EuroPhenome and EMPReSS: online mouse phenotyping resource

EuroPhenome (http://www.europhenome.org) and EMPReSS (http://empress.har.mrc.ac.uk/) form an integrated resource to provide access to data and procedures for mouse phenotyping. EMPReSS describes 96 Standard Operating Procedures for mouse phenotyping. EuroPhenome contains data resulting from carrying out EMPReSS protocols on four inbred laboratory mouse strains. As well as web interfaces, both resources support web services to enable integration with other mouse phenotyping and functional genetics resources, and are committed to initiatives to improve integration of mouse phenotype databases. EuroPhenome will be the repository for a recently initiated effort to carry out large-scale phenotyping on a large number of knockout mouse lines (EUMODIC)

Ontology-based cross-species integration and analysis of Saccharomyces cerevisiae phenotypes

Ontologies are widely used in the biomedical community for annotation and integration of databases. Formal definitions can relate classes from different ontologies and thereby integrate data across different levels of granularity, domains and species. We have applied this methodology to the Ascomycete Phenotype Ontology (APO), enabling the reuse of various orthogonal ontologies and we have converted the phenotype associated data found in the SGD following our proposed patterns. We have integrated the resulting data in the cross-species phenotype network PhenomeNET, and we make both the cross-species integration of yeast phenotypes and a similarity-based comparison of yeast phenotypes across species available in the PhenomeBrowser. Furthermore, we utilize our definitions and the yeast phenotype annotations to suggest novel functional annotations of gene products in yeast

Three Ontologies to Define Phenotype Measurement Data

Background: There is an increasing need to integrate phenotype measurement data across studies for both human studies and those involving model organisms. Current practices allow researchers to access only those data involved in a single experiment or multiple experiments utilizing the same protocol. Results: Three ontologies were created: Clinical Measurement Ontology, Measurement Method Ontology and Experimental Condition Ontology. These ontologies provided the framework for integration of rat phenotype data from multiple studies into a single resource as well as facilitated data integration from multiple human epidemiological studies into a centralized repository. Conclusion: An ontology based framework for phenotype measurement data affords the ability to successfully integrate vital phenotype data into critical resources, regardless of underlying technological structures allowing the user to easily query and retrieve data from multiple studies

Obol: Integrating Language and Meaning in Bio-Ontologies

Ontologies are intended to capture and formalize a domain of knowledge. The ontologies comprising the Open Biological Ontologies (OBO) project, which includes the Gene Ontology (GO), are formalizations of various domains of biological knowledge. Ontologies within OBO typically lack computable definitions that serve to differentiate a term from other similar terms. The computer is unable to determine the meaning of a term, which presents problems for tools such as automated reasoners. Reasoners can be of enormous benefit in managing a complex ontology. OBO term names frequently implicitly encode the kind of definitions that can be used by computational tools, such as automated reasoners. The definitions encoded in the names are not easily amenable to computation, because the names are ostensibly natural language phrases designed for human users. These names are highly regular in their grammar, and can thus be treated as valid sentences in some formal or computable language.With a description of the rules underlying this formal language, term names can be parsed to derive computable definitions, which can then be reasoned over. This paper describes the effort to elucidate that language, called Obol, and the attempts to reason over the resulting definitions. The current implementation finds unique non-trivial definitions for around half of the terms in the GO, and has been used to find 223 missing relationships, which have since been added to the ontology. Obol has utility as an ontology maintenance tool, and as a means of generating computable definitions for a whole ontology

Modellierung phänotypischer Beschreibungen auf der Grundlage von Bio-Ontologien

Von besonderem Interesse bei dieser Arbeit ist die Ontologie PATO (für entsprechendes OBO-Dokument siehe [Gko11]). Es handelt sich dabei um eine Ontologie von Qualitäten. Dementsprechend sind dort Konzepte wie concentration of (PATO:0000033) oder color (PATO:0000014) definiert. Diese Qualitäten können in Verbindung mit Konzepten spezies-spezifischer Ontologien (z. B. ChEBI oder MA) zur Beschreibung von Phänotypen genutzt werden. Hierbei treten jedoch einige Probleme auf, die im weiteren Verlauf der Arbeit aufgezeigt und gelöst werden

Open biomedical pluralism : formalising knowledge about breast cancer phenotypes

We demonstrate a heterogeneity of representation types for breast cancer phenotypes and stress that the characterisation of a tumour phenotype often includes parameters that go beyond the representation of a corresponding empirically observed tumour, thus reflecting significant functional features of the phenotypes as well as epistemic interests that drive the modes of representation. Accordingly, the represented features of cancer phenotypes function as epistemic vehicles aiding various classifications, explanations, and predictions. In order to clarify how the plurality of epistemic motivations can be integrated on a formal level, we give a distinction between six categories of human agents as individuals and groups focused around particular epistemic interests. We analyse the corresponding impact of these groups and individuals on representation types, mapping and reasoning scenarios. Respecting the plurality of representations, related formalisms, expressivities and aims, as they are found across diverse scientific communities, we argue for a pluralistic ontology integration. Moreover, we discuss and illustrate to what extent such a pluralistic integration is supported by the distributed ontology language DOL, a meta-language for heterogeneous ontology representation that is currently under standardisation as ISO WD 17347 within the OntoIOp (Ontology Integration and Interoperability) activity of ISO/TC 37/SC 3. We particularly illustrate how DOL supports representations of parthood on various levels of logical expressivity, mapping of terms, merging of ontologies, as well as non-monotonic extensions based on circumscription allowing a transparent formal modelling of the normal/abnormal distinction in phenotypes

