Bioinformatics Laboratories

Code:

BIOINF1001

Acronym:

BIOINF1001

Keywords
Classification	Keyword
OFICIAL	Bioinformatics

Instance: 2024/2025 - 2S

Active?	Yes
Responsible unit:	Department of Computer Science
Course/CS Responsible:	Bachelor in Bioinformatics

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
L:BIOINF	34	Official Study Plan	1	-	6	48	162

Teaching language

Suitable for English-speaking students

Objectives

The aim of this course is to provide students with an introduction to the techniques, tools and practical resources available for bioinformatics. Students should acquire skills for the selection and advanced use of the most appropriate tools to carry out research activities, including access to the main public sequence databases, advanced search and retrieval of gene expression datasets, differential expression analysis and enrichment functional, protein sequence alignment. Know open source repositories (e.g. CRAN, Bioconductor, GitHub), develop scripts and pipelines for automatic data processing using the Bash and Python programming languages, in particular the tools available in the BioPython modules. Students will also develop skills for a critical analysis of the results obtained, their validation and communication.

Learning outcomes and competences

The course will address the fundamental concepts of molecular biology (e.g. genomes, genes, proteins, gene expression) and their computational representation. It will present the main tools and databases used today in research and industry, which are freely accessible. The students will acquire a relatively in-depth technical knowledge of the use, potential problems and applications of tools and techniques. Tools to automate biological data analysis tasks allowing for their reproducibility will be presented. Students will acquire a critical and integrated skills to analyze biological sequences and their structures.

Working method

Presencial

Pre-requirements (prior knowledge) and co-requirements (common knowledge)

Knowledge of programming in the Python language and working in a command line environment on the Linux system.

Program

• Primary and curated biological sequence databases (e.g. GenBank, European Nucleotide Archive, UniProt).


• Biological sequence representation formats.


• Reference sequence annotations including genomic sequences, transcripts and proteins (e.g. Refseq, Gencode)


• Genomic browsers (UCSC or Ensembl Genome Browser).


• Protein Structure Databases (PDB).


• Tools for generating, formatting and analyzing DNA and protein sequences (e.g. format conversion, transcription, translation, open reading frames, primer finding, gene and pattern search).


• Infer similarity between sequences with the BLAST software package.


• Creation and Analysis of Multiple Protein Alignments (e.g. CLUSTAL, MAFFT).


• Phylogenetic Analysis.


• Development of Scripts for automating data analysis using the Scripting Bash language and BioPython's computational molecular biology tools.

Mandatory literature

David W. Mount; Bioinformatics. ISBN: 9780879697129
Andreas D. Baxevanis; Bioinformatics. ISBN: 0-471-38391-0

Complementary Bibliography

S. Choudhuri; Bioinformatics for Beginners: Genes, Genomes, Molecular Evolution, Databases and Analytical Tools (1st Edition), Academic Press , 2014
Baxevanis, G. D. Bader, D. S. Wishart (Editors); Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins 4th Edition, Wiley, 2020

Teaching methods and learning activities

Lectures with an expository component on the main concepts and fundamentals and a discussion component. In practical classes, research and application work will be carried out with a prototyping component of a project that includes all the contents of the course.



Assessment: distributed without a final exam

• Two Mini Tests: weight 40% (8 points).
• A project design work that integrates the different contents: weight 60% (12 points). The quality, originality and implementation of the work corresponds to 50 per cent of the overall mark and its presentation will have a weight of 10 per cent of the overall mark.

Students must have a minimum mark of 7 in both components, otherwise they will fail for lack of component.

The integrative project must be carried out in groups of three or four students and must be proposed by the students and discussed with the course leader.

Software

linux
python

Evaluation Type

Distributed evaluation without final exam

Assessment Components

designation	Weight (%)
Apresentação/discussão de um trabalho científico	10,00
Trabalho prático ou de projeto	50,00
Teste	40,00
Total:	100,00

Amount of time allocated to each course unit

designation	Time (hours)
Apresentação/discussão de um trabalho científico	2,00
Elaboração de relatório/dissertação/tese	40,00
Frequência das aulas	48,00
Trabalho de investigação	22,00
Trabalho laboratorial	50,00
Total:	162,00

Eligibility for exams

To obtain attendance, the following requirements must be met:
- mini test realization
- delivery and presentation of integrative project.

Calculation formula of final grade

N = 0.2xMT1 +0.2xMT2 + 0.5xP + 0.1xA

MT1 - Nota Mini Test 1
MT2 - Nota Mini Test 2
P - Weighted average (per student's contribution to the project) of the Integrator Project
A - Final presentation of the integrative project
N- Final Grade

Special assessment (TE, DA, ...)

Students with special circumstances should discuss their situation with the responsible of the course.

Classification improvement

There is no possibility of improvement.

Observations

Curricular Unit Jury:

- Pedro Ferreira
- Miriam Santos
- Hélder Oliveira