Universitat Internacional de Catalunya
Biological Databases
Other languages of instruction: Catalan, Spanish
Teaching staff
Introduction
The course is aimed at knowing the different types of databases available in the area of health sciences to solve problems in the field of biomedicine. Throughout the subject, the aspects that are introduced in the subject "Introduction to bioinformatics" will be expanded and its application for access to information will be deepened.
Pre-course requirements
It is recommended to have completed and passed:
- Introduction to bioinformaticsObjectives
Competences/Learning outcomes of the degree programme
CB1 Stundents must demonstrate possession and understanding of knowledge in an area of study that is based on general secondary education, and is usually found at a level that, although supported by advanced textbooks, also includes some aspects that involve knowledge from the forefront of their field of study
CB3 That students have the ability to gather and interpret relevant data (normally within their area of study) to make judgments that include a discussion on relevant issues of a social, scientific or ethical nature
CB4 That students can transmit information, ideas, problems and solutions to both a specialized and non-specialized audience
CB5 That students have developed those learning skills necessary to undertake further studies with a high degree of autonomy
CG7 Integrate the basic concepts related to the field of biomedicine both at a theoretical and experimental level.
CG10 Design, write and execute projects related to the area of Biomedical Sciences
CG11 Recognize basic concepts of different areas related to biomedical sciences.
CT1 Develop the capacity for organization and planning appropriate to the moment.
CT2 Develop the ability to solve problems.
CT3 Develop the capacity for analysis and synthesis.
CT5 Use the Internet as a way of communication and as a source of information.
CT6 Know how to communicate, make presentations and write scientific papers.
CT7 Be able to work in a team.
CT10 Be able to carry out autonomous learning.
Learning outcomes of the subject
The specific learning outcome of this mention are:
- Analyze biomedical problems and identify the aspects that require the use of databases. Obtain information of interest from the main massive databases (Big Data) in the field of Biomedical Sciences.
In addition, other learning outcomes will be that the student:
- Learn about the different types of databases available in the area of health sciences to solve the main problems in the field of biomedicine.
- Learn the basics of collecting, processing and analyzing large volumes of data from biomedical research.
- Learn about the main tools in the field of bioinformatics and the bases of programming languages that allow the extraction of information from databases.
- Knows and adequately uses the scientific, technical or specific vocabulary of the field in which the activity will be carried out.
Syllabus
1) Introduction to biological databases. Use of biomedical data in the field of life sciences. Types of data available in biomedicine. Main databases. Search for information in a database.
2) Bibliographic databases. MEDLINE bibliographic database. MeSH biomedical vocabulary. PubMed search engine. PubMed Central bibliographic repository. Mendeley bibliographic manager.
3) Nucleotide databases: genomes, genes and transcripts. INSDC: NCBI, EMBL-EBI and DDBJ. Genomes, Genes, and Transcripts at NCBI: Entrez, Nucleotide, GenBank, RefSeq; and in EMBL-EBI: ENA and Ensembl. Visualization of genomes at UCSC. Gene nomenclature in HGNC. Description of genes in Gene Cards. Coding sequence in CCDS.
4) Gene expression and regulation databases. Gene expression in GEO, Expression Atlas, Single Cell Expression Atlas and Single Cell Portal. Gene regulation in GTEx and ENCODE.
5) Protein databases. Protein sequence in UniProt, RefSeq and Ensembl. Protein domains in InterPro, PROSITE and Pfam. Protein structures in PDBe, RCSB PDB and AlphaFold. Protein interaction in IntAct, STRING and BioGRID.
6) Network databases. Gene function in Gene Ontology. Metabolic pathways in Reactome and KEGG.
7) Databases of diseases, genetic variability and drugs. Diseases in OMIM. Genetic variability in ClinVar, UniProt, Ensembl and dbSNP. Genetic variability in cancer: TCGA, COSMIC, cBioPortal and OncoKB. Drugs in DrugBank, PubChem, ChEMBL and Open Targets.
Teaching and learning activities
In person
Master classes: presentation for 50 minutes of a theoretical topic by the teacher.
Case methods Approach of a real or imaginary situation. The students work on the questions formulated in active interaction with the teacher and the answers are discussed. The teacher actively intervenes and, if necessary, contributes with new knowledge.
Laboratory practices: Students work individually on the problems raised by the teacher that will have to be solved using different bioinformatics tools.
Evaluation systems and criteria
In person
First call students:
- Case methods: 15%
- Internship reports: 15%
- Partial exam: 30%
- Final exam: 40%
1) In order to make an average, a minimum grade of 5 must be obtained in the final exam.
2) Attendance at case methods and practices is mandatory. The continuous nature of this assessment means that it is not possible to aprove the subject if a minimum of 75% of the hours have not been participated in.
3) The improper use of electronic devices (such as the recording and dissemination of both students and teachers during the different lessons, as well as the use of these devices for recreational and non-educational purposes) may lead to expulsion from class.
4) The expulsion of a student from the classroom may lead to the failure of the subject.
Students in second or subsequent call: the note of the methods of the case and the note of practices will be saved; and the final exam will represent 70% of the final grade. Repeating students who wish to repeat the partial in 3 or 5 calls, may do so by previously communicating it to the head teacher.
Bibliography and resources
https://www.ncbi.nlm.nih.gov/home/tutorials/
https://www.ensembl.org/info/index.html
https://www.ebi.ac.uk/training/online/
Andreas D. Baxevanis (Editor) , B. F. Francis Ouellette (Editor) (2004). Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins (3 ed.). Wiley.
Model, Mitchell L. (2010). Bioinformatics programming using Python (1 ed.). O’Reilly .
Evaluation period
- E1 12/01/2023 I1 16:00h
- E2 19/06/2023 18:00h