Genómica Funcional e Bioinformática

Objectives

The discipline aims to convey the most recent experimental approaches and bioinformatics tools in the field of Comparative and Functional Genomics, as well as its applications to the study of Biology at a genome-wide scale, in an integrative perspective. It emphasizes the development and utilization of computational resources for the analysis of genomic data.

Syllabus

W1 Genome structure and organization. Genome sequencing methods and strategies. Genome annotation. Metagenomics. Comparative genomics. W2 Genome- wide expression analysis: transcriptomics and RNomics W3 Expression Proteomics W4 Metabolomics and other Omics W5 Functional Genomics and Introduction to Systems Biology W6 - Introduction to Bioinformatics. Data mining and Biostatistics: Concepts and Techniques. Unsupervised Learning: Clustering, Dimensionality Reduction (Principal Components Analysis). W7 - Applications: Microarray Data Analysis. Supervised learning: multiple regression, logistic regression, decision trees, Naive Bayes. Applications: Model fitting and classification.

Prerequisites

The students taking this CU should have basic knowledge on Molecular Biology, Biochemistry and Genetics. For students holding a BSc degree not related to Life Sciences, the frequency of the CU Introduction to Biological Sciences is recommended.

Cross Competence Component

This CU promote the following soft skills, whose evaluation accounts for 10% of the final grade: 1 - critical thinking and problem solving: theoretical and practical classes and the evaluation through tests and exams is conducted by proposing real problems that the students need to find the solution to, while discussing its advantages and limitations. 2 - Interpersonal skills: the computation lab classes and derived reports are conducted in groups of 3, thus requiring the development of leadership, team work and conflict management. Oral and writen communication skills will be stimulated based on the delivery of writen reports and their oral presentation. 3 - Intrapersonal skills: reports are delivered on fixed deadlines, thus promoting self-discipline and organization skills. 4 - Global citizenship: principles of ethics in science are discussed; many of the proposed biotech problems are considered within the principles of global economic growth and environmental sustainability.

Laboratorial Component

Lab classes will focus on the use of bioinformatics tools for: 1. Genome annotation and comparative genomics 2. Protein structure prediction 3. Quantitative analysis of 2-dimensional protein gels 4. Interpretation of the biological meaning of genome-wide data 5. NMR-based metabolomics analysis 6. Metabolic network modelling 7. Data clustering 8. Data classification 9. Biostatistics

Programming And Computing Component

The lab classes will be stricly computer lab classes. Part of the CU is foccused on the use of pre-existing software applications, while one third is foccused on the development of new tools, developing programming skills. Specifically, the following software will be used: 1. Genome annotation and comparative genomics – CLC Genomics Workbench 2. Protein structure prediction – VAST; EXPRESSO 3. Quantitative analysis of 2-dimensional protein gels - SameSpots 4. Interpretation of the biological meaning of genome-wide data – GoToolBox; Kegg Mapper; Yeastract; STRING 5. NMR-based metabolomics analysis – MetaboAnalyst 6. Metabolic network modelling – COPASI 7. Data clustering – Genesis; MATLAB 8. Data classification - MATLAB 9. Biostatistics - MATLAB

Ethical Principles

All members of a group are responsible for the group’s work In any assessment every student shall honestly disclose any help received and sources used. In an oral assessment, every student shall be alble to present and answer questions about the entire assignment and solution.