Universitat Internacional de Catalunya

Introduction to Bioinformatics

Introduction to Bioinformatics
6
13489
2
Second semester
OB
BASIC HEALTH INFORMATICS TOOLS
Main language of instruction: Spanish

Other languages of instruction: Catalan, English

Teaching staff


Dr. VILLÀ FREIXA, Jordi - jvilla@uic.es

Meetings with the lecturer will be upon request by email: jvilla@uic.cat / Martin Floor (mfloor@uic.es) / Àngel Belmonte (abelmonte@uic.es)

Introduction

In the event that the health authorities announce a new period of confinement due to the evolution of the health crisis caused by COVID-19, the teaching staff will promptly communicate how this may effect the teaching methodologies and activities as well as the assessment.


As a graduate in Biomedicine, you should become familiar with the techniques available in the field of Bioinformatics to obtain information, sometimes from different sources, on molecular and cell biology, which is available to a scientist or professional to enhance the connection between basic biochemistry and biological processes and diseases. This is important enough that the work of a biomedical graduate will increasingly incorporate the use of computers to analyse biological data. This course will give you an extremely practical first exposure to this connection and provide tools for the first steps into the fascinating world of analysing and making sense of biological data. It will also open the door to programming in scripting languages such as Python, an essential tool for developing new applications that exploit that data.

Pre-course requirements

The course will be heavily based on the use of your personal computer. Familiarity with the use of development tools is an advantage, although we make sure to allow all students to move smoothly into the subject. The course will include basic elements of maths, statistics and programming, as well as web-browsing skills.

Objectives

The overall goal is to gain an insight into the use of computers to analyse biological data within biomedicine. This will be achieved by making extensive use of public biomedical databases as well as by learning the basics of programming with Python and becoming familiar with the R statistical package for simple analysis through an essentially practical approach and a schema of flipped classroom teaching, in which the teacher contact sessions will serve to consolidate independent learning.

The specific objectives are:

  • To gain an overview of the impact of the data and how biology and biomedicine are changing.
  • Know and master the main public biological databases available with a bias towards biomedical research.
  • Understand and critically apply the main algorithmic and computational techniques available for the study of genes and proteins.
  • Develop small programmes in Python and R.
  • Produce graphical visualisations of complex information.
  • Understand the relevance of open access and open science in an interconnected research world.

Competences/Learning outcomes of the degree programme

General:

  1. Team work and responsibility
  2. Ability to adapt to complex problems and to make informed decisions

 

Specific:

  1. To acquire ability to understand, to develop and to apply computational workflows to solve complex biological problems.
  2. To understand how data driven research is conducted.
  3. To develop skills for science communication in written and oral forms, making simple what is complex.

Learning outcomes of the subject

  1. Basic knowledge of the existing repertoire of biological databases and algorithms and their importance in solving biomedical problems.
  2. Ability to develop computational tools in Python and R for the analysis of complex biological data to understand biomedical problems.
  3. Ability to carry out team work to produce and communicate scientific research.

Syllabus

The subject is divided into three modules: 

Lectures: 

  1. General introduction: Biology as a data science 
  2. Algorithms and tools for genome bioinformatics 
  3. Algorithms and tools for structural bioinformatics 
  4. Expression, epigenomics and other interesting aspects 

Laboratory: 

  1. How to get started with biological databases 
  2. Conda, Python and R as basic work tools in Bioinformatics 
  3. Develop and apply solutions to common problems in bioinformatics 

PBL: 

  1. Approach problems of some complexity in Bioinformatics and teamwork to come up with an answer based on the development of Python and R applications
  2. Presentation and discussion of solutions 
  3. Overview, integration and final assessment

Teaching and learning activities

In blended



The subject is divided into three main activities: 

  1. Background sessions, based on sessions taught by the teacher and also student presentations. 
  2. Practical sessions in which the development of computer tools will allow the completion of practical exercises to answer a specified problem. 
  3. Problem-based learning sessions in which student teams will collectively study and develop a bioinformatics-based solution for a specified challenge. 

The whole subject is based on a flipped classroom scheme, in which students will have to work to prepare the content for the next sessions and, in some cases, present their learning to classmates in a collaborative and critical thinking context. 

Evaluation systems and criteria

In blended



The assessment will be based on the following items: 

  1. Written exams: 
    1. Partial: 15% of the final mark 
    2. Final (including full course material): 25% of the final mark 
  2. Completion of practical exercises: 30% of the final mark 
  3. Presentation of the PBL group: 30% of the final mark 

To pass the course the student must obtain a minimum mark of 5 on each of the items mentioned (Partial, Final, Practical Exercises and PBL). If the student does not achieve the 5 points on items 1a and 1b, they can take a final multiple-choice exam on June 25 to improve the marks. There is no second chance for items 2 and 3.

Bibliography and resources

The subject is based on strong use of the personal computer. Students are encouraged to get the latest miniconda installation in order to develop the needed tools for the success in the subject. The course will make use of Python and R as the main computational tools in Bioinformatics and public databases for accessing biological data.

Most material will be obtained on-line from public sources that will be made available as the course progresses through the course site.

General introductory texts:

  1. The Processes of Life: An Introduction to Molecular Biology (The MIT Press) ISBN-10: 026251737X

Basic texts in Bioinformatics:

  1. Bioinformatics Algorithms, Vol III http://bioinformaticsalgorithms.com ISBN 13: 9780990374633
  2. Bioinformatics with Python cookbook, 2nd Edition ISBN-10: 1789344697
  3. Bioinformatics Data Skills: Reproducible and Robust Research with Open Source Tools ISBN-10: 1449367372
  4. David W. Mount. Bioinformatics - Sequence and Genome Analysis. Cold Spring Harbor Laboratory Press, New Yor
  5. H. Wickham. R packages. O'Reailly, Sebastopol, 2015.
  6. BURKOWSKI, F. J. Structural Bioinformatics: an algorithmic approach. London: Chapman & Hall / CRC, c2009

Evaluation period

E: exam date | R: revision date | 1: first session | 2: second session:
  • E1 02/06/2021 I3 14:00h
  • E2 28/06/2021 11:00h
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