Subject

Introduction to Bioinformatics

  • code 13489
  • course 2
  • term Semester 2
  • type OB
  • credits 6

Matter: BASIC HEALTH INFORMATICS TOOLS

Main language of instruction: Spanish

Other languages of instruction: Catalan, English

Teaching staff

Head instructor

Dr. Jordi VILLÀ - jvilla@uic.es

Office hours

Interaction with the lecturer will be done upon request by e-mail: jvilla@uic.cat

Introduction

As a graduate in Biomedicine, one should be familiar with the techniques that are available in the area of Bioinformatics to obtain information from the sometimes disperse, most of the times huge amount of data in biology and, in particular, molecular and cell biology, that is available for a scientist or a professional to make sense of the connection of nucleic acids and proteins with biological processes and diseases. Nonetheless, the work of a biomedical graduate is going to incorporate more and more the use of computers to analyze biological data. This course will give you a first extremely practical view of this connection, and will provide tools for the initial steps to the fascinating world of analyzing and making sense of biological data.

Pre-course requirements

The course will be strongly based on the use of your personal computer. Familiarity with the use of development tools is a plus, although we will make it sure to allow every student to enter in the subject in a smooth way.

The course will involve basic elements of mathematics, statistics and programming, as well as proficiency with web browsing.

Objectives

The global objective is to obtain an initial glimpse of the use of computers to analyze biological data within biomedicine. This will be achieved by making extensive use of public biomedical databases as well as by learning the basis of programming with Python and getting familiar with R statistical package for simple analyses through an essentially practical approach and a flipped classroom teaching schema. The specific objectives are:

  1. To obtain a comprehensive view of the impact of big data and how it is changing biology and biomedicine.
  2. To learn and master the major public databases available for biological data with a bias towards biomedical research.
  3. To understand and to apply, in a critical way, the major algorithmic and computational techniques available for the study of genes and proteins.
  4. To gain familiarity with the use of Python and R as main tools for the development of solutions to bioinformatics problems.
  5. To be able to produce graphical views of complex information.
  6. To understand the relevance of open access and open science in a connected 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 conduct 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 gene and genome bioinformatics
  3. Algorithms and tools for structural bioinformatics
  4. Expression, epigenomics, and other cool stuff

 

Lab:

  1. Getting started with Biological databases
  2. Getting started with Conda, Python and R
  3. Developing and applying a solution in a typical bioinformatics problem

 

PBL: 

  1. Meeting the problem
  2. Problem analysis
  3. Discovery and reporting
  4. Solution presentation and discussion
  5. Overview, integration and final evaluation

Teaching and learning activities

In person

The subject is divided into three main activities:

  1. Background sessions, based on lecturers by the professor and presentations by the students.
  2. Practical sessions in which the development of computational tools will lead to a final delivery of a practical solution to a proposed problem.
  3. Problem based learning sessions in which student teams will collectively study and develop a bioinformatics based solution for a proposed challenge.

 

The whole subject is based on a flipped classroom schema, in which the students will have to work to prepare the content of the next sessions, and in some cases they will have to expose their learnings to their peers in a collaborative and critical thinking schema.

Evaluation systems and criteria

In person

The evaluation will be based in the following items:

  1. Written exams:
    1. Partial (March 6th): 15% of the final grade
    2. Final (including the whole course material) (June 4th): 25% of the final grade
  2. Individual term paper (including a specifically developed computational tool and its application to a given bioinformatics problem) (May 11th): 30% of the final grade
  3. PBL group presentation (April 22nd): 30% of the final grade

 

To pass the subject the student must obtain a minimum grade of 5 in each of the mentioned items (Partial, Final, Individual Term Paper and PBL). If the student does not reach 5 points in items 1a and 1b, she can take a final multiple choice Exam in June 25th to improve her grades. There is no second opportunity 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

Evaluation period

E: exam date | R: revision date | 1: first session | 2: second session:

  • E1 04/06/2020 14:00h
  • E2 25/06/2020 11:00h
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