Human protein function prediction: application of machine learning for integration of heterogeneous data sources

Experimental characterisation of protein cellular function can be prohibitively expensive and take years to complete. To address this problem, this thesis focuses on the development of computational approaches to predict function from sequence. For sequences with well characterised close relatives, annotation is trivial, orphans or distant homologues present a greater challenge. The use of a feature based method employing ensemble support vector machines to predict individual Gene Ontology classes is investigated. It is found that different combinations of feature inputs are required to recognise different functions. Although the approach is applicable to any human protein sequence, it is restricted to broadly descriptive functions. The method is well suited to prioritisation of candidate functions for novel proteins rather than to make highly accurate class assignments. Signatures of common function can be derived from different biological characteristics; interactions and binding events as well as expression behaviour. To investigate the hypothesis that common function can be derived from expression information, public domain human microarray datasets are assembled. The questions of how best to integrate these datasets and derive features that are useful in function prediction are addressed. Both co-expression and abundance information is represented between and within experiments and investigated for correlation with function. It is found that features derived from expression data serve as a weak but significant signal for recognising functions. This signal is stronger for biological processes than molecular function categories and independent of homology information. The protein domain has historically been coined as a modular evolutionary unit of protein function. The occurrence of domains that can be linked by ancestral fusion events serves as a signal for domain-domain interactions. To exploit this information for function prediction, novel domain architecture and fused architecture scores are developed. Architecture scores rather than single domain scores correlate more strongly with function, and both architecture and fusion scores correlate more strongly with molecular functions than biological processes. The final study details the development of a novel heterogeneous function prediction approach designed to target the annotation of both homologous and non-homologous proteins. Support vector regression is used to combine pair-wise sequence features with expression scores and domain architecture scores to rank protein pairs in terms of their functional similarities. The target of the regression models represents the continuum of protein function space empirically derived from the Gene Ontology molecular function and biological process graphs. The merit and performance of the approach is demonstrated using homologous and non-homologous test datasets and significantly improves upon classical nearest neighbour annotation transfer by sequence methods. The final model represents a method that achieves a compromise between high specificity and sensitivity for all human proteins regardless of their homology status. It is expected that this strategy will allow for more comprehensive and accurate annotations of the human proteome

Shaping Biological Knowledge: Applications in Proteomics

The central dogma of molecular biology has provided a meaningful principle for data integration in the field of genomics. In this context, integration reflects the known transitions from a chromosome to a protein sequence: transcription, intron splicing, exon assembly and translation. There is no such clear principle for integrating proteomics data, since the laws governing protein folding and interactivity are not quite understood. In our effort to bring together independent pieces of information relative to proteins in a biologically meaningful way, we assess the bias of bioinformatics resources and consequent approximations in the framework of small-scale studies. We analyse proteomics data while following both a data-driven (focus on proteins smaller than 10 kDa) and a hypothesis-driven (focus on whole bacterial proteomes) approach. These applications are potentially the source of specialized complements to classical biological ontologies

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

The SIB Swiss Institute of Bioinformatics 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

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

NETME: on-the-fly knowledge network construction from biomedical literature

Background: The rapidly increasing biological literature is a key resource to automatically extract and gain knowledge concerning biological elements and their relations. Knowledge Networks are helpful tools in the context of biological knowledge discovery and modeling. Results: We introduce a novel system called NETME, which, starting from a set of full-texts obtained from PubMed, through an easy-to-use web interface, interactively extracts biological elements from ontological databases and then synthesizes a network inferring relations among such elements. The results clearly show that our tool is capable of inferring comprehensive and reliable biological networks

