Tutorials

Fundamentals of Sequence Alignment
Introduction to Sequence Alignment. Significance of Alignment: biological soundness. Measure of Sequence Similarity. Pairwise Alignment. Dynamic programming for optimal global and local pairwise alignment, GAP penalties, Affine GAP approach. Protein Sequence Alignment and substitution Matrices: BLOSUM, PAM. Introduction to Multiple Sequence Alignment (MSA). Computational approaches to MSA. Progressive alignment. ClustalW. Improving alignment guide tree using randomization: AntiClustAl, MUSCLE.

Advanced Topics in Sequence Alignment
Consistency based Multiple Sequence Alignment. Progressive consistency MSA: T-Coffee. Probabilistic Consistency based approach: PROBCONS. Other approaches to guide global alignment: DIALIGN, ALIGN-M, SATCHMO, POA. Exploiting heterogeneous information, secondary and tertiary structures: SPEM, 3D-Coffee. Use discriminative learning techniques to produce better pairwise alignments: CONTRAlign. Combining heterogeneous information in a modular approach to improve alignment quality: RAINBOW. Validation of MSA. Scoring systems. Standard de facto benchmarks: BAliBASE, OXBENCH, PREFAB, SABmark. Choosing a program for MSA. Comparing output to find regions of agreement: ALTAVIST.

Dr. Stefano Forte, University of Catania

Microarray Technology
Microarray technologies and transciptome analysis. The concept of holism in biological sciences and in system biology and the need for "omics" in modern life sciences. Platforms for gene chips (cDNA, oligo and affimetrix chips): features, technologies and limits. Custom microarray production: RNA extraction and subsequent cDNA retrotranscription for cDNA microarray or oligo design for oligo arrays; surfaces chemistry, buffer selection, spotting procedures using robotic platforms, crosslinking and phisical post-processing. Slide scanning, with spot identification and background correction. Main software and hardware platform for image scanning and processing. Data pre-processing techniques for bias correction.

Prof. Raffaele Giancarlo, University of Palermo

Microarray Data Analysis
Statistics, Algorithms and intrinsic limitations of the technology. Cluster Analysis as a three step process: normalization and distance function selection, algorithm selection and parameter setting, selection of validation techniques. Distance and Similarity Functions. Connections with Sequence Alignment Methods. Fundamental Clustering Methods: Hierarchical, K-means. Advanced Clustering Methods: CAST, CLICK. Internal Validation Techniques: FOM, Consensus, Gap Statistics, Silhuette. External Validation Techniques or how good are clustering algorithms: The F-index, the Adjusted Rand Index. The need for Benchmark Data sets. "One Stop Shop" software systems for Microarray Data Analysis.

Prof. Concettina Guerra, University of Padova and Georgia Tech.

Protein-Protein Interactions and Protein Networks
Principles of protein-protein interaction. Prediction of protein-protein interfaces: combining geometry and sequence data. Recognition of bindind sites. 3D Shape descriptors: shape histograms, shape signatures, spherical harmonics. Basic techniques for shape recognition and retrieval. Similarity distances. The structure of biological networks. Review of such concepts as degree distributions, scale-free networks, clustering coefficients. Protein network alignment. Detecting network motifs to identify conserved functional modules in multiple species.

Dr. Rosalba Giugno, University of Catania

Network Analysis Systems for Biomedical Applications
Since many biological systems arise from complex interactions between components (people, cells, proteins, DNA, RNA and molecules), biomedical analysis requires the analysis of complex inter and intra cellular networks. Such networks are naturally modeled as large graphs. Cytoscape, a software platform for the visualization of biomedical interaction networks is introduced. In a given network, locating subgraphs is useful to characterize or to understand specific functionalities. NetMatch, a Cytoscape plugin which allows searches for all subcomponents of the network matching a given query is described. Finally, an overview of the main Cytoscape plugins such as cPath (a software for collecting, storing, and querying pathways) and Metabolica (a software for searching motifs in pathways) is given.