Lan Umek

Computational approaches for the genetic and phenotypic characterization of a Saccharomyces cerevisiae wine yeast collection

Within this study, we have used a set of computational techniques to relate the genotypes and phenotypes of natural populations of Saccharomyces cerevisiae, using allelic information from 11 microsatellite loci and results from 24 phenotypic tests. A group of 103 strains was obtained from a larger S. cerevisiae winemaking strain collection by clustering with self‐organizing maps. These strains were further characterized regarding their allelic combinations for 11 microsatellites and analysed in phenotypic screens that included taxonomic criteria (carbon and nitrogen assimilation tests, growth at different temperatures) and tests with biotechnological relevance (ethanol resistance, H2S or aromatic precursors formation). Phenotypic variability was rather high and each strain showed a unique phenotypic profile. The results, expressed as optical density (A640) after 22 h of growth, were in agreement with taxonomic data, although with some exceptions, since few strains were capable of consuming arabinose and ribose to a small extent. Based on microsatellite allelic information, naïve Bayesian classifier correctly assigned (AUC = 0.81, p < 10−8) most of the strains to the vineyard from where they were isolated, despite their close location (50–100 km). We also identified subgroups of strains with similar values of a phenotypic feature and microsatellite allelic pattern (AUC > 0.75). Subgroups were found for strains with low ethanol resistance, growth at 30 °C and growth in media containing galactose, raffinose or urea. The results demonstrate that computational approaches can be used to establish genotype–phenotype relations and to make predictions about a strains biotechnological potential. Copyright

Computational models for prediction of yeast strain potential for winemaking from phenotypic profiles.

Saccharomyces cerevisiae strains from diverse natural habitats harbour a vast amount of phenotypic diversity, driven by interactions between yeast and the respective environment. In grape juice fermentations, strains are exposed to a wide array of biotic and abiotic stressors, which may lead to strain selection and generate naturally arising strain diversity. Certain phenotypes are of particular interest for the winemaking industry and could be identified by screening of large number of different strains. The objective of the present work was to use data mining approaches to identify those phenotypic tests that are most useful to predict a strains potential for winemaking. We have constituted a S. cerevisiae collection comprising 172 strains of worldwide geographical origins or technological applications. Their phenotype was screened by considering 30 physiological traits that are important from an oenological point of view. Growth in the presence of potassium bisulphite, growth at 40°C, and resistance to ethanol were mostly contributing to strain variability, as shown by the principal component analysis. In the hierarchical clustering of phenotypic profiles the strains isolated from the same wines and vineyards were scattered throughout all clusters, whereas commercial winemaking strains tended to co-cluster. Mann-Whitney test revealed significant associations between phenotypic results and strains technological application or origin. Naïve Bayesian classifier identified 3 of the 30 phenotypic tests of growth in iprodion (0.05 mg/mL), cycloheximide (0.1 µg/mL) and potassium bisulphite (150 mg/mL) that provided most information for the assignment of a strain to the group of commercial strains. The probability of a strain to be assigned to this group was 27% using the entire phenotypic profile and increased to 95%, when only results from the three tests were considered. Results show the usefulness of computational approaches to simplify strain selection procedures.

Subgroup Discovery in Data Sets with Multi---dimensional Responses: A Method and a Case Study in Traumatology

Biomedical experimental data sets may often include many features both at input (description of cases, treatments, or experimental parameters) and output (outcome description). State-of-the-art data mining techniques can deal with such data, but would consider only one output feature at the time, disregarding any dependencies among them. In the paper, we propose the technique that can treat many output features simultaneously, aiming at finding subgroups of cases that are similar both in input and output space. The method is based on k -medoids clustering and analysis of contingency tables, and reports on case subgroups with significant dependency in input and output space. We have used this technique in explorative analysis of clinical data on femoral neck fractures. The subgroups discovered in our study were considered meaningful by the participating domain expert, and sparked a number of ideas for hypothesis to be further experimentally tested.

Subgroup discovery in data sets with multi-dimensional responses

Most of the present subgroup discovery approaches aim at finding subsets of attribute-value data with unusual distribution of a single output variable. In general, real-life problems may be described with richer, multi-dimensional descriptions of the outcome. The discovery task in such domains is to find subsets of data instances with similar outcome description that are separable from the rest of the instances in the input space. We have developed a technique that directly addresses this problem and uses a combination of agglomerative clustering to find subgroup candidates in the space of output attributes, and predictive modeling to score and describe these candidates in the input attribute space. Experiments with the proposed method on a set of synthetic and on a real social survey data set demonstrate its ability to discover relevant and interesting subgroups from the data with multi-dimensional fesponses.

