Summary: Brief history of bioinformatics. Concepts and characteristics of biological databases. Bioinformatics applied to structural and functional genomics. Algorithms and tools for nucleotide and amino acid sequence alignments. Search for sequence similarity. Patterns and motifs in amino acid sequences. Phylogeny and protein structural analysis.
Summary: Fundamentals of Unix-based Operating Systems. Introduction to Linux and Shell Script programming language.
Summary: Algorithms and main programming languages used in bioinformatics. Data structure, storage, and data manipulation. Concepts and programming styles. Program refinement techniques. Controlling strategies and recursive subroutines. Abstract data types: lists, stacks, queues. Data representation and manipulation: tables, lists, trees, graphs. Files. Sorting and searching techniques. At the end of the course, the student is expected to know the fundamentals of (1) at least one compiled language (e.g. C, C ++), (2) at least one interpreted language (e.g. Python, Perl), and (3) at least one programming language widely used in bioinformatics and statistics (e.g. R, MATLAB).
Summary: Database technologies. Biological databases. Database modeling. Database design, construction and administration in bioinformatics.
Summary: Structure and function of biomolecules. Properties of the chemical compounds that constitute living organisms and their functions in cellular biochemistry. Basic concepts in chemistry, bioenergetics, and metabolism.
Summary: Structure of nucleic acids of prokaryotic and eukaryotic genes and chromosomes. DNA replication, transcription, translation and repair mechanisms. Regulation of these processes. Genetic recombination and mutations. Concepts and techniques in molecular biology.
Summary: Modern approaches in systems biology focused on multidimensional data analysis. Reconstruction and analysis of regulatory networks in molecular biology, genetics, cell and developmental biology. Analysis of globally coherent datasets involving at least three levels of information, such as: genetic variability, gene expression, and a phenotype of interest. The main motivation for this course is to discuss how different layers of information are connected and which methods can provide a systemic view of biological problems.
Summary: Genomics, transcriptomics, and proteomics. Structure of eukaryotic and prokaryotic genomes. Genome sequencing and analysis. Metagenomics and methods for comparative genomics. Biodiversity. DNA polymorfism. Methods for detecting genetic variants. Genetic testing and potential therapeutic interventions. Methods for detecting and sequencing transcripts. Gene annotation and differential expression analysis of transcripts. Interference and non-coding RNAs. Protein sequencing methods and proteome comparison. Protein-protein interaction networks. Synthetic biology. Large scale DNA sequencing data and automated analysis pipelines. Genome annotation workflows. Files and data formats for storage of biological information associated to nucleotide and amino acid sequences.
Summary: Introduction to molecular evolution. Multiple sequence alignment. Evolutionary models. Approaches to phylogenetic tree reconstruction based on searching algorithms. Character- and distance-based methods. File formats used in phylogenetic analysis. Computational tools to build phylogenetic trees based on nucleotide and amino acid sequences. Ancestral sequence reconstruction. Adaptive evolution. Phylogenetic networks. Phylogenomics.
Summary: Data storage, manipulation and retrieval. Data recovery strategies. Data mining strategies in bioinformatics.
Summary: Artificial Intelligence techniques applied to bioinformatics. Neural networks. Fuzzy logic. Fuzzy sets. Pattern discovery and recognition. Expert systems. Genetic algorithms.
Summary: Statistical methods commonly used in bioinformatics that require intensive computing, for example: permutation and resampling techniques. Identification of sources of variation and biases in genomic data. Multiple hypothesis tests. Level of significance and correction. Analysis and integration of multidimensional data. Cluster stability. Enrichment analysis. Mutual information and related concepts.
Summary: Concepts and statistics commonly used in biological data analysis. Measures of central tendency. Measures of dispersion. Probability. Comparison of means. Comparison of proportions. Contingency table. Chi-square test. Adherence test. Accuracy and concordance analysis. Relative risk. Correlation. Linear regression. Nonlinear regression. Analysis of variance. Errors and residuals. Survival analysis. Analysis of groups. Multivariate analysis.
Summary: Exploring innovative concepts in bioinformatics.
Summary: Workshops about new methods and techniques in bioinformatics.
Summary: Workshops about new methods and techniques in bioinformatics.
Summary: Concepts and principles of the scientific method. Ethical bases in scientific research. Stages of a research project, including planning, development, qualification (progress review by the academic board), and presentation of the results. Literature searching and reviews. Data formats and use of reference management tools.
Summary: Experience and training in teaching bioinformatics and correlated areas. The student will attend theoretical and/or practical classes related to the field of bioinformatics, teach theoretical and/or practical classes under supervision, participate in teaching evaluations, use pedagogical methods and techniques such as seminars and case studies.
Summary: Presentation and discussion of projects and partial results obtained by the students. Seminars will also be given by invited speakers.
Summary: Presentation and discussion of final-stage projects and results, obtained by the students, aiming the conclusion of the research project. Seminars will also be given by invited speakers.
Summary: First cycle of discussions on papers, thesis, and on going projects.
Summary: Second cycle of discussions on papers, thesis, and on going projects.