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 generating 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 have been applied.
For some of the follow-up methods (learning objective 6), we will only discuss them on a generic level, as they will be studied in more detail in modules MSB1011 (Machine Learning, period 5, year 1) and MSB1014 (Network Biology, period 1, year2).
During the skills training, you will dive into data generated by real omics experiments of different types. You will explore how to obtain data related to published experiments and how to use the meta-data to be able to reuse the datasets. You will apply basic processing methods, statistical analysis, and follow-up methods to extract biologically understandable results from these experiments. You will use a variety of tools, both locally installed and available online.
The learning objectives of this course are:
- To know and understand commonly used technologies for genomics applications:
* DNA/cDNA microarrays
* Next generation sequencing/ Massive parallel sequencing
. Whole-genome and whole-exome
. In combination with immunoprecipitation
- To know and understand commonly used applications of high-throughput biological profiling, including
* Genetics / Genetic variation
* Comparative genomics
- To understand and be able to apply the initial processing steps required to check data quality and prepare high-throughput data for statistical or biological analysis
- To understand and apply the statistical methods used for analysis of high-throughput data
- To be able to apply and understand the results of overrepresentation analysis methods, including:
* Pathway analysis
* Gene Ontology analysis
- To know other methods used for further data processing:
* Clustering-based methods
* Correlation-based methods
* Classification-based methods
* Network analysis-based methods
- To be able to describe how multiple types of omics data can be brought together and commonly analysed to increase biological understanding
- To be able to find repositories for omics data and to use retrieved data and meta-data for integrative or comparative analysis
- To be able to give the possibilities and limitations and the advantages and disadvantages of:
* Genomics technologies
* Applications of high-throughput biological profiling
* Pre-processing and statistical methods for processing of high-throughput data
* Analysis methods for further processes and biological interpretation of the results
- To be able to describe real use cases for each of the applications studied
To be able to design an experiment tuned to answer a specific biological question, using –omics technologies
During this course, we will make use of study books, but also of scientific papers, dedicated study guides, and online study materials related to several tools and techniques.