Identifying Data 2018/19
Subject (*) Data structures and algorithmics for biological sequences Code 614522013
Study programme
Mestrado Universitario en Bioinformática para Ciencias da Saúde
Descriptors Cycle Period Year Type Credits
Official Master's Degree 2nd four-month period
First Obligatory 6
Language
Spanish
Galician
English
Teaching method Face-to-face
Prerequisites
Department Computación
Coordinador
Ladra González, Susana
E-mail
susana.ladra@udc.es
Lecturers
Ladra González, Susana
E-mail
susana.ladra@udc.es
Web
General description A materia introduce algoritmos e estruturas de datos conmunmente utilizados no ámbito da bioloxía computacional.


Competencies
STUDY PROGRAMME COMPETENCES
TypeA Code  
  Job guided
  AJ1 CE1 - Ability to know the scope of Bioinformatics and its most important aspects
  AJ2 CE2 – To define, evaluate and select the architecture and the most suitable software for solving a problem in the field of bioinformatics
  AJ3 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
  AJ8 CE8 - Understanding the basis of the information of the hereditary material, its transmission, analysis and evolution
  AJ9 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
TypeB Code  
  Job guided
  BJ1 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
  BJ2 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
  BJ8 CG3 - Be able to work in a team, especially of interdisciplinary nature
TypeC Code  
  Job guided
  CJ6 CT6 - To assess critically the knowledge, technology and information available to solve the problems they face to.
  CJ7 CT7 – To maintain and establish strategies for scientific updating as a criterion for professional improvement.

Learning aims

Contents
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

Planning
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
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.

Personalized attention
Methodologies
Supervised projects
ICT practicals
Description
There may exist differences among the students regarding their background on algorithms and data structures. Thus, teachers will provide personalized attention for practice sessions and for the supervised project, both individual or in small groups.

Assessment
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 orally. For the second opportunity the defenses will be done with a written test. 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 orally. For the second opportunity the defenses will be done with a written test. 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%): students must submit the exercises bulletins and defended them in a written test.
  • Supervised project (20%): the defense of the project will be done using a written test.
  • 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 will consist of a written exam that will compute for the 100% of the grade, and will include all the knowledge and skills acquired during lectures, practice sessions and supervised project.

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.


Sources of information
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
G. Navarro y M Raffinot (2002). Flexible Pattern Matching in Strings. Cambridge University Press
A. Moffat y A. Turpin (2002). Compression and Coding Algorithms. Kluwer Academic Publishers
T. C. Bell, J. G. Clearly y I. H. Witten (1990). Text Compression. Prentice Hall



Recommendations
Subjects that it is recommended to have taken before
Introduction to molecular biology/614522004
Genetics and molecular evolution/614522005
Genomics/614522006
Fundamentals of bioinformatics/614522008
Introduction to programming/614522001

Subjects that are recommended to be taken simultaneously

Subjects that continue the syllabus
Advanced processing of biological sequences/614522020
New trends and applications in bioinformatics and biomedical engineering/614522021

Other comments


(*)The teaching guide is the document in which the URV publishes the information about all its courses. It is a public document and cannot be modified. Only in exceptional cases can it be revised by the competent agent or duly revised so that it is in line with current legislation.