Transcriptional landscape of neuronal and cancer stem cells

Tumor mass is composed by heterogeneous cell population including a subset of “cancer stem cells” (CSC). Oncogenic signals foster CSC by transforming tissue stem cells or by reprogramming progenitor/differentiated cells towards stemness. Thus, CSC share features with cancer and stem cells (e.g. self-renewal, hierarchical developmental program leading to differentiated cells, epithelial/mesenchimal transition) and these latter are maintained by the constitutive activation of stemness-promoting signals. CSC could trigger tumor formation, drive to resistance to conventional therapeutics and underlie patients’ relapse. Indeed, stem cell signatures have been associated with poor prognosis in various. This background makes the identification of CSC molecular features mandatory to highlight the survival inner working and to design novel CSC specific therapeutic strategies. Medulloblastoma (MB) is the most common childhood malignant brain tumor and a leading cause of cancerrelated morbidity and mortality. Current multimodal therapies are effective in about 50% of patients but often cause long-term side effects, i.e. developmental, neurological, neuroendocrine and psychosocial deficits (Northcott PA Nature Rev cancer 2012). For many years, MB treated as a single tumor entity despite the divergent tumor histology, patients’ outcome and drug sensitivity, and also by the diversity of the stem cell of origin. Very recently the scenario of human MB has dramatically changed since its heterogeneous biology has been addressed by high-throughput gene expression analysis (oligonucleotide microarrays) or by the powerful genomic next-generation sequencing. These led to the identification of four tumor subgroups (WNT, SHH, Group 3 and Group 4) uncovering the existence of a highly diverse mutational spectra and gene expression. However a quantitative approach has not yet been applied to the transcriptional landscape of Medulloblastoma stem cells (MbSC) through RNA Next Generation Sequencing (RNA-Seq) technology. This is a relevant issue, since RNA-Seq is able to interrogate the genome wide global transcriptome including new transcripts, alternative spliced isoforms and non-coding RNAs. Lower rhombic lip progenitors of the dorsal brainstem are considered the trigger cells in WNT tumors; in SHH subgroup initiation cells are Prominin1+ CD15+ stem cells from the subventricular zone requiring the commitment to Math1+ granule cell progenitors [GCP] of the external granule cell layer [EGL]; while Math1+ or Math1- EGL-GCP or Prominin1+/lineage-negative stem cells sustain the MYC driven Group 3. MbSC derived from SHH tumors and postnatal normal cerebellar stem cells (NcSC) have been reported to share several features. A key signal for both of them is Hedgehog. Furthermore, both NcSC and MbSC display up-regulation of stemness genes (e.g Sox2, Nestin, Nanog, Prom1). Finally, constitutive activation of the Shh pathway by conditional deletion of Ptch1 inhibitory receptor in NcSC, promote medulloblastoma in vivo, producing a mouse model of the human SHH tumor. Acquisition of stemness features may therefore represent the first step of oncogenic conversion. Cooperation with additional oncogenic signals is however needed to enhance MbSC tumorigenicity. In order to understand the MbSCs transcriptional programs, we analyze by RNA-Seq, MbSC derived from Ptch1+/- tumors (Ptch1+/- MbSC). This choice, of a genetically determined model of MB, has allowed us to work with Ptch1+/- MbSC together with appropriate NcSC counterpart, and to analyze biological replicates doing statistical analysis. We identify a number of transcripts, annotated ones, novel isoforms, and long non-coding RNAs, characterizing MbSC and/or NcSC. Some of these genes control stemness or are cancer related and conserved in human medulloblastomas. Interestingly a subset of them, belonging to cell stress response, are of prognostic relevance being significantly related to clinical outcome. Correlation of genes expression characterizing MbSC with survival information from our human medulloblastomas database further demonstrates the significance of these findings. Our data suggest that the modulation of normal and cancer stem cell functions observed in vitro is effective in dissecting the transcriptional programs underlying the in vivo behavior of human medulloblastomas

Combining modularity, conservation, and interactions of proteins significantly increases precision and coverage of protein function prediction