Computational models reveal genotype-phenotype associations in Saccharomyces cerevisiae

Genome sequencing is essential to understand individual variation and to study the mechanisms that explain relations between genotype and phenotype. The accumulated knowledge from large‐scale genome sequencing projects of Saccharomyces cerevisiae isolates is being used to study the mechanisms that explain such relations. Our objective was to undertake genetic characterization of 172 S. cerevisiae strains from different geographical origins and technological groups, using 11 polymorphic microsatellites, and computationally relate these data with the results of 30 phenotypic tests. Genetic characterization revealed 280 alleles, with the microsatellite ScAAT1 contributing most to intrastrain variability, together with alleles 20, 9 and 16 from the microsatellites ScAAT4, ScAAT5 and ScAAT6. These microsatellite allelic profiles are characteristic for both the phenotype and origin of yeast strains. We confirm the strength of these associations by construction and cross‐validation of computational models that can predict the technological application and origin of a strain from the microsatellite allelic profile. Associations between microsatellites and specific phenotypes were scored using information gain ratios, and significant findings were confirmed by permutation tests and estimation of false discovery rates. The phenotypes associated with higher number of alleles were the capacity to resist to sulphur dioxide (tested by the capacity to grow in the presence of potassium bisulphite) and the presence of galactosidase activity. Our study demonstrates the utility of computational modelling to estimate a strain technological group and phenotype from microsatellite allelic combinations as tools for preliminary yeast strain selection. Copyright

New integrative computational approaches unveil the Saccharomyces cerevisiae pheno-metabolomic fermentative profile and allow strain selection for winemaking

During must fermentation by Saccharomyces cerevisiae strains thousands of volatile aroma compounds are formed. The objective of the present work was to adapt computational approaches to analyze pheno-metabolomic diversity of a S. cerevisiae strain collection with different origins. Phenotypic and genetic characterization together with individual must fermentations were performed, and metabolites relevant to aromatic profiles were determined. Experimental results were projected onto a common coordinates system, revealing 17 statistical-relevant multi-dimensional modules, combining sets of most-correlated features of noteworthy biological importance. The present method allowed, as a breakthrough, to combine genetic, phenotypic and metabolomic data, which has not been possible so far due to difficulties in comparing different types of data. Therefore, the proposed computational approach revealed as successful to shed light into the holistic characterization of S. cerevisiae pheno-metabolome in must fermentative conditions. This will allow the identification of combined relevant features with application in selection of good winemaking strains.

The impact of demographic factors on selected aspects of e-learning in higher education

Purpose – In higher education, a combination of traditional face-to-face learning and e-learning is becoming very popular. During their studies, students are enroled in several e-courses. They perceive various aspects of e-courses and show different responses when using teaching materials and learning in an e-course. The purpose of this paper is to measure such aspects from the students’ perspective and explore the differences among various subgroups of students. Design/methodology/approach – In the survey, students expressed their opinions on 13 different aspects (a seven-level scale) of the e-courses in which they were enroled. In addition, the influence of some demographic characteristics was analysed. The authors used statistical tests (t-test and ANOVA) to compare the means among the analysed subgroups. Findings – The empirical results reveal some differences among the subgroups of students. Students’ attitudes to blended learning increase significantly by year of study and decrease according to the amount of other non-study activities. Simplicity of finding materials in an e-course is the factor where male and female students differ significantly. This finding serves as a guideline for faculty management concerned with how to adjust blended learning to fulfil the various expectations of different student subgroups. Originality/value – This paper’s insights will be of value to individuals and institutions engaged in the e-learning process in higher education. In particular, the results will be helpful to the faculty management and teachers with the main task to increase the engagement of particular groups of students regarding the work in e-courses.

Demographic determinants of usefulness of e-learning tools among students of public administration

Purpose In higher education, blended learning is already strongly established. The e-courses vary in their structure, assignments, prompt examinations, interaction between students and teachers, etc. Such aspects may influence the students’ perception of usefulness of blended learning. The purpose of this paper is to identify the factors which influence that feeling and to look for possible differences in perception by different subgroups of students. Design/methodology/approach Students in the survey evaluated 13 aspects of e-courses in which they were enrolled. From enrolment documents, additional demographic data were collected (gender, high-school grade, study programme, etc.). A multiple linear regression was used with perceived usefulness as the response variable and the 12 other e-course aspects as predictors. Further, the same regression analysis was performed on different subgroups of students based on demographical data. Findings The empirical results showed that the general impression regarding the e-courses, their consistency with the face-to-face teaching and the teachers’ responsiveness had a significant influence on the students’ perception of the usefulness of e-courses. Further analysis based on demographic data revealed several subgroups of students where the perception of usefulness was influenced by different aspects. The teachers’ feedback and supplementing the tutorial play an important role in higher years of study, while the general impression loses its influence. Originality/value The paper is the first to explore the importance of demographic determinants of perceived usefulness of e-learning tools in EAPAA (European Association of Public Administration Accreditation)-accredited undergraduate public administration programmes.