Identify, describe and define key concepts in Bioinformatics in general and the processing and analysis of biological sequences in particular; Identify and describe the major problems faced by the level of processing and analysis of biological sequences Choose the appropriate classes of algorithms for solving the basic problems of Bioinformatics, namely in the processing and analysis of biological sequences; Implement basic algorithms for processing and analysis of biological sequences using programming languages and scripting using Bioinformatics software repositories available.
Introduction to basic concepts of bioinformatics and analysis of biological sequences; Basic algorithms for processing biological sequences of DNA, RNA and proteins Algorithms to search for patterns (motifs) in sequences; deterministic automata; suffix trees Sequence alignment algorithms: dynamic programming, heuristic algorithms Searches in databases for similar sequences; BLAST algorithm Algorithms for progressive multiple sequence alignment; ClustalW algorithm Implementing algorithms for biological sequence analysis using scripting languages; Exploitation of software repositories Bioinformatics and interaction with databases of biological sequences by programming
Lectures are taught covering the theoretical concepts and laboratory classes where students develop programs in a scripting language to implement algorithms for sequence analysis and access the existing online repositories. The evaluation is involves a test (70% of classification) and individual assignments during the entire period(30%).
H.J. Bockenhauer, D. Bongartz. Algorithmic Aspects of Bioinformatics, Springer, 2007 D. Mount, Bioinformatics: Sequence and genome analysis, 2nd ed., Cold Spring Harbor Laboratory Press, New York, 2006. S. Bassi, Python for Bioinformatics, CRC Press, 2009.
1 Identify and describe concepts and advanced problems in Bioinformatics 2 Choose classes of algorithms appropriate for solving complex problems in Bioinformatics, including optimization methods 3 Know the main alternatives in optimization metaheuristics, namely in the area of Natural Computation and their applications in advanced Bioinformatics problems 4 Implement advanced algorithms in Bioinformatics, including metaheuristic optimization methods using an appropriate programming language
1 Advanced algorithms for sequence analysis: motif discovery, multiple alignment, phylogenetic analysis 2 Clustering algorithms 3 Stochastic optimization methods and their application in Bioinformatics, including metaheuristic methods from Natural Computation 4 Graph algorithms and their application in cell modeling 5 Implementing advanced Bioinformatics algorithms using a programming language
Lectures: s Practical classes in the computer lab: Evaluation: written test; practical work in groups
- N. C. Jones, P. Pevzner, An Introduction to Bioinformatics Algorithms, MIT Press, 2004 - H.J. Bockenhauer, D. Bongartz. Algorithmic Aspects of Bioinformatics, Springer, 2007 - D. Mount, Bioinformatics: Sequence and genome analysis, 2nd ed., Cold Spring Harbor Laboratory Press, New York, 2006.
1 Identify, describe and define the main concepts related to knowledge extraction from databases, machine learning and data mining 2 Select the appropriate methodologies and apply available software to solve problems in data analysis 3 Know and be able to implement the main algorithms related to the techniques of Data Mining and Machine Learning 4 Apply the methodologies of knowledge extraction in the case of biological databases
1. Knowledge extraction from databases: the process and related concepts 2. Data Mining and Machine Learning: models and algorithms of classification and regression; decision trees; classification rules; neural networks; functional models; instance based learning 3. Evaluation and comparison of models and algorithms 4. Advanced topics: ensembles, hybrid approaches, model selection, metal earning, feature selection 5. Applications in the field of Bioinformatics: analysis of gene expression data, gene prediction, protein structure prediction, etc.
Lectures: Practical classes: Evaluation: written tests ; practical tasks in groups
Análise Inteligente de Dados – Algoritmos e Implementação em Java; M. Rocha, P. Cortez, J. Neves; FCA; 2008 I. Witten, E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, 2nd edition, Morgan Kaufman, 2005. T. Mitchell, Machine Learning, McGraw Hill, 1997.
1 Identify and search information on the major Bioinformatics databases 2 Identify, use and critically evaluate the main available Bioinformatics tools 3 Use scientific computing systems to solve problems in Bioinformatics, namely in experimental data analysis 4 Know how to define new functions in scientific computing systems and implement scripts for the solution of specific problems
1. Bioinformatics tools and databases - sequence databases - molecular biology tools - sequence alignment/ homology tools, motifs and phylogenetic analysis - protein analysis tools 2. Scientific computing systems and their use in Bioinformatics - introduçtion to Matlab - tools for biochemical engineering in MatLab - introduçtion to the R system - biological data analysis with R/ Bioconductor - programming new functions in R and Matlab
Practical classes in the computer lab Evaluation methods: practical test and performance on classes
d- D. Mount, Bioinformatics: Sequence and genome analysis, Cold Spring Harbor Laboratory Press, 2nd edition, 2005 - A. Baxevanis, F. Ouellete (Eds) Bioinformatics: A Practical Guide to the Analysis of genes and proteins, Wiley, 2004