Introduction to Bioinformatics

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Credits
6
Types
Compulsory
Requirements
This subject has not requirements
Department
UPF
This subject provides a comprehensive introduction to the foundational concepts, methods, and tools used in the field of bioinformatics. It covers practical computational skills including the use of Linux, Bash, and R programming. Additionally, the course delves into the nature of data in bioinformatics and explores various statistical techniques for data analysis and interpretation. Through hands-on exercises and real-world examples, students will gain a solid understanding of how to manage and analyze biological data effectively.

Teachers

Person in charge

  • Hafid Laayouni el Alaoui ( )

Others

  • Ferriol Calvet Riera ( )
  • Irene Acero Pousa ( )

Weekly hours

Theory
2
Problems
2
Laboratory
0
Guided learning
0
Autonomous learning
6

Learning Outcomes

Learning Outcomes

Knowledge

  • K1 - Recognize the basic principles of biology, from cellular to organism scale, and how these are related to current knowledge in the fields of bioinformatics, data analysis, and machine learning; thus achieving an interdisciplinary vision with special emphasis on biomedical applications.
  • K2 - Identify mathematical models and statistical and computational methods that allow for solving problems in the fields of molecular biology, genomics, medical research, and population genetics.
  • K5 - Identify the nature of the biological variables that need to be analyzed, as well as the mathematical models, algorithms, and statistical tests appropriate to develop and evaluate statistical analyses and computational tools.
  • K7 - Analyze the sources of scientific information, valid and reliable, to justify the state of the art of a bioinformatics problem and to be able to address its resolution.

Skills

  • S7 - Implement programming methods and data analysis based on the development of working hypotheses within the area of study.
  • S10 - Use acquired knowledge and the skills of bioinformatics problem solving in new or unfamiliar environments within broader (or multidisciplinary) contexts related to bioinformatics and computational biology.

Objectives

  1. Acquisition of basic notions of using the Linux operating system, bash language and R
    Related competences: K1, K2, K5, K7, S10, S7,
  2. Exposure to practical cases of biological problems and their solution using bioinformatics tools
    Related competences: K1, K2, K5, K7, S10, S7,
  3. Introduction to basic statistics and notions of probability.
    Related competences: K2, K5, S10, S7, K1,

Contents

  1. Bioinformatics Hand on sessions
    Getting familiar with the black screen (introduction to Linux)
    Bioinformatics databases: Genome browsers, NCBI Genbank, Uniprot, PDB
    Sequence alignment
    Bash commands
    Bash scripting
  2. Introduction to Data Analysis
    The nature and impact of variability in biological data. Observational studies and experiments. Random
    sampling. Description of distributions. Frequency distributions, descriptive statistics, the concept of population versus
    sample. Probability and the binomial distribution. The normal distribution. Sampling distributions. Confidence intervals
    for a single mean and for a difference in means.

Activities

Activity Evaluation act


Theoretical expository lectures


Objectives: 3
Contents:
Theory
25h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
45h

Problem solving sessions


Objectives: 1 2
Theory
0h
Problems
26h
Laboratory
0h
Guided learning
0h
Autonomous learning
45h

Mid term exam


Objectives: 3
Week: 1
Theory
2h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

R exam


Objectives: 1
Week: 13
Theory
0h
Problems
2h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

Practical exam bioinformatic cases


Objectives: 2
Week: 10
Theory
0h
Problems
2h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

Final exam


Objectives: 1 2 3
Week: 17
Theory
3h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

Teaching methodology

Lectures will be mainly of expository type. There will be also problem-based sessions and practical sessions using R.

Evaluation methodology

For the evaluation of the subject, the grade of the partial exam (P), the grade of the final exam (F) and the grade of the practical sessions will be taken into account and will be combined with the following formula:
Grade=max(0.2*P+0.4*Practical+0.4*F; 0.4*Practical+0.6*F)

A student is considered to have taken the subject if he/she takes the final exam. If the student has taken the subject but has failed, then the student may take the re-evaluation exam (RT) and in this case the grade for the subject will be 0.4*Practical+0.6*RT (the partial score is not used).

Bibliography

Basic:

  • Biomedical informatics: computer applications in health care and biomedicine. - , New York, NY : Springer, cop., 2006.
  • Statistics for the life Sciences - M.L. Samuel, J.A. Witmer, A. Shaffner, 2016.

Web links