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Code: IM2014_18     Acronym: Bioinf

Classification Keyword
OFICIAL Medical Informatics

Instance: 2020/2021 - 2S (of 08-02-2021 to 30-07-2021) Ícone do Moodle Ícone  do Teams

Active? Yes
Responsible unit: Department of Computer Science
Course/CS Responsible: Master Programme in Medical Informatics

Cycles of Study/Courses

Acronym No. of Students Study Plan Curricular Years Credits UCN Credits ECTS Contact hours Total Time
MIM 8 Current Studies Plan 1 - 3 27 81

Teaching Staff - Responsibilities

Teacher Responsibility
Pedro Gabriel Dias Ferreira

Teaching - Hours

Theoretical and practical : 1,00
Tutorial Supervision: 0,71
Other: 0,21
Type Teacher Classes Hour
Theoretical and practical Totals 1 1,00
Pedro Gabriel Dias Ferreira 1,00
Tutorial Supervision Totals 1 0,71
Pedro Gabriel Dias Ferreira 0,71
Other Totals 1 0,21
Pedro Gabriel Dias Ferreira 0,21

Teaching language

Suitable for English-speaking students


Bioinformatics is an interdisciplinary field that combines the fields of computer science, biology and biomedical science and statistics. Bioinformatics is devoted to the application and development of new computational methods for expanding the use of biological, biomedical or epidemiological data. Recent developments in high-throughput technologies have led to a real revolution in the biological and biomedical research with bioinformatics playing a central role in the analysis of massive amounts of data.

The goal of this course is that students understand some of the most relevant problems and tasks in bioinformatics for the analysis of molecular data. Particular emphasis will be given to the analysis of biological sequences. Students will acquire knowledge on the methods, tools and databases that are most appropriate for each task.

Learning outcomes and competences

By the end of this course it is expected that the student:

  • Is familiarized with the main concepts of Bioinformatics including the main concepts on Computational Molecular Biology;
  • Identifies the main sources of biological sequence data (e.g. nucleotide or amino-acid sequences; motifs and domains) and associated types and how can they be represented from a computational point of view.
  • Understands different problems related to sequence analysis and identifies the most adequate algorithms and methods to solve these problems.
  • Have a perspective of Bioinformatics as a field of critical importance to leverage biological, biomedical and health research and as a field of constant and fast-paced development.

Working method



  • Overview of Molecular Biology concepts

  • Bioinformatics resources over the Internet

  • DNA and Protein sequence analysis

  • Database similarity search with BLAST

  • Searching for sequence motifs

  • Genomics and high-throughput sequencing

Mandatory literature

NCBI Tutorials; Training materials in HTML, PDF and Video formats (https://www.ncbi.nlm.nih.gov/home/tutorials/)
N.C. Jones, P. Pevzner; An Introduction to Bioinformatics Algorithms, A Bradford book, London, 2004

Complementary Bibliography

Stephen F. Altschul, Warren Gish, Webb Miller, Eugene W. Myers, David J. Lipman; Basic local alignment search tool, Journal of Molecular Biology 215 (3) (1990) 403–410.
Stephen F. Altschul, Thomas L. Madden, Alejandro A. Schäffer, Jinghui Zhang, Zheng Zhang, Webb Miller, David J. Lipman; Gapped blast and psi-blast: a new generation of protein database search programs, Nucleic Acids Research 25 (17) (1997) 3389–3402.
T.L. Bailey; Discovering sequence motifs, Methods in Molecular Biology 452 (2008) 231–251.
T.L. Bailey, C. Elkan; Fitting a mixture model by expectation maximization to discover motifs inbiopolymers, Proceedings. International Conference on Intelligent Systems for Molecular Biology 2 (1994) 28–36.
Humberto Carrillo, David Lipman; The multiple sequence alignment problem in biology, SIAM Journal on Applied Mathematics 48 (5) (1988) 1073–1082.
M.K. Das, H.K. Dai; A survey of DNA motif finding algorithms, BMC Bioinformatics 8 (Suppl 7) (Nov 2007) S21.
P. D’haeseleer; What are DNA sequence motifs? Nature Biotechnology 24 (4) (Apr 2006) 423–425.
Desmond G. Higgins, Paul M. Sharp; Clustal: a package for performing multiple sequence alignment on a microcomputer, Gene 73 (1) (1988) 237–244.
Temple F. Smith, Michael S. Waterman; Identification of common molecular subsequences, Journal of Molecular Biology 147 (1) (1981) 195–197.

Teaching methods and learning activities

Theoretical classes: expository, accompanied by examples.
Practical classes: use of existing databases and tools.

Evaluation Type

Distributed evaluation with final exam

Assessment Components

designation Weight (%)
Exame 50,00
Trabalho prático ou de projeto 50,00
Total: 100,00

Amount of time allocated to each course unit

designation Time (hours)
Estudo autónomo 27,00
Frequência das aulas 14,00
Trabalho de investigação 40,00
Total: 81,00

Eligibility for exams

Minimum grande in exam (>8 values out of 20) and minimum grade in practical work (>8 values out of 20).

Calculation formula of final grade

The UC grade is determined based on satisfactory progress (minimum grade) in assignments or mini-project, final exam and review and discussion of a scientific paper.

The breakdown of these components is as follows:

  • Exam (E): 40%
  • Assignment or mini-projects (P): 40%

The final classification of the UC is given by the following formula:

 E*50% + P*50%

There will be six practical assignments throughout the class period. The foruth best works are considered for evaluation.

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