A field that focuses on creating various methods and software tools to understand biotechnology in Bioinformatics! Let’s have a deep look at it!
Introduction to Bioinformatics in Biotechnology
Bioinformatics is considered an interdisciplinary field of study which predominantly involves the domains of a number of sciences like molecular biology and genetics, mathematics, computer science and statistics.
More precisely it develops methods and software tools for understanding the enormous amount of biological data with great ease. Under the umbrella of bioinformatics data-intensive, large-scale biological problems are addressed from a different point of view.
The approach to finding out and solve a number of biological problems are extensively computational in nature. This branch of science has been quite often used for in silico analyses of different biological queries using mathematical and statistical techniques.
In short, it links a huge amount of biological data to techniques for the storage of data, analysis and distribution of data to the researchers so that different areas of scientific research could be supported.
It is mostly fed by high-throughput data-generating experiments including genomic sequence determinations and measurements of patterns of gene expression.
Different database projects then perform the curation and annotation of the data and then distribute it through the medium of the World Wide Web.
Due to the mining of these data number of scientific discoveries take place and also the identification of new clinical applications emerge in one or the other form.
If we trace back the history, it is has been known that the term “Bioinformatics” was coined by Paulien Hogeweg and Ben Hasper. The reason behind this term was that it was used to describe the use of information processes in biological systems.
The methods of initial analysis are still basic to many large-scale experiments in the molecular life sciences but nowadays bioinformatics in biotechnology is considered to be a much broader discipline which encompasses image modelling and analysis of image there are some classical methods which are used for comparison of linear sequences or three-dimensional structures.
More importantly, these days’ bioinformatics is used in the identification of candidate genes and Single Nucleotide polymorphisms for the purpose of a better understanding of a number of diseases.
Analyzing biological data to produce meaningful information involves writing and running software programs that use algorithms from graph theory, artificial intelligence, soft computing, data mining, image processing, and computer simulation.
Ex: Public Biological Data of Bioinformatics in Biotechnology
|
Data type |
Collaboration |
| Protein sequences | UniProt Consortium |
| Genomic and clinical data | Global Alliance for Genomics and Health |
| Protein identifications | The ProteomeXchange Consortium |
| Macromolecular structures | Worldwide Protein Data Bank |
| Nucleotide sequences | International Sequence Database Collaboration |
| Molecular interactions | The International Molecular Exchange Consortium |
| Metabolomics data | Coordination of Standards in Metabolomics |
These collaborations have already proven to be beneficial for the scientific community in one or the other way. Another such important aspect of these collaborations is that their participants get to exchange a huge amount of data and/or assign workload in such a way in order to avoid the duplication of effort along with ensuring that data are annotated and made available in a coherent and consistent way.
Some of the other accessory tools also exist such as genome browsers, databases that bring together all the available genomic and molecular information about a particular species. Whenever the information has to be retrieved from the data archives there is the utilization of standard tools for the identification of data items by keyword.
The algorithms in turn depend on theoretical foundations such as discrete mathematics, control theory, system theory, information theory, and statistics.
In bioinformatics, for the purpose of storing and organizing data different data banks are used. These storing data bank entities collect DNA and RNA sequences from globally published scientific papers and a number of genome projects.
Different databases are being run under the hands of internationally based consortia. For example, an advisory committee made up of members of the European Molecular Biology Laboratory Nucleotide Sequence Database (EMBL-Bank) in the United Kingdom, the DNA Data Bank of Japan (DDBJ), and GenBank of the National Center for Biotechnology Information (NCBI) located in the United States are all handling the International Nucleotide Sequence Database Collaboration (INSDC).
As per their outputs, these databases are considered as the centres of excellence. To remain up to date it is mandatory to make it ensure that sequence data that are of nucleic acid, as well as protein structures, are freely available.
There is a requirement that the scientific journals should submit the new nucleotide sequences in a publicly accessible database as a condition for publication of an article. In the past times as the number of published sequences increased, the workflow also changed.
Now the databank operators started open discussions with publishers of the scientific literature and the organizations which were behind these databases started convincing publishers to urge the researchers to submit their sequences to one of the public databases before submitting the paper.
Ex: if one will type “aardvark myoglobin” into Google, the molecule’s amino acid sequence is retrieved from different databanks. There are some other algorithms that search different data banks to detect similarities between different data items.
Some of the relations which can be established between bioinformatics and other fields are becoming much popular day by day. It is a science field that is similar to but distinct from biotechnological computation, while it is often considered the same as that of computational biology.
There is a contrasting difference between the two branches of science. On one hand, biological computation uses bioengineering and biology to build biological computers and on the other hand, bioinformatics uses computation to better understand biology and solves different biological problems.
Bioinformatics & Computational Biology in Biotechnology
Bioinformatics and computational biology involve the analysis of biological data, particularly DNA, RNA, and protein sequences. There was an explosive growth in the field of bioinformatics starting in the mid-1990s, driven largely by the Human Genome Project and by rapid advances in DNA sequencing technology.
There have been high throughput advances in the field of bioinformatics in Biotechnology since the time it has been developed. Recently in May 2019, scientists identified a key peptide to develop a vaccine for leptospirosis, an emerging tropical infectious bacterial disease that affects humans and animals completely using the computational approach.
Final Words about what is Bioinformatics in Biotechnology
I hope that now you have a clear idea about Bioinformatics in Biotechnology. If you have any suggestions or feedback, please leave a comment below.
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