FIRST OPPORTUNITY:

Students that do not take the written exam will obtain a grade of "Non presentado" (Absent).

SECOND OPPORTUNITY:

Only those students that have not passed the course in the first opportunity can be evaluated in the second opportunity. Students can recover any of the parts as follows:

ICT practicals (50%): the students can repeat the ICT practicals under the same circumpstances than in the first opportunity (those submitted out of time can obtain a maximum of 80% of the grade). Thus, in case of repeating all the assignments, the maximum grade will be 4 points.
Supervised project (20%): in the same conditions as in the first opportunity.
Written test (30%): in the same conditions as in the first opportunity.
In case of not retaking one of the parts, the grade obtained in the first opportunity for that part will be kept.
To pass the course globally it is necessary to obtain in the mixed test a minimum grade of 1.5 (over 3).
Students that do not retake any part will obtain a grade of "Non presentado" (Absent).

ADVANCED OPPORTUNITY:

The assessment for the advanced opportunity is equivalent to that of the first opportunity (50% ICT practicals, 20% supervised project, which must be done individually, and 30% written text).

ACADEMIC DISPENSATION:

Students officially enrolled part-time who have been granted an official dispensation from attending classes, as stipulated in the regulations of this University, must contact with the responsible of the course within the first two weeks to establish the conditions for submitting and defending the practical exercises and the supervised project.

Learning outcomes	Study programme competences
To know the data structures and the algorithms used for compactly storing and processing of biological sequences.	AJ1 AJ2 AJ9
To analyze and compare the data structures and the complexity of the algorithms used.	AJ2 AJ3	BJ1	CJ6 CJ7
To understand, analyze, design and implement solutions for different fundamental problems of sequence alignment, read error correction, contig assembly, gap filling, etc.	AJ1 AJ2 AJ3 AJ8 AJ9	BJ1 BJ2 BJ8	CJ6 CJ7
To explain, analyze, design and implement solutions to the problems related with evolution, such as haplotype assembly, motif finding, permutation patterns, genomic rearrangement, etc.	AJ1 AJ2 AJ3 AJ8 AJ9	BJ1 BJ2 BJ8	CJ6 CJ7

Topic	Sub-topic
Introduction to algorithms complexity analysis	Algorithms analysis Complexity
Sequence pattern search	Exact string matching methods Approximate string matching methods Suffix trees and suffix arrays
Introduction to sequence compression and indexing	Compression techniques Indexes and self-indexes
Applications to biological sequences	Sequence comparison Motif finding Genomic rearrangements Sequence alignment Sequence assembly Phylogenetic analysis

Methodologies / tests	Competencies	Ordinary class hours	Student’s personal work hours	Total hours
ICT practicals	A2 A3 B1 B2 B8 C6 C7	14	60	74
Supervised projects	A1 A2 A3 A8 A9 B1 B2 B8 C6 C7	3	30	33
Mixed objective/subjective test	A1 A2 A3 A8 A9 B2	0	5	5
Guest lecture / keynote speech	A1 A2 A3 A8 A9	28	10	38

Personalized attention		0	0	0

(*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students.

Methodologies	Description
ICT practicals	Students will complete practical exercises to develop all the knowledge acquired during lectures.
Supervised projects	Students will develop a work, individually or in small group, under the supervision of the teachers.
Mixed objective/subjective test	It consists of a written test to show that the student has acquired the knowledge and skills during lectures and practice sessions.
Guest lecture / keynote speech	Lectures where the course contents are exposed.

Methodologies	Competencies	Description	Qualification
Mixed objective/subjective test	A1 A2 A3 A8 A9 B2	It will consist of a written test where the students must prove the knowledge and competences acquired during lectures and practice sessions. To pass the course globally it is necessary to obtain in the mixed test a minimum grade of 1.5 (over 3). If that minimum grade is not achieved, the maximum grade cannot exceed 4.9 (and therefore the course is failed)	30
Supervised projects	A1 A2 A3 A8 A9 B1 B2 B8 C6 C7	Students must complete a project, individually or in small groups, related with a scientific article. It must be presented in front of the teaching staff.	20
ICT practicals	A2 A3 B1 B2 B8 C6 C7	The work done by the students during practice sessions will be assessed. Students must submit bulletins with their solutions to proposed problems and defend them in front of the teaching staff.	50

Assessment comments
FIRST OPPORTUNITY: Students that do not take the written exam will obtain a grade of "Non presentado" (Absent). SECOND OPPORTUNITY: Only those students that have not passed the course in the first opportunity can be evaluated in the second opportunity. Students can recover any of the parts as follows: ICT practicals (50%): the students can repeat the ICT practicals under the same circumpstances than in the first opportunity (those submitted out of time can obtain a maximum of 80% of the grade). Thus, in case of repeating all the assignments, the maximum grade will be 4 points. Supervised project (20%): in the same conditions as in the first opportunity. Written test (30%): in the same conditions as in the first opportunity. In case of not retaking one of the parts, the grade obtained in the first opportunity for that part will be kept. To pass the course globally it is necessary to obtain in the mixed test a minimum grade of 1.5 (over 3). Students that do not retake any part will obtain a grade of "Non presentado" (Absent). ADVANCED OPPORTUNITY: The assessment for the advanced opportunity is equivalent to that of the first opportunity (50% ICT practicals, 20% supervised project, which must be done individually, and 30% written text). ACADEMIC DISPENSATION: Students officially enrolled part-time who have been granted an official dispensation from attending classes, as stipulated in the regulations of this University, must contact with the responsible of the course within the first two weeks to establish the conditions for submitting and defending the practical exercises and the supervised project.

Basic	Dan Gusfield (1997). Algorithms on Strings, Trees and Sequences. Cambridge University Press Neil C. Jones, Pavel A. Pevzner (2004). An Introduction to Bioinformatics Algorithms. MIT Press Veli Mäkinen, Djamal Belazzougui, Fabio Cunial, Alexandru I. Tomescu (2015). Genome-Scale Algorithm Design. Cambridge University Press

Complementary	Enno Ohlebusch (2013). Bioinformatics Algorithms: Sequence Analysis, Genome Rearrangements, and Phylogenetic Reconstruction. Oldenbusch Verlag A. Moffat y A. Turpin (2002). Compression and Coding Algorithms. Kluwer Academic Publishers G. Navarro y M Raffinot (2002). Flexible Pattern Matching in Strings. Cambridge University Press T. C. Bell, J. G. Clearly y I. H. Witten (1990). Text Compression. Prentice Hall

Other comments