Techniques and Tools for Bioinformatics

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Credits
3
Types
Elective
Requirements
This subject has not requirements

Department
CS
In this course the main genomic techniques and tools will be given: exact and approximated string matching , alignment algorithms, phylogenetic algorithms, Blast, Hiddem Markov Models, Genome sequencing, ...

Teachers

Person in charge

  • Xavier Messeguer Peypoch ( )

Others

  • Gabriel Valiente Feruglio ( )

Weekly hours

Theory
2
Problems
1
Laboratory
1
Guided learning
0
Autonomous learning
0

Competences

Technical Competences of each Specialization

Direcció i gestió

  • CDG1 - Capability to integrate technologies, applications, services and systems of Informatics Engineering, in general and in broader and multicisciplinary contexts.

Especifics

  • CTE7 - Capability to understand and to apply advanced knowledge of high performance computing and numerical or computational methods to engineering problems.
  • CTE9 - Capability to apply mathematical, statistical and artificial intelligence methods to model, design and develop applications, services, intelligent systems and knowledge-based systems.

Generic Technical Competences

Generic

  • CG4 - Capacity for mathematical modeling, calculation and simulation in technology and engineering companies centers, particularly in research, development and innovation tasks in all areas related to Informatics Engineering.

Transversal Competences

Solvent use of the information resources

  • CTR4 - Capability to manage the acquisition, structuring, analysis and visualization of data and information in the area of informatics engineering, and critically assess the results of this effort.

Basic

  • CB6 - Ability to apply the acquired knowledge and capacity for solving problems in new or unknown environments within broader (or multidisciplinary) contexts related to their area of study.

Contents

  1. Efficient search algorithms and data structures
    The most efficient string matching algorithms according to the alphabet, the length and number patterns will be given. Also we will discuss useful dat structures for comparing genomes as suffix trees. Finally we will study strategies for exhaustive search in very long DNA sequences(Gb).
  2. Sequence alignment
    The dynamic programming algorithm will be given and its application to some cases: the edit distance between two words, the approximate string matching and the best alignment between two sequences. Finally we introduce the multiple sequence alignment.
  3. Data base searching: BLAST
    The computational and statistical foundations of the BLAST algorithm and its use for approximate searches in databases will be introduced.
  4. Phylogenetic algorithms
    The fundamental algorithms to estimate the evolution of individuals within a species will be explained.
  5. Hidden Markov Models
    The Hidden Markov Models and its applications to bioinformatics will be explained.

Teaching methodology

The course is proposed with 16h of theoretical sessions, to understand the theoretical foundations of the techniques used, and 10h of computer laboratory sessions to know the main tools of the topic.

The theoretical sessions (and problems) will be given on whiteboard with slides..

The laboratory sessions will be devoted to practice with programs designed for the subject.

Evaluation methodology

The course note (NF) has two contributions: a laboratory grade (NL), which becomes from the evaluations of some given exercises in laboratory sessions, and a final exam grade (NE), then

NF=NE*0.7+NL*0.3

Bibliografy

Basic: