Omics Techniques

Credits
6
Types
Compulsory
Requirements
This subject has not requirements , but it has got previous capacities
Department
UAB
The aim of this course is to provide a basic vision of the application of Omic Techniques in different fields with the description of practical examples. The different themes that will be taught include recent advances in sequencing, epigenomics, metagenomics, single-cell genomics, advanced transcriptomic technologies, or proteomics. Maximum priority will be given to the application of the topics introduced in the lectures in different scientific areas, providing a basic training in a broad range of omic techniques. Given the current importance of this field, which is very dynamic and in permanent change, the scope and contents of the course will be subjected to regular updates.

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.
  • K3 - Identify the mathematical foundations, computational theories, algorithmic schemes and information organization principles applicable to the modeling of biological systems and to the efficient solution of bioinformatics problems through the design of 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

  • S1 - Integrate omics and clinical data to gain a greater understanding and a better analysis of biological phenomena.
  • S2 - Computationally analyze DNA, RNA and protein sequences, including comparative genome analyses, using computation, mathematics and statistics as basic tools of bioinformatics.
  • S3 - Solve problems in the fields of molecular biology, genomics, medical research and population genetics by applying statistical and computational methods and mathematical models.
  • S5 - Disseminate information, ideas, problems and solutions from bioinformatics and computational biology to a general audience.
  • S7 - Implement programming methods and data analysis based on the development of working hypotheses within the area of study.
  • S8 - Make decisions, and defend them with arguments, in the resolution of problems in the areas of biology, as well as, within the appropriate fields, health sciences, computer sciences and experimental sciences.

    • Competences

  • C2 - Identify the complexity of the economic and social phenomena typical of the welfare society and relate welfare to globalization, sustainability and climate change in order to use technique, technology, economy and sustainability in a balanced and compatible way.
  • C3 - Communicate orally and in writing with others in the English language about learning, thinking and decision making outcomes.
  • C4 - Work as a member of an interdisciplinary team, either as an additional member or performing managerial tasks, in order to contribute to the development of projects (including business or research) with pragmatism and a sense of responsibility and ethical principles, assuming commitments taking into account the available resources.

    • Objectives

    1. Acquisition of specific knowledge to apply the different technologies and strategies to generate sequencing data (DNA and RNA)
      Related competences: K1, K7, S1, S2, C2, C3,
    2. Application of genomic, transcriptomic and proteomic data within different contexts to solve biological problems
      Related competences: K1, K2, K3, K7, S2, S3, S7, S8, C2, C4,
    3. Understand the experimental and computational methods of genomics, transcriptomics and proteomics
      Related competences: K1, K2, K3, K7, S1, S2, S3, S5, C2, C4,

    Contents

    1. Sequencing technologies
      Generation of sequencing data. Sanger sequencing; 2nd and 3rd generation sequencing techniques (Illumina, Oxford-Nanopore, PacBio).
    2. Applications of DNA-seq
      Sequencing and processing of DNA sequencing data: genome sequencing, re-sequencing and variant calling.
    3. Metagenomics and microbiome
      Applications of metagenomic and microbiome analysis; pipelines and data analysis to study natural environments and health.
    4. RNA sequencing and analysis of RNA-seq data.
      Analysis of RNA-seq data to understand gene expression and its variations across different conditions: quality control, read alignment, quantification, and differential expression analysis.
    5. Applications of RNA-seq
      Gene expression and splicing variant annotation, metatranscriptomics. Applications in different fields: disease diagnosis, drug discovery, and functional genomics.
    6. Epigenomics and epigenetics
      Epigenomics and epigenetic modifications on the genetic material: data processing and analyses for differential DNA methylation.
    7. Hi-C for 3D genome organization.
      The study the three-dimensional structure of genomes to understand how the spatial organization of DNA influences gene expression and other cellular processes. Hi-C protocols and general processing of Hi-C data.
    8. Single-cell genomics.
      Study of the genetic material (genome, transcriptome) of individual cells: applications and analysis.
    9. Proteomics.
      Methodological foundations of the techniques to study of the protein composition (proteome) within a cell or organism. Special emphasis will be placed on the technologies developed for protein identification, including mass spectrometry and DIGE technology, and their applications in biomedicine.

    Activities

    Activity Evaluation act




    Teaching methodology

    - Theoretical classes. Lectures will address the main concepts behind the different topics. They will consist of question-based lectures that promote an active participation of all students in the discussion of different topics/situations/problems/cases that will be presented.

    - Practicals. Hands-on problem-based tutorials. They will promote both self-study and teamwork learning strategies. Please bring your laptop computers in class.

    Evaluation methodology

    The success in meeting the course learning objectives will be evaluated as follows:

    - Continuous assessment (40%): may include tests of combined multiple-choice and/or short-answer questions to recapitulate worked contents in each unit, homework, programming exercises, group activities proposed in class, etc.

    - Exam (60%) consists of a 2 partial theoretical-practical exams taken at mid term (30%) and final term (30%).

    Overall, in order to successfully complete this course, the student must get a minimum final grade of 5 points (out of 10).

    Make-up exam: only the students that after the regular evaluation have not passed the course can take a reassessment exam. The grade obtained at the make-up exam will substitute the failed regular grade obtained during the trimester.

    Bibliography

    Basic

    Web links

    Previous capacities

    NA