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Technical Courses V: Methodological approaches in Neuroscience, Elements of Bioinformatics (big data bases)


This is an intensive two and a half week course focused on computational analysis and robust interpretation of molecular data streams, generated by a broad spectrum of high-throughput experimental technologies, termed as -omics.  Overall these technologies revolutionize the landscape of modern biological research, enabling adoption of holistic approaches in the study and modification of biological mechanisms, yet their efficient integration in the discovery cycle entails great challenges, due to their immense complexity. The derivation of the instrumental molecular networks, behind disease emergence and progression, requests the intelligent utilization of powerful, computational strategies, in order to single out of the millions of biological measurements, those pivotal for the disease interrogated. Moreover, it is crucial to prioritize the important cellular events, in order to be able to propose a rational, combinatorial therapeutic approach, targeting these events, with novel combinations of compounds. In this direction, the various pillars of computational analysis that aid efficient and robust integration, analysis and interpretation of high-dimensional, omic data, potentially from multiple layers of dissection (cross-omics) will be examined, as well as the respective experimental technologies they support. 

Course Overview

Ultimate goal of this intensive course is that students are gaining familiarization with this broad pool of experimental technologies, under the umbrella of -omics, together with the various sorts of bio-informatic analytical algorithms and workflows, deployed at different stages and for different data-types. In addition, emphasis will be given in the meaningful integration and robust functional interpretation, in terms of the active emergent molecular modules that shape the phenotypic landscape of the biological problem interrogated, as well as the reliable association of molecular with phenotypic markers. The course will review the application of these concepts in the field of epidemiological stratification, pharmacogenomics analysis and personalized medicine. 

Topics to be discussed will cover

•             Next generation sequencing technologies, providing an introduction coupled by an overview of the main Next Generation sequencing methodologies (Gen-Seq, Exome seq, RNA- Seq, ChIP-Seq, etc), from the point of view of the computational analysis (de-novo / Reference Genome-based Assembly, Filtering, Signal Estimation, Differential Expression, Statistical Selection, Variant Calling) 

•             Microarray technologies and their processing (background correction, signal estimation, normalization, filtering and statistical selection)

•             Bioinformatic methodologies for multi-layered structural and functional characterization  of nucleotide and aminoacid sequences (homology based screening, ORF and gene function prediction, taxonomic and phylogenetic analysis, protein domain analysis, machine learning for functional characterization of proteins, regulatory motif analysis, hot-spot prediction, metagenomic screening)

•             Translational, integrative bioinfomatic analysis of omic datasets, which  highlight the critical biological processes implicated in the biological problem interrogated (integrative methodologies, enrichment statistics, ontologies and controlled vocabularies, resampling based correction, gene set analysis, pathway prioritization, pharmacogenomic  knowledgebases)

•             Target prioritization and diagnostic stratification discussing the methodologies for the inference of small-sized, highly informative signatures for diagnostic classification, pharmacogenomic analysis, combinatorial treatment (semantic networks, interaction networks, network inference and analysis, derivation of molecular signatures, classification and clustering methodologies, machine learning techiques)

Skills & Learning Outcomes

Upon successful completion of this course, students will be able to define, describe and discern critical functional features of:

1.            the main Next Generation Sequencing Methodologies and their fundamental computational steps.

2.            the current state-of-the-art microarray technologies, popular platform configurations for various omic experimental designs, and the requisite algorithmic processing steps.

3.            the main structural bioinformatic algorithmic tools that are available for the analysis and functional characterization of nucleotide and aminoacid sequences.

4.            various molecular enrichment, gene set and pathway analysis tools / platforms, 

5.            Network based methodologies for target prioritization, connectivity with drug-related databases, geometric estimation of complexity (Principal Component Analysis) supervised (machine learning, Linear discriminant analysis, PLS) and unsupervised (clustering) classification of phenotypic categories