Omics Data and Bioinformatics I

Objectives

One of the most profound revolutions that humanity has experienced in the last 50 years is the digitization of information. This process, which has enabled the development of computing and telecommunications, has also deeply affected the biosciences and, very specifically, biomedicine, a discipline in which large amounts of digitized information are available. The fields of genomics, transcriptomics, epigenomics and proteomics and, in general, the so-called omic technologies, previously introduced in the subject Omic Technologies, are a source of this type of information that must be known, explored and learned to exploit.

In this subject we focus especially on the analysis of transcriptomics data. Transcriptomics is the study of the complete set of RNA transcripts produced by a genome in a specific condition and allows us to understand how genes are expressed and how they are regulated in different biological situations.

We also study the basic workflows in transcriptomics, focusing on the various bioinformatics analysis methodologies. We learn how to treat this type of data for the achievement of research objectives and biomedical applications.

Finally we discuss some of the biomedical utilities of genomics and epigenomics. We see how this data is generated and how we can easily exploit it to obtain valuable information for research.

Learning outcomes

• RA1. Interprets and communicates the results of statistical and bioinformatics analyzes rigorously.
• LO2. Use computing, biological databases, and bioinformatics tools to obtain biological information.
• LO3. Shows skills for critical reflection in the processes linked to the exercise of the profession.
• LO4. Analyzes specific knowledge of the field and its contextualization in national and international environments.
• LO5. It applies procedures specific to scientific research in the development of training and professional activity.
• LO6. It designs interventions that meet the needs of the field in a multidisciplinary way.
• LO7. Moves with ease in contexts of virtual interaction through the use of ICT.
• LO8. It moves with desymboltura in the general use of ICT and, in particular, in the technological environments specific to the professional field.
• RA9. Shows sensitivity for fair and equal professional practice from a gender perspective.
• RA10. Moves with ease in complex situations or situations that require the development of new solutions.

Skills

General skills

• Formulate hypotheses following the scientific method, with an ability to summarize and analyze information in a critical way in order to be able to solve problems.

Specific skills

• Analyze biomedical data and biological sequences through the use of statistics and computation.
• Be able to critically interpret the results and conclusions of scientific studies.
• Formulate hypotheses and design experiments in the field of biomedical research.

Basic skills

• Students can apply their knowledge to their work or vocation in a professional manner and have competencies typically demonstrated through drafting and defending arguments and solving problems in their field of study.
• Students have demonstrated knowledge and understanding in a field of study that builds on general secondary education with the support of advanced textbooks and knowledge of the latest advances in this field of study.
• Students have the ability to gather and interpret relevant data (usually within their field of study) in order to make judgments that include reflection on relevant social, scientific and ethical issues.

Core skills

• Develop strategies for promoting gender equality and equity for all.
• Interact in international contexts to transfer knowledge to current and emerging fields of professional development and research.
• Make use of professional skills in multidisciplinary, complex, networked environments, whether on-site or online.
• Reflect critically on knowledge of all kinds, with a commitment to professional rigor and quality.
• Take control of one's learning process with a view to personal and professional growth and an all-round education.

Content

• Transcriptomics
  • Data acquisition from RNA-seq experiments
  • Bioinformatics workflow for processing data from RNA-seq experiments
  • Data manipulation and exploration of RNA-seq experiments with R
  • Manipulation, exploration, and visualization of differential expression analysis results with R
  • Multivariate techniques for data analysis of RNA-seq experiments with R
  • Functional analysis techniques of gene lists
• Genomics and epigenomics
  • Obtaining data from DNA sequencing experiments
  • User-level exploration and interpretation of sequencing results

Evaluation

The evaluation of the subject is continuous and is done through 4 elements:

• Participation and attitude in the classroom: 10% (non-refundable). Test-type questionnaires at the end of each session on the contents worked on during the class.
• Follow-up of RNA-seq data analysis and visualization practices with R: 30% (non-recoverable). Questionnaires and small deliveries.
• Data analysis project for RNA-seq experiments: 20% (recoverable). Recovery takes place during the school period.
• Final exam: 40% (recoverable). To pass the subject you must obtain a grade of 4.5/10 in this test.

The make-up tests that a student takes cannot exceed 50% of the subject's grade.

important

Plagiarism or copying someone else's work is penalized at all universities and, according to the UVic-UCC Coexistence Rules , constitutes serious or very serious offences. Therefore, in the course of this subject, plagiarism or the misappropriation of other people's texts or ideas (see what is considered plagiarism ) and the improper or undeclared use of artificial intelligence in an activity are translated automatically in suspension or other disciplinary measures.

To cite texts and materials appropriately, consult the academic citation guidelines and guidelines available on the UVic Library website.

Methodology

Classes are taught face-to-face. Most of the sessions consist of a theoretical part and a practical part, in which omic data analysis exercises are also done with R or other programs.

Bibliography

Key references

• Arivaradarajan, P., Gauri, M. (2018). Omics Approaches, Technologies and Applications. Springer.
• Braun, W. John, Duncan J. Murdoch (2016). A first course in statistical programming with R. Cambridge University Press.
• Gerner, C. & Hill, M. (2021). Integrative Multi-Omics in Biomedical Research: Multidimensional omics. Retrieved from https://directory.doabooks.org/handle/20.500.12854/77086
• González, JR., Cáceres, A. (2019). Omic Association Studies with R and Bioconductor. CRC Press.
• Wang, Xinkun (2016). Next-generation sequencing data analysis. CRC Press.

Further reading

Teachers will provide complementary bibliography and compulsory reading throughout the course via the Virtual Campus.