| Study programme competencies |
| Code | Study programme competences |
| A1 | CE1 - Ability to know the scope of Bioinformatics and its most important aspects |
| A2 | CE2 – To define, evaluate and select the architecture and the most suitable software for solving a problem in the field of bioinformatics |
| A3 | CE3 – To analyze, design, develop, implement, verify and document efficient software solutions based on an adequate knowledge of the theories, models and techniques in the field of Bioinformatics |
| A6 | CE6 - Ability to identify software tools and most relevant bioinformatics data sources, and acquire skill in their use |
| A8 | CE8 - Understanding the basis of the information of the hereditary material, its transmission, analysis and evolution |
| A9 | CE9 – To understand the benefits and the problems associated with the sequencing and the use of biological sequences, as well as knowing the structures and techniques for their processing |
| B1 | CB6 - Own and understand knowledge that can provide a base or opportunity to be original in the development and/or application of ideas, often in a context of research |
| B2 | CB7 - Students should know how to apply the acquired knowledge and ability to problem solving in new environments or little known within broad (or multidisciplinary) contexts related to their field of study |
| B8 | CG3 - Be able to work in a team, especially of interdisciplinary nature |
| C6 | CT6 - To assess critically the knowledge, technology and information available to solve the problems they face to. |
| C7 | CT7 – To maintain and establish strategies for scientific updating as a criterion for professional improvement. |
| Learning aims | |||
| Learning outcomes | Study programme competences | ||
| To know the main state-of-the-art data structures for the compact and self-indexed representation of sequences, and algorithms to manage them. | AJ1 AJ2 AJ9 |
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| To create compressed data structures to develop analysis and alignment tasks on sequences efficiently in time and space. | AJ2 AJ3 AJ6 AJ8 |
BJ1 BJ2 BJ8 |
CJ6 CJ7 |
| To know the main issues associated secondary and tertiary protein structure prediction and their importance, as well as the main prediction techniques in the state of the art. | AJ1 AJ2 AJ3 AJ6 AJ9 |
BJ1 |
CJ6 CJ7 |
| Contents |
| Topic | Sub-topic |
| Compresión de secuencias biolóxicas | Lempel-Ziv Grammar-based compression |
| Biological sequence indexing | Burrows-Wheeler Transform FM-index Search and assembly applications |
| Succinct representation of graphs and biological networks | Data structures for compact graph representation Representation of biological networks Applications to biological sequences |
| Protein structure prediction |
Basic concepts on proteins Secondary structure prediction with machine learning techniques Tertiary structure prediction Protein folding models |
| Planning | ||||
| Methodologies / tests | Competencies | Ordinary class hours | Student’s personal work hours | Total hours |
| Guest lecture / keynote speech | A1 A2 A3 A6 A8 A9 | 11 | 11 | 22 |
| Mixed objective/subjective test | A1 A2 A3 A6 A8 A9 B2 | 4 | 0 | 4 |
| ICT practicals | A1 A2 B1 B2 B8 C6 C7 | 10 | 38 | 48 |
| Personalized attention | 1 | 0 | 1 | |
| (*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students. | ||||
| Methodologies |
| Methodologies | Description |
| Guest lecture / keynote speech | Lectures where the course contents are exposed |
| Mixed objective/subjective test | Test to show that the student has acquired the knowledge and skills required during lectures and practice sessions |
| ICT practicals | Students will complete, individually or in groups, different practical exercises to develop the concepts acquired in the lectures |
| Personalized attention |
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| Assessment | |||
| Methodologies | Competencies | Description | Qualification |
| Mixed objective/subjective test | A1 A2 A3 A6 A8 A9 B2 | Constará dunha proba na que deben ser demostrados os coñecementos e competencias adquiridos. Para aprobar a materia globalmente hai que obter unha NOTA MÍNIMA de 1 (sobre 2) nesta proba. Non sendo así, a nota máxima global da materia non será en ningún caso superior a 4,0 e a materia considerarase suspensa. |
20 |
| ICT practicals | A1 A2 B1 B2 B8 C6 C7 | Os estudantes deberán entregar boletíns cos resultados das prácticas realizadas ou solución aos problemas propostos. | 80 |
| Assessment comments | |||
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FIRST OPPORTUNITY
Sudents that do not take the test will obtain a grade of "Non presentado" (absent) SECOND OPPORTUNITYOnly students that have not passed the course in the first opportunity can be evaluated in the second opportunity. In the second opportunity, students that do not retake any part will obtain a grade of "Non presentado" (absent) ADVANCED OPPORTUNITY: The assessment for the advanced opportunity will consist of a written exam that will compute for the 100% of the grade, and will include the knowledge and skills acquired during lectures and practice sessions. ACADEMIC DISPENSATION: Students enrolled part-time with official dispensation from attending classes must contact the teachers within the first two weeks of the course to establish the condition for submitting and defending the practical exercises. PRIMEIRA OPORTUNIDADE Oportunidade ganar |
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| Sources of information |
| Basic |
N. C. Jones, P. A. Pevzner (2004). An introduction to bioinformatics algorithms. MIT Press
V. Mäkinen, D. Belazzougui, F. Cunial, A.I.Tomescu (2015). Genome-scale algorithm design. Cambridge University Press
A. Tramontano (2006). Protein structure prediction: Concepts and Applications. Wiley-VCH |
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| Complementary |
T.K. Attwood, D.J. Parry-Smith (2002). Introducción a la bioinformática. Pearson educacion |
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| Recommendations | |
| Subjects that it is recommended to have taken before | |
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| Subjects that are recommended to be taken simultaneously |
| Subjects that continue the syllabus |
| Other comments | |