Applied Bioinformatics in Saccharomyces cerevisiae: Data storage, integration and analysis

The massive amount of biological data has had a significant effect on the field of bioinformatics. This growth of data has not only lead to the growing number of biological databases but has also imposed the needs for additional and more sophisticated computational techniques to proficiently manage, store and retrieve these data, as well as to competently help gaining biological insights and contribute to novel discoveries. This thesis presents results from applying several bioinformatics approaches on yeast datasets. Three yeast databases were developed using different technologies. Each database emphasizes on a specific aspect. yApoptosis collects and structurally organizes vital information specifically for yeast cell death pathway, apoptosis. It includes predicted protein complexes and clustered motifs from the incorporation of apoptosis genes and interaction data. yStreX highlights exploitation of transcriptome data generated by studies of stress responses and ageing in yeast. It contains a compilation of results from gene expression analyses in different contexts making it an integrated resource to facilitate data query and data comparison between different experiments. A yeast data repository is a centralized database encompassing with multiple kinds of yeast data. The database is applied on a dedicated database system that was developed addressing data integration issue in managing heterogeneous datasets. Data analysis was performed in parallel using several methods and software packages such as Limma, Piano and metaMA. Particularly the gene expressions of chronologically ageing yeast were analyzed in the integrative fashion to gain a more thorough picture of the condition such as gene expression patterns, biological processes, transcriptional regulations, metabolic pathways and interactions of active components. This study demonstrates extensive applications of bioinformatics in the domains of data storage, data sharing, data integration and data analysis on various data from yeast S.cerevisiae in order to gain biological insights. Numerous methodologies and technologies were selectively applied in different contexts depended upon characteristics of the data and the goal of the specific biological question

yStreX: yeast stress expression database

Over the past decade genome-wide expression analyses have been often used to study how expression of genes changes in response to various environmental stresses. Many of these studies (such as effects of oxygen concentration, temperature stress, low pH stress, osmotic stress, depletion or limitation of nutrients, addition of different chemical compounds, etc.) have been conducted in the unicellular Eukaryal model, yeast Saccharomyces cerevisiae. However, the lack of a unifying or integrated, bioinformatics platformthat would permit efficient and rapid use of all these existing data remain an important issue. To facilitate research by exploiting existing transcription data in the field of yeast physiology, we have developed the yStreX database. It is an online repository of analyzed gene expression data from curated data sets from different studies that capture genome-wide transcriptional changes in response to diverse environmental transitions. The first aim of this online database is to facilitate comparison of cross-platform and cross-laboratory gene expression data. Additionally, we performed different expression analyses, meta-analyses and gene set enrichment analyses; and the results are also deposited in this database. Lastly, we constructed a user-friendly Web interface with interactive visualization to provide intuitive access and to display the queried data for users with no background in bioinformatics. Database URL: http://www.ystrexdb.co

yApoptosis: yeast apoptosis database

In the past few years, programmed cell death (PCD) has become a popular research area due to its fundamental aspects and its links to human diseases. Yeast has been used as a model for studying PCD, since the discovery of morphological markers of apoptotic cell death in yeast in 1997. Increasing knowledge in identification of components and molecular pathways created a need for organization of information. To meet the demands from the research community, we have developed a curated yeast apoptosis database, yApoptosis. The database structurally collects an extensively curated set of apoptosis, PCD and related genes, their genomic information, supporting literature and relevant external links. A web interface including necessary functions is provided to access and download the data. In addition, we included several networks where the apoptosis genes or proteins are involved, and present them graphically and interactively to facilitate rapid visualization. We also promote continuous inputs and curation by experts. yApoptosis is a highly specific resource for sharing information online, which supports researches and studies in the field of yeast apoptosis and cell death

Applied Bioinformatics in Saccharomyces cerevisiae: Data storage, integration and analysis

The massive amount of biological data has had a significant effect on the field of bioinformatics. This growth of data has not only lead to the growing number of biological databases but has also imposed the needs for additional and more sophisticated computational techniques to proficiently manage, store and retrieve these data, as well as to competently help gaining biological insights and contribute to novel discoveries.This thesis presents results from applying several bioinformatics approaches on yeast datasets. Three yeast databases were developed using different technologies. Each database emphasizes on a specific aspect. yApoptosis collects and structurally organizes vital information specifically for yeast cell death pathway, apoptosis. It includes predicted protein complexes and clustered motifs from the incorporation of apoptosis genes and interaction data. yStreX highlights exploitation of transcriptome data generated by studies of stress responses and ageing in yeast. It contains a compilation of results from gene expression analyses in different contexts making it an integrated resource to facilitate data query and data comparison between different experiments. A yeast data repository is a centralized database encompassing with multiple kinds of yeast data. The database is applied on a dedicated database system that was developed addressing data integration issue in managing heterogeneous datasets. Data analysis was performed in parallel using several methods and software packages such as Limma, Piano and metaMA. Particularly the gene expressions of chronologically ageing yeast were analyzed in the integrative fashion to gain a more thorough picture of the condition such as gene expression patterns, biological processes, transcriptional regulations, metabolic pathways and interactions of active components. This study demonstrates extensive applications of bioinformatics in the domains of data storage, data sharing, data integration and data analysis on various data from yeast S.cerevisiae in order to gain biological insights. Numerous methodologies and technologies were selectively applied in different contexts depended upon characteristics of the data and the goal of the specific biological question