<p>Abstract</p> <p>Background</p> <p>While the number of newly sequenced genomes and genes is constantly increasing, elucidation of their function still is a laborious and time-consuming task. This has led to the development of a wide range of methods for predicting protein functions in silico. We report on a new method that predicts function based on a combination of information about protein interactions, orthology, and the conservation of protein networks in different species.</p> <p>Results</p> <p>We show that aggregation of these independent sources of evidence leads to a drastic increase in number and quality of predictions when compared to baselines and other methods reported in the literature. For instance, our method generates more than 12,000 novel protein functions for human with an estimated precision of ~76%, among which are 7,500 new functional annotations for 1,973 human proteins that previously had zero or only one function annotated. We also verified our predictions on a set of genes that play an important role in colorectal cancer (<it>MLH1</it>, <it>PMS2</it>, <it>EPHB4 </it>) and could confirm more than 73% of them based on evidence in the literature.</p> <p>Conclusions</p> <p>The combination of different methods into a single, comprehensive prediction method infers thousands of protein functions for every species included in the analysis at varying, yet always high levels of precision and very good coverage.</p

(MASSA: Multi-agent system to support functional annotation)

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, Departamento de Ingeniería del Software e Inteligencia Artificial, leída el 23-11-2015Predecir la función biológica de secuencias de Ácido Desoxirribonucleico (ADN) es unos de los mayores desafíos a los que se enfrenta la Bioinformática. Esta tarea se denomina anotación funcional y es un proceso complejo, laborioso y que requiere mucho tiempo. Dado su impacto en investigaciones y anotaciones futuras, la anotación debe ser lo más able y precisa posible. Idealmente, las secuencias deberían ser estudiadas y anotadas manualmente por un experto, garantizando así resultados precisos y de calidad. Sin embargo, la anotación manual solo es factible para pequeños conjuntos de datos o genomas de referencia. Con la llegada de las nuevas tecnologías de secuenciación, el volumen de datos ha crecido signi cativamente, haciendo aún más crítica la necesidad de implementaciones automáticas del proceso. Por su parte, la anotación automática es capaz de manejar grandes cantidades de datos y producir un análisis consistente. Otra ventaja de esta aproximación es su rapidez y bajo coste en relación a la manual. Sin embargo, sus resultados son menos precisos que los manuales y, en general, deben ser revisados ( curados ) por un experto. Aunque los procesos colaborativos de la anotación en comunidad pueden ser utilizados para reducir este cuello de botella, los esfuerzos en esta línea no han tenido hasta ahora el éxito esperado. Además, el problema de la anotación, como muchos otros en el dominio de la Bioinformática, abarca información heterogénea, distribuida y en constante evolución. Una posible aproximación para superar estos problemas consiste en cambiar el foco del proceso de los expertos individuales a su comunidad, y diseñar las herramientas de manera que faciliten la gestión del conocimiento y los recursos. Este trabajo adopta esta línea y propone MASSA (Multi-Agent System to Support functional Annotation), una arquitectura de Sistema Multi-Agente (SMA) para Soportar la Anotación funcional...Predicting the biological function of Deoxyribonucleic Acid (DNA) sequences is one of the many challenges faced by Bioinformatics. This task is called functional annotation, and it is a complex, labor-intensive, and time-consuming process. This annotation has to be as accurate and reliable as possible given its impact in further researches and annotations. In order to guarantee a high-quality outcome, each sequence should be manually studied and annotated by an expert. Although desirable, the manual annotation is only feasible for small datasets or reference genomes. As the volume of genomic data has been increasing, specially after the advent of Next Generation Sequencing techniques, automatic implementations of this process are a necessity. The automatic annotation can handle a huge amount of data and produce consistent analyses. Besides, it is faster and less expensive than the manual approach. However, its outcome is less precise than the one predicted manually and often has to be curated by an expert. Although collaborative processes of community annotation could address this expert bottleneck in automatic annotation, these e orts have failed until now. Moreover, the annotation problem, as many others in this domain, has to deal with heterogeneous information that is distributed and constantly evolving. A possible way to overcome these hurdles is with a shift in the focus of the process from individual experts to communities, and with a design of tools that facilitates the management of knowledge and resources. This work follows this approach proposing MASSA, an architecture for a Multi-Agent System (MAS) to Support functional Annotation...Depto. de Ingeniería de Software e Inteligencia Artificial (ISIA)Fac. de InformáticaTRUEunpu