In silico evaluation of the Eucalyptus transcriptome

The expressed sequence tags (ESTs) produced in the Forests project provide an invaluable opportunity to assess the Eucalyptus transcriptome. Besides providing information on the different proteins produced by this plant, it is possible to infer gene expression profiles because non-normalized cDNA libraries were used. The EST frequency from any gene is correlated to the transcript levels in the tissues from which the cDNA libraries were constructed. The goal of this work was to identify Eucalyptus genes that showed either differential expression pattern or were ubiquitously expressed in the tissues sampled in the Forests project. Six robust statistical tests and very restrictive rules were applied to gain confidence in the in silico data aiming to avoid false positives. Several genes with interesting expression profiles were identified and some of them were validated by RT-PCR. Copyright by the Brazilian Society of Genetics.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq

Combining Evidence of Preferential Gene-Tissue Relationships from Multiple Sources

An important challenge in drug discovery and disease prognosis is to predict genes that are preferentially expressed in one or a few tissues, i.e. showing a considerably higher expression in one tissue(s) compared to the others. Although several data sources and methods have been published explicitly for this purpose, they often disagree and it is not evident how to retrieve these genes and how to distinguish true biological findings from those that are due to choice-of-method and/or experimental settings. In this work we have developed a computational approach that combines results from multiple methods and datasets with the aim to eliminate method/study-specific biases and to improve the predictability of preferentially expressed human genes. A rule-based score is used to merge and assign support to the results. Five sets of genes with known tissue specificity were used for parameter pruning and cross-validation. In total we identify 3434 tissue-specific genes. We compare the genes of highest scores with the public databases: PaGenBase (microarray), TiGER (EST) and HPA (protein expression data). The results have 85% overlap to PaGenBase, 71% to TiGER and only 28% to HPA. 99% of our predictions have support from at least one of these databases. Our approach also performs better than any of the databases on identifying drug targets and biomarkers with known tissue-specificity

Current approaches to gene regulatory network modelling.

Abstract Many different approaches have been developed to model and simulate gene regulatory networks. We proposed the following categories for gene regulatory network models: network parts lists, network topology models, network control logic models, and dynamic models. Here we will describe some examples for each of these categories. We will study the topology of gene regulatory networks in yeast in more detail, comparing a direct network derived from transcription factor binding data and an indirect network derived from genome-wide expression data in mutants. Regarding the network dynamics we briefly describe discrete and continuous approaches to network modelling, then describe a hybrid model called Finite State Linear Model and demonstrate that some simple network dynamics can be simulated in this model.</p

Linking assessment and learning analytics to support learning processes in higher education

In higher education assessments are mostly used for summative purposes such as grading and certifying. Albeit, assessments are also considered to support learning processes by offering formative feedback to learners about their current performance and how to improve. Even though such feedback might enhance learners’ self-regulated learning processes, it is used infrequently due to resource constraints. In addition, the competences, skills, and knowledge that should be assessed are evermore complex. To derive valid inferences about learners’ current performance, ongoing assessments across contexts are desirable. With the advancing use of digital learning environments, learning analytics are also coming in for increasing discussion in higher education. However, learning analytics are still not sufficiently linked to learning theory and are lacking empirical evidence. Hence, the purpose of this paper is to propose how theory on assessment and related feedback can be linked to learning analytics with regard to supporting self-regulated learning. Therefore, relevant concepts of assessment, assessment design, and feedback plus current perspectives on learning analytics are introduced. Based on this theoretical foundation, a conceptual integrative framework and potential learning analytics features were derived. The framework and its implications plus further research needs are discussed and concluded