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Biological informatics (abbreviated bioinformatics) is an interdisciplinary field that develops methods and software tools for understanding biological data. As an interdisciplinary field of science, bioinformatics combines computer science, statistics, mathematics, and engineering to study and process biological data. Bioinformatics is the research, development, or application of computational tools and approaches for expanding the use of biological, medical, behavioural or health data, including those tools required to acquire, store, organize, archive, analyse, and visualize such data [Huerta+Al:2000].

It is impossible to do much with this gigantic volume of data unless one uses computer tools to decipher and to find meaningful patterns in the data. Bioinformatics essentially is using computers to analyse a wide variety of biological problems. Bioinformatics is the transformation of data into knowledge and understanding in the area of biology. Bioinformatics started as a small and obscure discipline. Today it is a huge field that is making a digital revolution in biology.

Bioinformatics and computational biology have similar aims and approaches, but they differ in scope where bioinformatics is mostly more general and computational biology problems are often more specific in their focus. Bioinformatics organizes and analysis basic biological data, while computational biology builds theoretical models of biological systems. To be more precise, bioinformatics usually deals with genomics and other omics while computational biology is totally focused on building accurate mathematical simulations. The aims of bioinformatics are three-fold:

  1. Storage: At its simplest bioinformatics organises data in a way that allows researchers to access existing information and to submit new entries as they are produced.
  2. Analysis: Further bioinformatics aims to develop tools and resources that aid in the analysis of data.
  3. Interpretation: The third aim is to use these tools to interpret the results in a biologically meaningful manner.

Today bioinformatics aims to conduct global analysis of all the available data with the aim of uncovering common principles that apply across many systems and highlight novel features. The computational goals of bioinformatics are:

  • Learn & Generalize: Discover conserved patterns (models) of sequences, structures, interactions, metabolism and chemistry from well-studied examples.
  • Prediction: Infer function or structure of newly sequences, genes, genomes, proteins or proteomes from these generalizations.
  • Organize & Integrate: Develop a systematic and genomic approach to molecular interactions, metabolism, cell signalling, gene expression, etc.
  • Simulate: Model gene expression, gene regulation, protein folding, protein-protein interaction, protein-ligand binding, catalytic function, metabolism, etc.
  • Engineer: Construct novel organisms, functions or regulations of genes and proteins in silico.
  • Manipulation: Target specific genes via in silico mutation/knockdown to change phenotype .

The central paradigm of molecular biology is:

DNA -> RNA -> Protein -> Phenotype (Symptoms)

The corresponding central paradigm of bioinformatics is:

Genetic information
             -> Molecular structure
                          -> Biochemical function
                                        -> Phenotype (Symptoms)

Bioinformatics is a fast-growing interdisciplinary field. As a result of the rising research effort in bioinformatics, the global bioinformatics market was valued at 4,110.6 million USD in 2014 and is expected to reach 12,542.4 million USD in 2020 [Hare:2014].

Genomic sequencing capabilities have increased exponentially [Lander+Al:2001] [Venter+Al:2001] [Kircher+Kelso:2010], outstripping advances in computing power [Kahn:2011] [Gross:2011] [Huttenhower+Hofmann:2010] [Schatz+Al:2010] [Cloud:2012]. The rate of available genomic data is increasing approximately tenfold every year, a rate much faster than Moore's Law for computational processing power [Kahn:2011].

Human genomes differ on average by only 0.1% [Venter+Al:2001]. One thousand human genomes contain less than twice the unique information of one genome. Thus, although individual genomes are not very compressible, collections of related genomes are extremely compressible [Christley+Al:2009] [Brandon+Al:2009] [Maekinen+Al:2009] [Kozanitis+Al:2010].

Compressive algorithms for genomics have the advantage of becoming proportionally faster with the size of the available data [Loh+Al:2012]. As computing moves toward distributed and multiprocessor architectures, this ability must be considered for new algorithms to be run in parallel.

Bioinformatics is very useful as it forms a basic for other disciplines at the intersection of biology and computer science, as it deals with the raw data and provides algorithms to operate on it, and gathers meaningful insights into biological phenomena. It is, for instance, utilized heavily in functional genomics to infer the functions of biological entities encoded in the genome.


[Hare:2014]Hare, Glen (2014) 'Global Bioinformatics Market Will reach USD 12,542.4 million in 2020.'' Finances.
[Lander+Al:2001]Lander, E S, et Al (2001). 'Initial sequencing and analysis of the human genome.' Nature. 409(6822), pp. 860-921.
[Venter+Al:2001](1, 2) Venter, J C, et Al (2001). 'The sequence of the human genome.' Science (New York, N.Y.). 291(5507), pp. 1304-51.
[Kircher+Kelso:2010]Kircher, Martin & Kelso, Janet (2010). 'High-throughput DNA sequencing--concepts and limitations.' BioEssays : news and reviews in molecular, cellular and developmental biology. 32(6), pp. 524-36.
[Kahn:2011](1, 2) Kahn, Scott D (2011). 'On the future of genomic data.' Science (New York, N.Y.). 331(6018), pp. 728-9.
[Gross:2011]Gross, Michael (2011). 'Riding the wave of biological data.' Current biology : CB. 21(6), pp. R204-6.
[Huttenhower+Hofmann:2010]Huttenhower, Curtis & Hofmann, Oliver (2010). 'A quick guide to large-scale genomic data mining.' PLoS computational biology. 6(5), pp. e1000779.
[Schatz+Al:2010]Schatz, M., Langmead, B. & Salzberg, S. Nat. Biotechnol. 28, 691–693 (2010). 8. 1000 Genomes Project data available on Amazon
[Cloud:2012]'1000 Genomes Project data available on Amazon Cloud.' NIH press release, 29 March 2012.
[Christley+Al:2009]Christley, Scott; Lu, Yiming; Li, Chen; & Xie, Xiaohui (2009). 'Human genomes as email attachments.' Bioinformatics (Oxford, England). 25(2), pp. 274-5.
[Brandon+Al:2009]Brandon, M.C., Wallace, D.C. & Baldi, P. Bioinformatics 25, 1731–1738 (2009).
[Maekinen+Al:2009]Mäkinen, Veli; Navarro, Gonzalo; Sirén, Jouni; & Välimäki N. Niko (2009) 'Storage and Retrieval of Individual Genomes.' in Research in Computational Molecular Biology, vol. 5541 of Lecture Notes in Computer Science (Batzoglou, S., ed.) 121–137 (Springer Berlin/Heidelberg, 2009).
[Kozanitis+Al:2010]Kozanitis, Christos; Saunders, Chris; Kruglyak, Semyon; Bafna, Vineet; & Varghese, George (2010) 'Compressing Genomic Sequence Fragments Using SlimGene.' in Research in Computational Molecular Biology, vol. 6044 of Lecture Notes in Computer Science (Berger, B., ed.) 310–324 (Springer Berlin/Heidelberg, 2010).
[Loh+Al:2012]Loh, Po-Ru; Baym, Michael; & Berger, Bonnie (2012). 'Compressive genomics.' Nature biotechnology. 30(7), pp. 627-30.
[Huerta+Al:2000]Huerta, Michael; Downing, Gregory; Haseltine, Florence; Seto, Belinda; & Yuan Liu (2000) 'NIH working definition of bioinformatics and computational biology.'' Biomedical Information Science and Technology Initiative. 17 July 2000. Retrieved 18 August 2012.

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