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  • To understand the differences between computational biology, bioinformatics, and systems biology
  • To appreciate the application of bioinformatics to scientific and clinical domains of interest
  • Locating and applying bioinformatics tools on the web
  • Understanding the strengths and weaknesses of different bioinformatics tools
  • Learning simple scripting languages to automate bioinformatic analyses


computational biology,bioinformatics, molecular biology, DNA microarray, tissue microarray, genomics, proteomics, systems biology, Translational Research Informatics, PERL, nanotechnology

Read about

Bioinformatics is the application of information technology to a problem in the biological domain, most commonly molecular biology. The applied information technology is usually a result of computational biology research which is focused on methodology development, such as algorithms and computer simulation techniques. Due to its data intensive nature, computational biology relies heavily on machine learning approaches. Systems biology is a multi-disciplinary field, including bioinformatics and computational biology, with the premise "the whole is greater than the sum of the parts". Due to the complexity of the systems studied, computer simulations and numerical methods are commonly used in systems biology research.

Bioinformatics applications usually involve the development of a computational software pipeline, basically using a series of programs in order to complete a certain analysis. Due to the use of multiple programs which are commonly from various sources, there are usually many different input and output formats. In order to create a unified pipeline, many file format conversions may be required. The practical extraction and report language (PERL) programming language is adept at performing this functionality due to its ability to process strings effectively and efficiently using regular expressions. It is an interpreted language and thus used as "glue" between the more computationally intensive programs, normally written in a compiled language, like C or FORTRAN. A group of PERL modules, called BioPerl, incorporate commonly used functionality into a standardized set of application programming interfaces (APIs). There are many other languages that are also used in bioinformatics, including Java/BioJava and Python/BioPython. Many bioinformatics pipelines are implemented in the UNIX/Linux OS environment and use shell scripting, awk and sed in addition to the aforementioned programming languages.

With the large amount of data from clinical and "-omics" studies, bioinformatics is poised to help fuel clinical translational research informatics studies. The key point is the integration of clinical and molecular data in order to accelerate improved healthcare outcomes, commonly referred to as "bench to bedside". The hope is that this translational research will help bring along personalized medicine. Potential translational research studies could include biomarker validation studies, discovery of drug targets, as well as discovering novel disease genes and molecular pathways. Many of the current studies have relied on associating single nucleotide polymorphisms (SNPs) on the genomic scale to observable (commonly disease) states, known as genome-wide association studies (GWAS). There has also been significant research in trying to use gene expression data, which is currently DNA microarrays, integrated with other data sources including clinical parameters, in order to tease apart the genes underlying disease states.


Talk to your bioinformatics core or computational biology labs about collaborating on a translational research project.

Online Resources


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Advanced courses

Expert corner

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Bioinformatics | Goals | Topics | Read about | Activities | Online Resources | Questions | Advanced courses | Expert corner This page was last modified by - JYKim_Path JYKim_Path on Nov 30, 2009 10:46 am.