Introduction to Bioinformatics

Credits
6
Types
Compulsory
Requirements
This subject has not requirements , but it has got previous capacities
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

Others

Weekly hours

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

Competences

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, S7, S10,
    2. Exposure to practical cases of biological problems and their solution using bioinformatics tools
      Related competences: K1, K2, K5, K7, S7, S10,
    3. Introduction to basic statistics and notions of probability.
      Related competences: K1, K2, K5, S7, S10,

    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
    0h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    0h

    R exam


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

    Practical exam bioinformatic cases


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

    Final exam


    Objectives: 1 2 3
    Week: 17
    Theory
    0h
    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

    Web links