrokhotov@isb-sib.ch; Cyril.Goutte@xrce.xerox.com *To whom correspondence should be addressed.

SWISS-PROT is a protein sequence database, which aims to be nonredundant, fully annotated and highly cross-referenced. Most eukaryotic gene products undergo co- and/or post-translational modifications, and these need to be included in the database in order to describe the mature protein. SWISS-PROT includes information on many types of different protein modifications. As glycosylation is the most common type of post-translational protein modification, we are currently placing an emphasis on annotation of protein glycosylation in SWISS-PROT. Information on the position of the sugar within the polypeptide chain, the reducing terminal linkage as well as additional information on biological function of the sugar is included in the database. In this paper we describe how we account for the different types of protein glycosylation, namely N-linked glycosylation, O-linked glycosylation, proteoglycans, C-linked glycosylation and the attachment of glycosyl-phosphatidylinosital anchors to proteins.

Motivation: Post-translational modifications (PTMs) are important steps in the maturation of proteins. Several models exist to predict specific PTMs, from manually detected patterns to machine learning methods. On one hand, the manual detection of patterns does not provide the most efficient classifiers and requires an important workload, and on the other hand, models built by machine learning methods are hard to interpret and do not increase biological knowledge. Therefore, we developed a novel method based on patterns discovery and decision trees to predict PTMs. The proposed algorithm builds a decision tree, by coupling the C4.5 algorithm with genetic algorithms, producing high-performance white box classifiers. Our method was tested on the initiator methionine cleavage (IMC) and N α -terminal acetylation (N-Ac), two of the most common PTMs. Results: The resulting classifiers perform well when compared with existing models. On a set of eukaryotic proteins, they display a cross-validated Matthews correlation coefficient of 0.83 (IMC) and 0.65 (N-Ac). When used to predict potential substrates of N-terminal acetyltransferaseB and N-terminal acetyltransferaseC, our classifiers display better performance than the state of the art. Moreover, we present an analysis of the model predicting IMC for Homo sapiens proteins and demonstrate that we are able to extract experimentally known facts without prior knowledge. Those results validate the fact that our method produces white box models. Availability and implementation: Predictors for IMC and N-Ac and all datasets are freely available at http://terminus.unige.ch/ . Contact: jean-luc.falcone@unige.ch Supplementary information: Supplementary data are available at Bioinformatics online.

The annotation of protein post-translational modifications (PTMs) is an important task of UniProtKB curators and, with continuing improvements in experimental methodology, an ever greater number of articles are being published on this topic. To help curators cope with this growing body of information we have developed a system which extracts information from the scientific literature for the most frequently annotated PTMs in UniProtKB. The procedure uses a pattern-matching and rule-based approach to extract sentences with information on the type and site of modification. A ranked list of protein candidates for the modification is also provided. For PTM extraction, precision varies from 57% to 94%, and recall from 75% to 95%, according to the type of modification. The procedure was used to track new publications on PTMs and to recover potential supporting evidence for phosphorylation sites annotated based on the results of large scale proteomics experiments. The information retrieval and extraction method we have developed in this study forms the basis of a simple tool for the manual curation of protein post-translational modifications in UniProtKB/Swiss-Prot. Our work demonstrates that even simple text-mining tools can be effectively adapted for database curation tasks, providing that a thorough understanding of the working process and requirements are first obtained. This system can be accessed at http://eagl.unige.ch/PTM/ .

Background Although the UniProt KnowledgeBase is not a medical-oriented database, it contains information on more than 2,000 human proteins involved in pathologies. However, these annotations are not standardized, which impairs the interoperability between biological and clinical resources. In order to make these data easily accessible to clinical researchers, we have developed a procedure to link diseases described in the UniProtKB/Swiss-Prot entries to the MeSH disease terminology. Results We mapped disease names extracted either from the UniProtKB/Swiss-Prot entry comment lines or from the corresponding OMIM entry to the MeSH. Different methods were assessed on a benchmark set of 200 disease names manually mapped to MeSH terms. The performance of the retained procedure in term of precision and recall was 86% and 64% respectively. Using the same procedure, more than 3,000 disease names in Swiss-Prot were mapped to MeSH with comparable efficiency. Conclusions This study is a first attempt to link proteins in UniProtKB to the medical resources. The indexing we provided will help clinicians and researchers navigate from diseases to genes and from genes to diseases in an efficient way. The mapping is available at: http://research.isb-sib.ch/unimed.

The UniProt/Swiss-Prot Knowledgebase records about 30,500 variants in 5,664 proteins (Release 52.2). Most of these variants are manually curated single amino acid polymorphisms (SAPs) with references to the literature. In order to keep the list of published documents related to SAPs up to date, an automatic information retrieval method is developed to recover texts mentioning SAPs. The method is based on the use of regular expressions (patterns) and rules for the detection and validation of mutations. When evaluated using a corpus of 9,820 PubMed references, the precision of the retrieval was determined to be 89.5% over all variants. It was also found that the use of nonstandard mutation nomenclature and sequence positional correction is necessary to retrieve a significant number of relevant articles. The method was applied to the 5,664 proteins with variants. This was performed by first submitting a PubMed query to retrieve articles using gene or protein names and a list of mutation-related keywords; the SAP detection procedure was then used to recover relevant documents. The method was found to be efficient in retrieving new references on known polymorphisms. New references on known SAPs will be rendered accessible to the public via the Swiss-Prot variant pages.

The complete set of available ribosomal proteins was utilized, at both the peptidic and the nucleotidic level, to establish that plants and metazoans form two sister clades relative to fungi. Different phylogenetic inference methods are applied to the sequence data, using archeans as the outgroup. The evolutionary length of the internal branch within the eukaryotic crown trichotomy is demonstrated to be, at most, one-tenth of the evolutionary length of the branch leading to the cenancester of these three kingdoms.

Background This paper describes and evaluates a sentence selection engine that extracts a GeneRiF (Gene Reference into Functions) as defined in ENTREZ-Gene based on a MEDLINE record. Inputs for this task include both a gene and a pointer to a MEDLINE reference. In the suggested approach we merge two independent sentence extraction strategies. The first proposed strategy (LASt) uses argumentative features, inspired by discourse-analysis models. The second extraction scheme (GOEx) uses an automatic text categorizer to estimate the density of Gene Ontology categories in every sentence; thus providing a full ranking of all possible candidate GeneRiFs. A combination of the two approaches is proposed, which also aims at reducing the size of the selected segment by filtering out non-content bearing rhetorical phrases. Results Based on the TREC-2003 Genomics collection for GeneRiF identification, the LASt extraction strategy is already competitive (52.78%). When used in a combined approach, the extraction task clearly shows improvement, achieving a Dice score of over 57% (+10%). Conclusions Argumentative representation levels and conceptual density estimation using Gene Ontology contents appear complementary for functional annotation in proteomics.