Research Training Exercise (Laboratory Rotation) – Technical Courses I
Teaching hours
This is a 1st semester course of about 8 weeks that corresponds to 10 ECTs and 39 total hours of lectures and approximately 320 total hours of laboratory presence/training.
The course is subdivided into the following parts:
- Technical courses I: Methodological approaches in Neuroscience, Statistics (SPSS, R programming, graphpad)
- Technical courses II: Molecular Biology-Omics
- Technical courses III: Methodological approaches in Neuroscience, Elements of Bioinformatics (big data bases)
Co-ordinators
- Nikos Fyllas
Assistant Professor, Department of Biology, National and Kapodistrian University of Athens
- Spiros Efthimiopoulos Ph.D
Professor, Department of Biology, National and Kapodistrian University of Athens
Description:
This course is intended to cover the basic statistical principles usually applied in the biological sciences. The goal of the course is to provide the students with the ability to choose and apply the appropriate statistics for their studies.
Course Overview
The course will begin with an analysis of the hypothesis test, a statistical test used to compare two data sets for the purpose of rejecting a null hypothesis and not to indicate the more likely of two hypotheses. The students will also be introduced in the process of randomization, of making something random, for example on how to select a random sample of a population. In addition, the course will provide detailed information on some of the most commonly used distributions and an analysis of the central limit theorem which establishes that the sum of independent random variables tends toward a normal distribution although the original variables may not. Time will be allocated in the presentation of parametric and nonparametric tests and their comparison in order for the students to understand the prerequisites for applying one or the other. Special attention will be given to the multiple comparison problems and means to overcome it. The problem arises from the fact that when you are performing multiple statistical tests a fraction of them is false positives. The application of Linear, Logistic and Cox models will be also presented.
Technical Courses I: Methodological approaches in Neuroscience, Statistics (SPSS, R programming, graphpad)
Description:
This course is intended to cover the basic statistical principles usually applied in the biological sciences. The goal of the course is to provide the students with the ability to choose and apply the appropriate statistics for their studies.
Course Overview
The course will begin with an analysis of the hypothesis test, a statistical test used to compare two data sets for the purpose of rejecting a null hypothesis and not to indicate the more likely of two hypotheses. The students will also be introduced in the process of randomization, of making something random, for example on how to select a random sample of a population. In addition, the course will provide detailed information on some of the most commonly used distributions and an analysis of the central limit theorem which establishes that the sum of independent random variables tends toward a normal distribution although the original variables may not. Time will be allocated in the presentation of parametric and nonparametric tests and their comparison in order for the students to understand the prerequisites for applying one or the other. Special attention will be given to the multiple comparison problems and means to overcome it. The problem arises from the fact that when you are performing multiple statistical tests a fraction of them is false positives. The application of Linear, Logistic and Cox models will be also presented.
Technical courses II: Molecular Biology-Omics
Description:
These series of lectures will provide
- an overview of Next Generation Sequencing (NGS) and the most relevant biomedical NGS applications: Targeted sequencing, Whole-exome sequencing, Whole-genome sequencing, RNA-seq (mRNA-seq, smallRNA-seq, etc., approaches to study RNA structure and RNA-protein interactions), approaches to interrogate the composition and structure of chromatin (ChIP-seq, ATAC-seq, chromosome conformation capture techniques, etc.).
- an introduction to proteomic methodologies and their application to the study of human diseases.
- an introduction of the significance but also the challenges of applying metabolomic analysis in neurophysiology research. The students will be presented with the major changes in the way problems in life sciences are now approached in the context of the systems biology and the omic analyses revolution, focusing on brain research and the field of systems neurophysiology. The muti-step metabolomic analysis will be described and its contribution to the reconstruction of an accurate metabolic physiology map for the brain will be discussed. Experimental and computational protocol standardization challenges that need to be addressed for its vast deployment in neurophysiology research and practice will be described. An example of brain metabolomic analysis in a mouse model will be presented.
- Examples of genetic and biochemical approaches to develop agents interfering with protein aggregation. Many neurodegenerative diseases are associated with protein misfolding and protein self-assembly, which lead to the formation of protein oligomers and/or higher-order aggregates with neurotoxic properties. Thus, understanding these pathogenic processes is of fundamental importance for neurobiology. Furthermore, chemical and biological agents interfering with protein aggregation are much sought-after factors in the quest for effective drugs against these conditions.
