I will introduce a small number of very popular (and important) Web sites for bioinformatics (NCBI EBI and others). As the sheer volume of sequence data increases exponentially the ability to ‘browse’ the datasets decreases at a corresponding rate. I will introduce heuristic and rigorous tools to locate sequence data.
The fundamental ‘unit’ of bioinformatics is comparison. I will present a number of tools that permit the user to compare, that is biologically align, pairs of nucleotide and protein sequences using publicly available software accessed over the internet.
A discussion of homology (BLAST) searching will also introduce some more specialised tools which ‘encode’ a model of aspects of biology.

intro | list of documents

The lectures by Profs. Laayouni and Navarro aimin to provide an update on the increasingly popular Genome-Wide Association Study (GWAS), the choice large-scale approach to study the genetic architecture of complex diseases. A wide perspective of the field will be provided, with lectures covering from technological and design issues to new data analysis and interpretation strategies. Practical hands-on sessions will familiarize students with the most common softwares in the area.

The goal of this course is to introduce various aspects of protein sequence alignments. We will start introducing the notion of substitution matrix, insisting on the necessary biological relevance of the considered models. We will then introduce Blast, the most commonly used application of pairwise sequence alignments. In the second part of the course, we will introduce the notion of multiple sequence alignments. We will develop the strength and weaknesses of the most widely used strategies like ClustalW. The last part of the course will introduce state of the art methods for the comparison of protein and RNA sequences, including T-Coffee, a software developed in my lab.

In this session students will learn how important is to well build a protein association network as a first step to elucidate protein function in different biological systems. Despite of rapid advances in the field of high throughput experiments to determine protein interactions, the majority of associations remain unknown. In this context, computational predictions will be a valuable approach completing or guiding experimental designs. In this course session we will describe computational methods for significantly expanding protein association networks based on genomics, phylogenetic, homology, protein domain comparison and other signals. We will show methods for integrating multiple independent sources of evidence in order to obtain higher quality predictions and coverage, and we will show the major publicly available resources available for experimentalists to use, making practical hands-on sessions for some of these leading web resources.

1.) Visualization biological data
The lecture will give an overview of widely used visualization tools in biology. I will show some
good and bad examples and will discuss some of the challenges we are facing in todays large scale biology.

2.)  Analyzing large data sets
The lecture will focus on the data streams and analysis pipelines in todays high-throughput biology. 
I will show some examples and discuss the need for intelligent data integration and data mining.

3.) High performance and high throughput computing in biology
The lecture will give an overview of the current and future hardware and software environments and I will discuss the need for bioinformatician to handle these upcoming hard and software challenges.

The Ensembl project (http://www.ensembl.org) provides a comprehensive source of high quality annotation of the human genome sequence, as well as other chordate and selected eukaryotic genomes. The evidence–based annotation produces unique datasets that contain information about genes and transcripts with additional data including multiple species alignments, homology prediction, sequence-specific variations and regulatory regions annotation. The main focus of the workshop will be on how to use the Ensembl genome browser and Biomart.

You will learn how to:

• find a gene or protein of interest
• get additional information and external links
• use the variation, comparative and functional genomics data