Bioinformatics aims at extraction of information from genetic data. Statistical methods used toward this end are the focus of statistical bionformatics.
Technological developments in molecular biology over the last two decades have improved the knowledge of molecular and cellular processes underlying diseases and treatment effects. This has opened an opportunity to create novel diagnostic tests and treatments, profoundly influencing the management of patients in various disease areas. For instance, development of molecular biology targeted therapies have dramatically changed the range and efficacy of treatments available to cancer patients.
“Omics”-oriented approaches to study underlying molecular and cellular processes in health, disease and treatment effects consider all molecules collectively instead of one molecule at a time, generating a system-wide understanding. Several “-omics” areas exist, such as genomics, transcriptomics, and proteomics.
Data obtained with the help of “omics” technologies are usually very voluminous (yielding even milions of measurements per single biological sample), highly structured, and complex. Analysis of such data is not trivial. First, some understanding of the technology and related molecular biology concepts is needed. Second, the analysis often requires application of methods that are not used in a “classical” statistics setting where the number of measurements (variables) is much smaller than the number of (independent) observations. Finally, processing of large datasets requires appropriate data storage and access tools, as well as efficient numerical computation techniques and software.
Thus, analysis of “omics” data has become a specialty of its own. Of course, good knowledge of “classical” statistical methodology is required and training in this respect is offered in the first year of our program. Additionally, an introduction to medical and molecular biology is offered, together with an introductory training in programming. The second year focuses on the methods specific for the analysis of “omics” data, in particular, genomic and proteomic data. The training in programming is continued, together with courses on computer intensive statistical aproaches and computational methods.
Bioinformatics is an interdisciplinary science. Statisticians working in this domain have to interact with biologists, geneticists, clinicians, and computer scientists. Thus, communication and presentation skills, and ability to work in a team are indispensable. During the studies such skills are developed across all the courses by working on individual and group projects and making presentations of the results obtained in these projects.
The “omics” revolution is a fact. Specialists capable of designing, analyzing, and interpreting of “omics” experiments are highly sought by universities, research institutions, biotechnology companies, and pharmaceutical industry. They can support fundamental research in academia, but also work in teams looking for, e.g., novel, molecular biology driven drug targets in biotech and pharma companies. The demand for such specialists is already high in the developed world, but also increases in the developing countries. It is thus a safe bet that the number of working places in statistical bioinformatics will only increase in the years to come.
With the constant evolution of high-throughput technologies, the future of biomedical research has been projected to be at the molecular level. These advanced genomic and proteomic technologies generate large amount of data that require multi-disciplinary expertise to handle and derive useful information from the generated data. This often requires a blend of biological, database management, computer programming, data mining, and advanced statistical skills.
Despite being an intensive program, the Master of Statistics – Bioinformatics trajectory produces multi-disciplinary expertise in such a way that it provides its graduates with the necessary background and/or hands-on experience in molecular biology, database management, data mining algorithms, standard and advanced statistical modeling, reporting, communication, and, in summary, being an independent professional and a great team player.
Coming from a mathematical and computing background, this master trajectory equipped me with the relevant biological and statistical knowledge that, in turn, earned me the position of a statistical bioinformatics consultant (in charge of all high-dimensional experimental designs, data-preprocessing, data analyses, and analyses pipelines) at Erasmus University in Rotterdam and a PhD Fellow (with focus on class prediction with high-dimensional data) at Utrecht University.
(Victor Lih Jong, Cameroon)
Programme coordinator: prof. dr. Tomasz Burzykowski (tomasz.burzykowski[at]uhasselt.be)
Administrative info: Peter Vandoren (peter.vandoren[at]uhasselt.be)