Computational Genomics in Bioinformatics

Computational genomics (often referred to as Computational Genetics) refers to the use of computational and statistical analysis to decipher biology from genome sequences and related data, including both DNA and RNA sequence as well as other "post-genomic" data (i.e., experimental data obtained with technologies that require the genome sequence, such as genomic DNA microarrays). These, in combination with computational and statistical approaches to understanding the function of the genes and statistical association analysis, this field is also often referred to as Computational and Statistical Genetics/genomics.

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As such, computational genomics may be regarded as a subset of bioinformatics and computational biology, but with a focus on using whole genomes (rather than individual genes) to understand the principles of how the DNA of a species controls its biology at the molecular level and beyond. With the current abundance of massive biological datasets, computational studies have become one of the most important means to biological discovery.

The roots of computational genomics are shared with those of bioinformatics. During the 1960s, Margaret Dayhoff and others at the National Biomedical Research Foundation assembled databases of homologous protein sequences for evolutionary study.[3] Their research developed a phylogenetic tree that determined the evolutionary changes that were required for a particular protein to change into another protein based on the underlying amino acid sequences. This led them to create a scoring matrix that assessed the likelihood of one protein being related to another.

Contributions of computational genomics research to biology includes:

  • proposing cellular signalling networks
  • proposing mechanisms of genome evolution
  • predict precise locations of all human genes using comparative genomics techniques with several mammalian and vertebrate species
  • predict conserved genomic regions that are related to early embryonic development
  • discover potential links between repeated sequence motifs and tissue-specific gene expression
  • measure regions of genomes that have undergone unusually rapid evolution

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