Computational genomics

Computational genomics (1) refers to the use of computational and statistical analysis to decipher biology from genome sequences and related data, [1] including both DNA and RNA sequences as well as other “post-genomic” data (ie DNA microarrays, which requires the genome sequence . These fields are also often referred to as Computational and Statistical Genetics/ genomics. As such, computational genomics can be considered as a subset of bioinformatics and computational biology , but with a focus on whole genomes (rather than individual genes) to understand the principles of the DNA of a species of biology and molecular biology. beyond. With the current abundance of massive biological datasets, computational studies have become one of the most important means to biological discovery. [2]

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.

Beginning in the 1980s, databases of genome sequences began to be recorded, but this presented new challenges in the form of searching and comparing the databases of gene information. Unlike text-searching algorithms that are used on websites such as Google or Wikipedia, searching for sections of genetic similarity requires one to find strings that are not simply identical, but similar. This led to the development of the Needleman-Wunsch algorithm, which is a dynamic programming algorithm for comparing sets of amino acid sequences with each other by using scoring matrices derived from the earlier research by Dayhoff. Later, the BLAST algorithm was developed for performing fast, optimized searches of gene sequence databases. BLAST and its derivatives are probably the most widely used algorithms for this purpose.[4]

The emergence of the phrase “computational genomics” coincides with the availability of complete sequenced genomes in the mid-to-late 1990s. The first meeting of the Annual Conference on Computational Genomics was organized by scientists from The Institute for Genomic Research (TIGR) in 1998, providing a forum for this speciality and effectively distinguishing this area of science from the more general fields of Genomics or Computational Biology.[5][6] The first use of this term in scientific literature, according to MEDLINE abstracts, was just one year earlier in Nucleic Acids Research.[7] The final Computational Genomics conference was held in 2006, featuring a keynote talk by Nobel Laureate Barry Marshall, co-discoverer of the link between Helicobacter pylori and stomach ulcers. As of 2014, the leading conferences in the field include Intelligent Systems for Molecular Biology (ISMB) and Research in Computational Molecular Biology (RECOMB).

The development of computer-assisted mathematics (using products such as Mathematica or Matlab) has helped engineers, mathematicians and computer scientists to start operating in this domain, and a public collection of case studies and demonstrations is growing, ranging from whole genome comparisons to gene expression analysis.[8] This has increased the introduction of different ideas, including concepts from systems and control, information theory, strings analysis and data mining. It is anticipated that computational approaches will become and remain a standard topic for research and teaching, while students fluent in both topics start being formed in the multiple courses created in the past few years.

Contributions of computational genomics research to biology

Contributions of computational genomics research to biology include:[2][9]

  • discovering subtle patterns in genomic sequences [9]
  • 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

Latest Development (from 2012)

First Computer Model of an Organism

Researchers at Stanford University created the first software simulation of an entire organism.[10][11] They mapped the 525 genes of the bacteria Mycoplasma genitalium, the smallest free-living organism. With data from more than 900 scientific papers reported on the bacterium, researchers developed the software model using the object-oriented programming approach. A series of modules mimic the various functions of the cell, and then integrated it into a whole simulated organism. The simulation runs on a single CPU, recreating the complete life span of the cell at the molecular level, reproducing the interactions of molecules in cell processes including metabolism and cell division.[12]

The ‘silicon cell’ will act as computerized laboratories that could perform experiments which are difficult to do on an actual organism, or could carry out procedures much faster. The applications will include faster screening of new compounds, understanding of basic cellular principles and behavior.[10][12]

See also

  • Bioinformatics
  • Biowiki
  • Computational biology
  • Genomics
  • Microarray
  • Computational epigenetics


  1. Jump up^ Koonin EV (March 2001). “Computational genomics”. Curr. Biol11(5): R155–8. doi:10.1016/S0960-9822(01)00081-1. PMID 11267880.
  2. ^ Jump up to:a b Computational Genomics and Proteomics at MIT
  3. Jump up^ Mount, David (2000). Bioinformatics, Sequence and Genome Analysis. Cold Spring Harbor Laboratory Press. pp. 2–3. ISBN 0-87969-597-8.
  4. Jump up^ Brown, T.A. (1999). Genomes. Wiley. ISBN 0-471-31618-0.
  5. Jump up^ [backPid]=67&cHash=fd69079f5e The 7th Annual Conference on Computational Genomics (2004)
  6. Jump up^ The 9th Annual Conference on Computational Genomics (2006)Archived 2007-02-12 at the Wayback Machine.
  7. Jump up^ Wagner A (September 1997). “A computational genomics approach to the identification of gene networks”. Nucleic Acids Res25 (18): 3594–604. doi:10.1093/nar/25.18.3594. PMC 146952 . PMID 9278479.
  8. Jump up^ Cristianini, N.; Hahn, M. (2006). Introduction to Computational Genomics. Cambridge University Press. ISBN 0-521-67191-4.
  9. ^ Jump up to:a b Gagniuc, P; Ionescu-Tirgoviste, C (Sep 28, 2012). “Eukaryotic genomes may exhibit up to 10 generic classes of gene promoters”. BMC Genomics13: 512. doi:10.1186/1471-2164-13-512. PMC 3549790 . PMID 23020586.
  10. ^ Jump up to:a b McClure, Max (19 July 2012). “Stanford researchers produce first complete computer model of an organism”. Stanford University News. Retrieved 3 August 2012.
  11. Jump up^ Karr JR, Sanghvi JC, Macklin DN, et al. (July 2012). “A whole-cell computational model predicts phenotype from genotype”. Cell150 (2): 389–401. doi:10.1016/j.cell.2012.05.044. PMC 3413483 . PMID 22817898.
  12. ^ Jump up to:a b John Markoff (20 July 2012). “In First, Software Emulates Lifespan of Entire Organism”. The New York Times. Retrieved 3 August 2012.