Gestión Académica

Computational Biology is an optional subject that belongs to the IV Module: Applied Biology and the vehicular language is English. It is a multidisciplinary subject in which two different departments are involved: the Department of Biochemistry and Molecular Biology, and the Genetics Area within the Department of Functional Biology. The main aim of this subject is that the student acquires abilities and knowledge to be able to properly analyze biological data through the use of bioinformatic tools. The subject will cover the analysis of the most commonly used biological data, such as genomes, DNA and protein sequences, expression data,… in order to answer different biological questions (identify the molecular causes of a disease, understand evolutionary processes, biomarker identification,…). Students will be able to fetch genomic information, compare sequences, or perform clustering analysis of data.

The vehicular language for lectures, practicum and tests is English. Therefore, the students need to be English proficient to be able to follow the subject. In addition, the students are supposed to have enough knowledge of Structural Biology, Molecular Biology and Genetics, as well as basic understanding of Statistics. Students are also required to have intermediate knowledge of computer use (either Windows, Mac or Linux), and MS Excel or equivalent for data analysis and representation.

Competences:

CG1.- Autonomous learning and confidence.

CG2.- Analysis and synthesis skills, to have an integrated vision of knowledge.

CG3.- Application of biological knowledge to the professional world, being able to elaborate and defend arguments for responsible decision-making.

CG4.- Solving problems related with Biology in an efficient and innovative way.

CG6.- Ability to obtain and interpret relevant data, and being able to provide critical and reasoned assessment, including reflections on social, scientific and ethical subjects related with the information obtained.  

CG7.- Capacity to transmit the information acquired, and to debate ideas, problems and solutions about Biology, both orally and in writing, in front of experts or non-expert audience.

CG8.- Be able to employ international information sources, as well as to communicate in other languages of relevance in international scientific instances.

CG9.- Team working ability and capacity to create groups of interdisciplinary nature, as well as to obtain alternative points of view and arrive to consensual conclusions.

CG10.- Developing the necessary skills to undertaken further studies with a high degree of autonomy.

CG11.- Basic skills on communication and information technologies to achieve an adequate capacity of information management.

CG13.- Acquiring ethical compromise and responsibility as citizens and professionals, especially in environmental and quality of life subjects

CE29.- To know how to perform phylogenetic analysis.

CE33.- To know how to identify and analyze material of biological origin, and its anomalies. CE34.- To know how to manipulate genetic material, and how to carry out genetic analysis and genetic assessment.

CE37.- To know how to conduct bioassays and biological diagnostics.

CE49.- To know how to design experiments, obtain information and interpret the results.

Learning results:

By the end of this course, the student will be able to:

- Use sequence databases, compare nucleic or protein sequences against different biological databases and choose the appropriate tools for each type of study.

- Use bioinformatic tools to design specific primers for PCR, and interpret DNA sequences.

- Analyze the human genome, or from other species, and integrate information from difference sources.

- Basic analysis of next generation sequencing data.

- Experimental design and analysis of gene expression data.

- Use of the Linux operative system at a user level and use basic Python scripts to analyze biological data, automate routines and visualize complex data.

- Create multiple sequence alignments and phylogenetic trees.

- Use specific software to study evolutionary parameters and to identify genes or loci under positive or negative selection.

1.- Computational Biology Introduction. Biologial data: type of data, formats and biological data bases. Use of biological sequence data bases.

2.- Sequence comparison: simple aligment (global and local). Use of sequence alignment software (BLAST, BLAT,…). Use of alignment algorithms to identify novel genes.

3.- Introduction to the Linux operative system and Python programming language for the processing of biological data.

4.- Data mining. Analysis of eukaryotic genomes. Types of information. Application of BioMart and Ensembl to analyze the human genome. Data integration by using information from different data bases.

5.- Primer design and sequence analysis. Bioinformatic tools for the automated design of primers for PCR amplification. Tools for the analysis of Sanger sequencing data. Sequence interpretation.

6.- Clusterization methods: K-Means and Hierarchical clustering

7.- Dimensionality reduction: Principal Component Analysis. Application to large datasets. Differential gene expression analysis. Multiple testing correction.

8.- Multiple sequence alignment. Position matrices (PAM, BLOSUM,…). Position-specific iterated BLAST (PSI-BLAST). Hidden Markov Models. Protein family predictions and structural domains. Search for novel genes using Hidden Markov Models.

9.- Evolutionary bioinformatics. Distance, parsimony and maximum likelihood methods. Parameters and gaps. Bootstraping. Consensus trees.

10.- Evolutionary models for DNA sequences. Identification of genes under positive or negative selection (Ka/Ks). Coalescence: search and dating ancestral haplotypes.

Due to the ongoing sanitary crisis caused by COVID-19, in case that limitations on the assistance to class are imposed, lectures and practicum will be carried out using online tools such as MS Teams.

The evaluation will be based on different small tests or work assignments (up to 30% of the final score), participation (up to 10%), plus a final written test. This one will test the ability of the student to solve different problems related to the ones studied during the semester, the ability to integrate the different concepts covered during the lectures, as well as to be able to discuss the results obtained.

The scores obtained on the small tests and work assignments will be maintained for the final or extraordinary test during the same academic year.

Due to the ongoing sanitary crisis caused by COVID-19, in case that limitations on the assistance to class are imposed, the exams could be carried out using online tools. In that case, additional assignments will be used to facilitate continuous evaluation, having a higher impact on the final score.

The teachers will use the Virtual Campus to provide different resources and materials to complete the course. Furthermore, a Linux server and a dedicated web page will be available for students to use all bioinformatic tools, as well as step by step instructions with the methodology to use during the computer classes.

- “An Introduction to Bioinformatics”. Arthur Lesk. Oxford, 2008, 3rd Ed.

- Ringnér M (2008) "What is principal component analysis?" Nat Biotech 26:303.

- Noble WS (2009) "How does multiple testing correction work?" Nat Biotech 27:1135.

- “Practical Computing for Biologists”. Steven Haddock, Casey Dunn. Sinauer Associates, 2010.

- "Bioinformatics and Molecular Evolution”. Paul G.Higgs and Teresa K.Atwood. Blackwell Publishing, 2005.

- “Molecular Evolution”. Wen-Hsiung Li. Sinauer Associates, 1997.