Mass spectrometry in clinical protein biomarker discovery

The field of biological sciences has expanded enormously within the last few decades. Developments in techniques and instrumentation have allowed biologist to explore biological mechanisms in an unprecedented detail. One of the most evolved disciplines is the field of proteomics. In general, proteins function in many different biological roles. They serve as structural molecules, in signaling routes mediating information in the cell, in intra- and extracellular transport and trafficking as well as in numerous other cellular functions. The area of protein research entails the study of all things relating to proteins and their functions. These include cellular protein composition, expression changes, protein structure, post-translational modifications and protein-protein interactions. Mass spectrometry (MS) has become one of the key technologies in proteomic research. The relative ease of sample handling and automated MS machinery has made proteomic analysis relatively straightforward. Mass spectrometers work by measuring the weight of intact proteins or protein-derived peptides. Proteomic MS identification is usually done by fragmenting the proteins or peptides in the mass spectrometer and using the resulting mass spectral information in identification of peptide sequence. There are two main strategies of peptide sequence identification: database dependent and de novo identification. Database dependent algorithms utilize known sequence information stored in databases to decipher the peptide amino acid sequence of the MS-observed spectra and use that information to predict the protein from which the peptide is derived from. On the other hand de novo methods try to construct the peptide sequence solely based on the fragmentation patterns of the peptide. The completeness of sequence databases of many species and the speed and efficiency of the search engines have made the database dependent search as the main method in peptide and protein identification. The modern high resolution mass spectrometers along with ultra-performance liquid chromatography have enabled the detection of thousands of protein in one single MS run. This, together with advances in MS-based protein quantification has extended the use of mass spectrometers in discovery type biomarker search. Mass spectrometers are able to produce a large amount of data on numerous proteins that can be used to detect and quantify differences in patient and control samples. This in turn can be used as starting point for more focused validation studies on the acquired data and ultimately lead to useful clinical biomarkers. The focus of this study was to utilize and learn mass spectrometric methodologies and to analyze different proteomic processes in sample types. We analyzed the protein-protein interactions in Baker´s yeast PSA1 protein in various points of batch cultivation using database dependent and de novo protein identification methods. We showed that the interactome of PSA1 is very dynamic depending on the phase of the cultivation. We also showed the limitations and benefits of de novo identification and the combined use of both search strategies in improving the confidence of the identifications. In another study using affinity purification and mass spectrometry we identified Fibrillin-2 as the binding partner of lung cancer associated Gremlin-1 protein. This finding elucidates functions and mechanisms of Gremlin-1 and Fibrillin-2 in malignant tissues. In two mass spectrometry-based protein quantification studies we characterized the protein concentration changes in human plasma during liver transplantation surgery as well as the effect of excess sialic acid production in HEK293 model cell line. In the liver transplantation plasma project we identified protein concentration changes in liver in response to the trauma caused by the surgery using label-based iTRAQ method. We showed consumption and secretion of several coagulation related proteins within the liver suggesting activation of coagulation cascade in the very early phases of the craft reperfusion. In the study of excess sialic acid production we first verified the amounts of sialic acid using mass spectrometry-based multiple reaction monitoring method. We were able to induce the production of sialic acid to almost 70-fold compared to control cells. We also monitored the protein abundance changes in sialic acid producing cells using label free proteins quantification method identifying 105 changed proteins. We analyzed those proteins with several functional enrichment tools revealing modifications in cellular protein transport, metabolic and signaling pathways and in remodeling of cellular adherens junctions. Such large scale MS-analyses using ontology-based tools can significantly aid in deciphering the effect of perturbations to complex systems but also reveal novel functional targets for biomarker discovery. The results obtained from targeted interaction experiments as well as large scale quantification studies