An Introduction to Bioinformatics and Its Importance in Understanding Biology

Bioinformatics relies on the development of methods and software tools to understand particularly large and complex biological data. It bridges multiple fields including Biology, Computer Science, Mathematics to treat and interpret biological data. 

  • Sequence alignment: DNA sequences of similar or distinct species can be compared to shed light on their biological significance ie why and where these sequences are present. If they are conserved amongst different species there is high chance that it is an evolutionary important feature which may be indispensable to the species’ survival. For example, BLAST (Basic Local Alignment Search Tool) is a computer program enables the researcher to compare a query sequence against a large database containing sequences from different organisms. 
  • Genomics: the study of our genomes useful for example to understand how so many diverging species have emerged. Bioinformaticians look for patterns (DNA or amino acid proteins sequences) in the genomes to unveil what their function is. If a researcher discovers a novel gene in mice for example, it may also exist in humans or not. If it does and performs the same function in the humans, it is called a homolog and will be useful when conducting researchers – modelling diseases or advancing biological knowledge on gene function. 
  • Drug design: in the past, drugs were developed based on a “trial and error” approach which have been substituted to more rational drug designs thanks to bioinformatics. Indeed, drug candidates can now be modelled (using computer algorithms), selected and discarded at a large scale which means that pharmaceutical companies focus now only on lead candidates. This reduces the time scale of drug development and financial expenses. Moreover, personalised medicine has also benefitted from this approach. A patient may now receive a medication or therapy based on their genomic fingerprint. 

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Protein structure visualisation and prediction: The recognition of shared domains can allow to predict how an unknown protein may look like structurally and what roles it may undertake in the organism. As such, 3D models can be developed to mimic and predict how the protein looks like prior to any structural analysis such as X-ray crystallography. Indeed, this method relies on a bank of existing structures and assuming that sequences with many regions of identity (exact sequences) or similarity (similar sequences) will have a similar fold in space. Structures are harder to predict for very dynamical areas. 

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