Course Overview
The course will cover basic principles of NGS technologies, description of the omic analysis revolution and the consequent fundamental changes in the way problems in life sciences are now approached, mass spectrometry and applications involving differential proteomics, identification of post-translational modifications and analysis of protein complexes as well as of metabolomics. Description of the multi-step experimental and computational analysis process that needs to be carefully designed and standardized for its accurate and vast application in neurophysiology research.
Furthermore, biochemical, biophysical and biological assays, which can be utilized for high-throughput screenings of chemical and biological libraries so as to discover modulators of protein aggregation will be described. Furthermore, the design, development and outcomes of recently developed biotechnological platforms for producing chemical libraries with greatly expanded diversities and for identifying chemical rescuers of pathogenic protein misfolding and aggregation in an ultrahigh-throughput fashion will also be covered.
Skills & Learning Outcomes
The objective will be to familiarize students with
- the principles underlying NGS and the main biomedical applications in which NGS is employed
- the experimental design of proteomic experiments and current challenges in their application to the study of human diseases
- the holistic perspective of biological system analysis gained from systems and network biology research and shown the complementary role of the various omic analyses in deciphering the complexity of brain function
- metabolomic analysis and it’s various experimental and computational components
- network reconstruction and how this could contribute to the elucidation of brain metabolic physiology and the reconstruction of the entire brain connectome
- the experimental and computational challenges that need to be considered for accurate application of metabolomics in brain research
- state-of-the-art high-throughput approaches for monitoring neurodegeneration-associated protein aggregation and for identifying chemical and biological inhibitors of these processes
- recently developed biotechnological approaches for the discovery of chemical rescuers of protein misfolding and aggregation with potentially therapeutic effects against major neurodegenerative diseases
Technical courses III: Methodological approaches in Neuroscience, Elements of Bioinformatics (big data bases)
Description:
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:
- the main Next Generation Sequencing Methodologies and their fundamental computational steps.
- the current state-of-the-art microarray technologies, popular platform configurations for various omic experimental designs, and the requisite algorithmic processing steps.
- the main structural bioinformatic algorithmic tools that are available for the analysis and functional characterization of nucleotide and aminoacid sequences.
- various molecular enrichment, gene set and pathway analysis tools / platforms.
- 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
Titles of lectures and names of the lecturers
A/A | TECHNICAL COURSES: Statistics, Molecular Biology, Bioinformatics | Lecturers |
1 | Comparing means, one- and two- sample tests (parametric and non-parametric), ANOVA, Confounding variables: presence and control of confounding effects | Nikos Fyllas |
2 | Correlational designs, Correlation and Regression, Multiple Linear Regression | Nikos Fyllas |
3 | Random variables and Probability Distributions, Descriptive statistics, Framing and Testing Hypotheses, Meaning of statistical significance, R overview | Nikos Fyllas |
4 | High throughput assays-amyloid disassembly or prevention of protein aggregation | George Skretas |
5 | Next Generation Sequencing and its applications in Biomedicine. Genomics | Pantelis Hatzis |
6 | Next Generation Sequencing and its applications in Biomedicine. Genomics | Pantelis Hatzis |
7 | Next Generation Sequencing and its applications in Biomedicine. Genomics | Pantelis Hatzis |
8 | Proteomics | George Panayotou |
9 | Data Analysis and Results Visualization in Proteomics | Martina Samiotaki |
10 | Introduction to Bioinformatics, Biological Data Types, Biological Repositories, Querying Biological Databases, sequence analysis, sequence-structure-function, taxonomic/phylogenetic analysis | E. Pilalis-Fani Gkotsi |
11 | Analysis of Biological signals (Microarrays/ Next Generation Seq technologies), types of biological signals and workflows, processing steps | E. Pilalis-Fani Gkotsi |
12 | Signature mediated interpretation, stratification, machine learning, precision medicine | E. Pilalis-Fani Gkotsi |
13 | Protein and DNA Sequence Analysis | I. Michalopoulos |
