Motifs tree: a new method for predicting post-translational modifications

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 Nα-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: [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin

Combining NLP and probabilistic categorisation fordocument and term selection for Swiss-Prot medical annotation

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: [email protected]; [email protected] * To whom correspondence should be addresse

Text mining for Swiss-Prot curation: A story of success and failure

A text mining group has been set up at the Swiss Institute of Bioinformatics, with objective to develop and adapt information retrieval and extraction tools to help Swiss-Prot curators in their daily annotation work. After over 7 year activities, this group has gathered a significant amount of experience about the need in text mining for biocuration.

The first observation we made is that there is no “in-a-box” solution which can satisfy every needs. Each curator has his/her own strategy to find information from the literature and none of the existing information retrieval systems is able to compete with it, more for reason of habits than for reason of performance. Second observation: to be completely operative, an information retrieval system should be embedded in the annotation platform. For instance, it should be possible to copy/paste information, such as the article reference or some interesting sentences, directly in the database format. Most of the existing online programs are hardly adaptable for this task and their use usually results in additional editing efforts for the curators. From this observation, we can derive the fact that integrating text mining services is usually more costly than expected since wrappers and user interfaces need significant developments sometimes fairly user-specific.

After noticing these problems in the design and use of a generic information retrieval system for the Swiss-Prot curators, we focused our effort on text mining applications for database update. The follow-up of the literature is essential in the process of database maintenance and there are needs for automatic information extraction tools on a large panel of topics. We developed several IE applications in the field of:
-	PTM information (phosphorylation, glycosylation, disulfide bridge)
-	Subcellular localization
-	Variant/mutation detection and characterization
-	New sequence with enzymatic activities
-	New characterization of enzymes.
These tools are integrated into pipelines which follow PubMed daily outcomes and generate list of selected abstracts with highlights on the relevant sentences. These procedures are done independently of the usual annotation workflow, so that curators can mine these preselected data whenever they work on database entry updates.

To conclude, we have identified big challenges in text mining services after discussion with the curators. One of them is the detection of novel information, especially those related to a new function or a new characterization of a protein or one of its close homologues. We are currently working on this task in the framework of the collaborative project “EAGL”. Another challenge is definitely the large-scale screening of newly published full-text papers to complement the often incomplete information in abstracts. This becomes more and more indispensable, not really for the annotation of widely studied “hot” proteins, but to find new data on uncharacterized ones. For instance, when no gene name has been attributed to a sequence, the only way to retrieve information is to use the orf names, which are never provided in abstracts.

Finally, one should definitely stress that many of these information retrieval and extraction tasks could be greatly simplified with the requirement of metadata at the article submission time, such as an official HGNC gene name or a UniProt reference

Mapping proteins to disease terminologies: from UniProt to MeSH

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>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: <url>http://research.isb-sib.ch/unimed</url>.</p

Easy retrieval of single amino-acid polymorphisms and phenotype information using SwissVar

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: [email protected]; [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin

GPSDB: a new database for synonyms expansion of gene and protein names

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]

Gene Ontology density estimation and discourse analysis for automatic GeneRiF extraction

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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%).</p> <p>Conclusions</p> <p>Argumentative representation levels and conceptual density estimation using Gene Ontology contents appear complementary for functional annotation in proteomics.</p

Text mining for the biocuration workflow

Molecular biology has become heavily dependent on biological knowledge encoded in expert curated biological databases. As the volume of biological literature increases, biocurators need help in keeping up with the literature; (semi-) automated aids for biocuration would seem to be an ideal application for natural language processing and text mining. However, to date, there have been few documented successes for improving biocuration throughput using text mining. Our initial investigations took place for the workshop on ‘Text Mining for the BioCuration Workflow’ at the third International Biocuration Conference (Berlin, 2009). We interviewed biocurators to obtain workflows from eight biological databases. This initial study revealed high-level commonalities, including (i) selection of documents for curation; (ii) indexing of documents with biologically relevant entities (e.g. genes); and (iii) detailed curation of specific relations (e.g. interactions); however, the detailed workflows also showed many variabilities. Following the workshop, we conducted a survey of biocurators. The survey identified biocurator priorities, including the handling of full text indexed with biological entities and support for the identification and prioritization of documents for curation. It also indicated that two-thirds of the biocuration teams had experimented with text mining and almost half were using text mining at that time. Analysis of our interviews and survey provide a set of requirements for the integration of text mining into the biocuration workflow. These can guide the identification of common needs across curated databases and encourage joint experimentation involving biocurators, text mining developers and the larger biomedical research community

The SIB Swiss Institute of Bioinformatics' resources: focus on curated databases

The SIB Swiss Institute of Bioinformatics (www.isb-sib.ch) provides world-class bioinformatics databases, software tools, services and training to the international life science community in academia and industry. These solutions allow life scientists to turn the exponentially growing amount of data into knowledge. Here, we provide an overview of SIB's resources and competence areas, with a strong focus on curated databases and SIB's most popular and widely used resources. In particular, SIB's Bioinformatics resource portal ExPASy features over 150 resources, including UniProtKB/Swiss-Prot, ENZYME, PROSITE, neXtProt, STRING, UniCarbKB, SugarBindDB, SwissRegulon, EPD, arrayMap, Bgee, SWISS-MODEL Repository, OMA, OrthoDB and other databases, which are briefly described in this article