Omics
Full course description
In this course, you will explore the rich world of omics technologies and their applications.
This not only includes the detailed study of the technological possibilities, but also of the
data generated by experiments using these. The course will combine biological
understanding with computational understanding, and discuss which methods can be
applied to bring the two together.
We will discuss how a wide variety of omics technologies work, what they require and what
they can deliver and we will detail their features and limitations. Also we will explore the
entire trajectory of applying an omics method, checking and pre-processing the data to
make them suited for statistical evaluation, perform the statistical tests, and apply further
methods to help interpret the results and put them in a biological context. The initial phases
of the analytical process will mainly depend on the exact technology used, whereas the
later steps rather depend on the specific scientific questions to be answered.
Also we will look into specific examples of domains and specific studies in which omics
technologies are applied.
Some follow-up methods to further explore data generated by omics methods and
biologically interpret the findings of the experiments will be studied in more detail in
modules MSB1011 (Machine Learning, period 5, year 1) and MSB1014 (Network Biology,
period 1, year2).
Course objectives
The aim of this course is to obtain applied understanding of omics technologies and the processing of the data obtained using these technologies in research or other domains.
Specifically, the student should be able:
1. To describe commonly used technologies for genomics applications
1.1. DNA/cDNA microarrays
1.2. Next generation sequencing/ Massive parallel sequencing
Whole-genome and whole-exome
RNA (mRNA, miRNA, ncRNA)
Bisulphite-based
In combination with immunoprecipitation
2. To discuss commonly used applications of high-throughput biological profiling
2.1. Genetics / Genetic variation
2.2. Comparative genomics
2.3. Transcriptomics
2.4. Epigenomics
2.5. Metabolomics
2.6. Proteomics
2.7. Microbiomics
3. To retrieve, process, analyse and interpret omics data and experimental results
3.1 To query repositories for omics data and to use retrieved data and meta-data for integrative or comparative analysis
3.2 To apply the initial processing steps required to check data quality and prepare high-throughput data for statistical or biological analysis
3.3 To apply the statistical methods used for analysis of high-throughput data
3.4 To apply and explain the results of overrepresentation analysis methods, including:
Pathway analysis
Gene Ontology analysis
3.5 To describe other methods used for further data processing:
Clustering-based methods
Correlation-based methods
Classification-based methods
Network analysis-based methods
4. To explain the possibilities and limitations and examine the advantages and disadvantages of omics technologies, applications and data analysis methods
4.1 Genomics technologies
4.2 Applications of high-throughput biological profiling
4.3 Pre-processing and statistical methods for processing of high-throughput data
4.4 Analysis methods for further processes and biological interpretation of the results
5. To report how multiple types of omics data can be brought together and jointly analysed to increase biological understanding.
6. To discuss the societal (legal and ethical) implications of application of omics technologies and the big data generated.
7. To formulate and analyse use cases for each of the applications studied and to design an experiment tuned to answer a specific biological question using omics technologies.
Recommended reading
During this course, we will make use of scientific papers, dedicated study guides, and online study materials related to several tools and techniques. For the PBL tutorials,
recommended literature is provided with each case (after the pre-discussion). For the journal clubs, the papers are provided at least a week before the meeting is scheduled. For
skills labs and lectures materials are provided before, during and/or after the skills lab or lecture takes place, including example answer for skills lab assignments.