Omics Data and Bioinformatics I

Objectives

One of the most profound revolutions that humanity has experienced in the last 50 years is the digitalization of information. This process, which has allowed the development of computing and telecommunications, has also profoundly affected the biosciences and, in a very special way, biomedicine, a discipline in which large amounts of digitalized information are available. The fields of genomics, transcriptomics, epigenomics and proteomics and, in general, the so-called omic technologies, previously introduced in the subject Omics 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 under a specific condition and allows us to understand how genes are expressed and regulated in different biological situations.

We also study basic transcriptomics workflows, focusing on the various bioinformatic analysis methodologies. We learn how to process this type of data to achieve research objectives and biomedical applications.

Finally, we discuss some of the biomedical uses 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. It uses computing, biological databases and bioinformatics tools to obtain biological information.
• LO3. Demonstrates skills for critical reflection in processes linked to the exercise of the profession.
• LO4. Analyses knowledge specific to the field and its contextualisation in national and international environments.
• LO5. Apply 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 virtual interaction contexts 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 an equitable and egalitarian professional practice from a gender perspective.
• RA10. Moves with ease in complex situations or those that require the development of new solutions.

Competencies

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
  • Obtaining data from RNA-seq experiments
  • Bioinformatics workflow for processing data from RNA-seq experiments
  • Statistical analysis of data from RNA-seq experiments with R (parametric statistics, non-parametric statistics and regression models)
  • Manipulation, exploration and visualization of differential expression analysis results with R
• 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). Multiple-choice questions at the end of each session on the content covered during class.
• Follow-up practices for analyzing and visualizing RNA-seq data with R: 15% (non-refundable). Questionnaires and small deliveries.
• Data analysis project for RNA-seq experiments: 25% (refundable). The recovery takes place during the school period.
• Final exam: 50% (retrievable). 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 grade.

Important

Plagiarism or copying someone else's work is penalized in all universities and, according to the UVic-UCC coexistence rules, constitute serious or very serious faults. Therefore, during the course of this subject, plagiarism or the improper appropriation of texts or ideas from other people (see What is considered plagiarism?) and the improper or undeclared use of artificial intelligence in an activity automatically result in suspension or other disciplinary measures.

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

Methodology

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

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.