can be used as basis for more rigorous investigations on the various subjects in search for potential biomarkers for clinical use. The techniques and methods used in the studies also demonstrate the many uses of mass spectrometric techniques in several fields of proteomic and biological research.Kaikkien elollisten olentojen elämä perustuu deoksiribonukleiinihappoon eli DNA:han. Se säilöö ja järjestää geenien sisältämän tiedon, joka käännetään ribonukleiinihappoketjuksi eli RNA:ksi. RNA:n sisältämä koodisto puolestaan toimii mallina proteiinien rakennuksessa. Proteiinit taas toimivat kaikissa solun vaatimissa fyysisissä toiminoissa. Ne säätelevät geenien toimintaa, osallistuvat kaikkiin solun vaatimiin rakenteisiin, ne toimivat solunsisäisinä kuljetusmolekyyleinä, välittävät viestejä solussa ja sen ulkopuolella, ne rakentavat uusia ja tuhoavat vanhoja proteiineja sekä toimivat tehtaina jotka tuottavat solulle energiaa ja muita molekyylejä solun toimintoja varten. Proteiinien toimintaa puolestaan säätelee useat tekijät kuten muiden molekyylien liittäminen proteiiniin, proteiinien keskinäiset vuorovaikutukset, sekä itse proteiinien sisäiset rakenteet ja niiden muutokset. Proteiinien toimintaa, rakennetta ja niiden vuorovaikutuksien tutkimista kutsutaan proteomiikaksi. Proteominen tutkimus käsittää useita eri tekniikoita proteiinien tunnistamiseen ja määrälliseen selvittämiseen. Viime vuosina massaspektrometria on kuitenkin muodostunut yhdeksi tehokkaimmista ja tarkimmista proteomisen tutkimuksen menetelmäksi. Massaspektrometriassa tutkitaan proteiinien ja niiden hajotuksessa syntyvien peptidipalasten painoa, joiden perusteella voidaan päätellä palasten aminohappojärjestys ja tätä kautta myös voidaan tunnistaa itse proteiini mistä peptidi on peräisin. Nykyisellä massaspektrometritekniikalla voidaan tunnistaa tuhansia proteiineja hyvinkin pienestä näytteestä. Nykyiset tekniikat mahdollistavat myös proteiinimäärien selvittämisen massaspektrometrisin keinoin. Käyttäen hyväksi proteiinien tunnistusta sekä niiden määrällistä selvittämistä massaspektrometrillä, on mahdollista selvitellä proteiinien muutoksia ja vaikutuksia erilaisissa sairauksissa ja tätä kautta löytää diagnostisia merkkiproteiineja tai mahdollisia lääkkeiden vakuutuskohteita sairauksien hoitoon ja parantamiseen. Tässä työssä käytimme massaspektrometrisia tunnistus- ja määritysmetodeja useissa biologisissa tilanteissa. Ensimmäisessä osajulkaisussa käytimme malliorganismina leivinhiivaa, jonka avulla tutkimme hiivasolun energia- ja sokeriaineenvaihduntaan osallistuva PSA1-proteiinin vuorovaikutuksia muiden proteiinien kanssa. Osoitimme että vuorovaikutukset ovat hyvin vaihtelevia riippuen solun ravintotilanteesta. Samassa tutkimuksessa selvittelimme myös kahden eri massaspektrometrisen tunnistusmenetelmän eroja. Toisessa osajulkaisussa käytimme massaspektrometriä selvittämään proteiinimäärien muutoksia maksatransplantaation aikana. Osoitimme, että useat veren hyytymiseen proteiinit vähentyvät maksassa heti verenkierron kytkemisen jälkeen, kun taas toisten määrä lisääntyy. Kolmannessa osajulkaisussa tutkimme millaisia muutoksia ylimääräinen sialihapon tuotto solussa aiheuttaa solunsisäisiin proteiinimääriin. Osoitimme että useat solunsisäiseen kuljetukseen, eri metaboliareitteihin ja solu-soluliitoksiin osallistuvien proteiinien määrä muuttui vasteena sialihapon tuoton lisäykseen. Neljännessä osajulkaisussa selvitimme keuhkosyöpään liitetyn GREM1- proteiinin vuorovaikutuksia muiden proteiinien kanssa. Käyttäen hyväksi kahta eri massaspektrometristä tunnistusmenetelmää osoitimme että GREM1 sitoutuu Fibrillin-2- proteiiniin. Tätä tietoa proteiinisitoutumisesta voidaan käyttää etsinnässä lääkkeitä keuhkosyövän hoitoon. Tässä väitöskirjatyössä ja osoitimme massaspektrometrin soveltuvuuden ja hyödyn erilaisten biologisten ongelmien ja tilanteiden tutkimisessa. Näytimme että laajamittainen massaspektrometrinen proteiinitasojen määritys voi tuoda huomattavasti uutta tietoa solun ja organismien solunkattavasta vasteesta stressiin ja erilaisiin biologisiin häiriötilanteisiin. Lisäksi osoitimme että yksittäisten proteiinien massaspektrometrinen tutkiminen voi myös laajentaa tietämystä proteiinin ja mahdollisesti myös siihen liitetyn taudin toiminnasta ja mekanismeista

Building mouse phenotype ontologies

The structured description of mutant phenotypes presents a major conceptual and practical problem. A general model for generating mouse phenotype ontologies that involves combing a variety of different ontologies to better link and describe phenotypes is presented. This model is based on the Phenotype and Trait Ontology schema proposal and incorporates practical limitations and designing solutions in an attempt to model a testbed for the first phenotype ontology constructed in this manner, namely the mouse behavior phenotype ontology. We propose the application of such a model could provide curators with a powerful mechanism of annotation, mining and knowledge representation as well as achieving some level of free text disassociation.