MetaMapR: pathway independent metabolomic network analysis incorporating unknowns

UnlabelledMetabolic network mapping is a widely used approach for integration of metabolomic experimental results with biological domain knowledge. However, current approaches can be limited by biochemical domain or pathway knowledge which results in sparse disconnected graphs for real world metabolomic experiments. MetaMapR integrates enzymatic transformations with metabolite structural similarity, mass spectral similarity and empirical associations to generate richly connected metabolic networks. This open source, web-based or desktop software, written in the R programming language, leverages KEGG and PubChem databases to derive associations between metabolites even in cases where biochemical domain or molecular annotations are unknown. Network calculation is enhanced through an interface to the Chemical Translation System, which allows metabolite identifier translation between >200 common biochemical databases. Analysis results are presented as interactive visualizations or can be exported as high-quality graphics and numerical tables which can be imported into common network analysis and visualization tools.Availability and implementationFreely available at http://dgrapov.github.io/MetaMapR/. Requires R and a modern web browser. Installation instructions, tutorials and application examples are available at http://dgrapov.github.io/MetaMapR/[email protected]

MetaMapR: pathway independent metabolomic network analysis incorporating unknowns: Fig. 1.

Summary: Metabolic network mapping is a widely used approach for integration of metabolomic experimental results with biological domain knowledge. However, current approaches can be limited by biochemical domain or pathway knowledge which results in sparse disconnected graphs for real world metabolomic experiments. MetaMapR integrates enzymatic transformations with metabolite structural similarity, mass spectral similarity and empirical associations to generate richly connected metabolic networks. This open source, web-based or desktop software, written in the R programming language, leverages KEGG and PubChem databases to derive associations between metabolites even in cases where biochemical domain or molecular annotations are unknown. Network calculation is enhanced through an interface to the Chemical Translation System, which allows metabolite identifier translation between >200 common biochemical databases. Analysis results are presented as interactive visualizations or can be exported as high-quality graphics and numerical tables which can be imported into common network analysis and visualization tools. Availability and Implementation: Freely available at http://dgrapov.github.io/MetaMapR/. Requires R and a modern web browser. Installation instructions, tutorials and application examples are available at http://dgrapov.github.io/MetaMapR/. Contact: [email protected]

Genome-wide expression analyses of the stationary phase model of ageing in yeast

Ageing processes involved in replicative lifespan (RLS) and chronological lifespan (CLS) have been found to be conserved among many organisms, including in unicellular Eukarya such as yeast Saccharomyces cerevisiae. Here we performed an integrated approach of genome wide expression profiles of yeast at different time points, during growth and starvation. The aim of the study was to identify transcriptional changes in those conditions by using several different computational analyses in order to propose transcription factors, biological networks and metabolic pathways that seem to be relevant during the process of chronological ageing in yeast. Specifically, we performed differential gene expression analysis, gene-set enrichment analysis and network-based analysis, and we identified pathways affected in the stationary phase and specific transcription factors driving transcriptional adaptations. The results indicate signal propagation from G protein-coupled receptors through signaling pathway components and other stress and nutrient-induced transcription factors resulting in adaptation of yeast cells to the lack of nutrients by activating metabolism associated with aerobic metabolism of carbon sources such as ethanol, glycerol and fatty acids. In addition, we found STE12, XBP1 and TOS8 as highly connected nodes in the subnetworks of ageing yeast

Genomic, Proteomic, and Metabolomic Data Integration Strategies

Robust interpretation of experimental results measuring discreet biological domains remains a significant challenge in the face of complex biochemical regulation processes such as organismal versus tissue versus cellular metabolism, epigenetics, and protein post-translational modification. Integration of analyses carried out across multiple measurement or omic platforms is an emerging approach to help address these challenges. This review focuses on select methods and tools for the integration of metabolomic with genomic and proteomic data using a variety of approaches including biochemical pathway-, ontology-, network-, and empirical-correlation-based methods

Systems biology of yeast cell death

Programmed cell death (PCD) (including apoptosis) is an essential process, and many human diseases of high prevalence such as neurodegenerative diseases and cancer are associated with deregulations in the cell death pathways. Yeast Saccharomyces cerevisiae, a unicellular eukaryotic organism, shares with multicellular organisms (including humans) key components and regulators of the PCD machinery. In this article, we review the current state of knowledge about cell death networks, including the modeling approaches and experimental strategies commonly used to study yeast cell death. We argue that the systems biology approach will bring valuable contributions to our understanding of regulations and mechanisms of the complex cell death pathways