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Bioinformatics : A Vast Untapped Potential

Updated: Sep 7, 2022

The word “Bioinformatics” instantly prompts us to think about DNA related coding and protein sequencing. Well, technically we aren’t wrong but bioinformatics is so much more than that. To put it in simple words, bioinformatics is an interdisciplinary field that creates techniques and programming devices for understanding natural information, especially when the information indexes are too vast and complicated. With a basic understanding of this subject, we can easily consolidate scientific information, design data, decipher natural information and make use of the results and insights from this process.




Bioinformatics and its impact on genomics:


Last year, it was announced that the entire human genome had been mapped as a result of the world genome project and various private institutions. In the recent years, the world has witnessed the finalization of the whole genome sequence of various other organisms including Haemophilus influenzae, which was the first complete genome of any free-living organism to be sequenced, Mycobacterium tuberculosis, Yersinia pestis, among many others. The knowledge obtained from these sequences has made a considerable improvement in our understanding of this field. Due to the fast improvement in bioinformatics, we will soon be able to locate every single human gene and its function.


Functional genomics is the study of genes, their resulting proteins, and the role played by the proteins. Since the completion of the first draft of the human genome, the emphasis has been changing from genes themselves to gene products.


DNA microarray technique, which incorporates genotyping and DNA sequencing, assesses the degree of gene expression. The messenger RNA expression levels of thousands of genes resulting in benign and malignant skin cancer tumors can be simultaneously analyzed using gene expression arrays. Tumor classification and prospective treatment targets are provided by expression profiles.


When analyzing and interpreting biological data, information is taken into account at multiple levels, including the genome, the proteome, and the transcriptome. Analysis of the overall number of proteins (proteome) expressed by a cell is known as proteomics, and examination of the messenger RNA transcripts generated by a cell is known as transcriptomics (transcriptome).


Annotated protein and two-dimensional electrophoresis databases are used in bioinformatic protein research. The next difficulty in bioinformatics is the prediction of a protein's structure after a protein has been separated, identified, and characterized. In order to generate three dimensional models of molecules, structural biologists also employ bioinformatics to manage the enormous and complex data from x-ray crystallography, nuclear magnetic resonance, and electron microscopy research.


Various bioinformatics tools are employed in the study of bioinformatics. The main tools of this subject are computer software programs and the internet. The basic activity is analyzing the sequence of proteins and DNA by using the various databases available on the internet like The National Center for Biotechnology Information (www.ncbi.nlm.nih.gov) and the National Center for Genome Resources (www.ncgr.org/), PDB (https://www.wwpdb.org/) , EMBL(https://www.embl.org/), SWISS-PROT (www.expasy.org/sprot/), KEGG (https://www.genome.jp/kegg/) and GenBank (http://www.ncbi.nlm.nih.gov/Genbank) . Large companies and private institutions employ bioinformaticians to perform as well as maintain large scale complicated bioinformatic needs as required by these industries. All these resources would be extremely difficult to get by an individual researcher and there would be a certain need for an external bioinformatic advisor.


In order to manage the growing volume of biological data from the genome projects effectively, computer databases with quick assimilation, readable formats, and algorithm software programs are required. There is no single comprehensive database for accessing all of this information due to the diversity of emerging data. However, there are an increasing number of databases available that offer information that is useful to academics and physicians. Most of these databases offer their information free of charge to academic users, however some sites need subscriptions and some industrial users need to pay a license fee. Examples include websites that provide in-depth descriptions of clinical illnesses, identify genetic mutations and polymorphisms associated with disease susceptibility, and enable a search for disease genes given a DNA sequence (box).


While exploring the applications of bioinformatics, we come across the three main places where genomics is used are

  • Gene analysis in cancer: A succession of genetic mutations that lead to major modifications to cancer cells' metabolism are mostly accountable for the disease's progression. In order to facilitate the growth of primary cancer cells as well as their invasion and metastasis into other parts of the body, new genes will replace the functions of those that have been lost and acquire new roles. The identification of new therapeutic targets in the form of both specific genes or proteins as well as whole sets of genes and proteins has emerged as a result of the genome-wide study of both cancer cells and tissues. In order to develop these novel therapeutics, the genome and transcriptome of cancer tissues are studied in order to find genetic changes that are found to be related to cancer risk and/or clinical outcome.

  • Bioinformatics and SNP: SNP is essentially, the Single-Nucleotide Polymorphism, is the substitution of a single nucleotide at a particular location in the genome. Understanding an individual's probability of developing cancer and/or their likely response to various therapies can be improved by detecting inherited genetic variants like SNPs and understanding how these variations can change protein function, gene regulation, and expression. Over 2 million SNPs have been discovered by the SNP consortium so far, adding up to a total of over 10 million documented SNPs. SNPs frequently have a dense spread across the genome, making them an ideal marker for extensive genome-wide association studies for many diseases and malignancies.

A person’s gene is often chosen and screened for SNPs when used to detect cancer. It is then possible to identify any haplotypes, haplotype frequencies, and the risk of disease and/or therapeutic response that every haplotype carries.

The statistical analysis of SNP data and the identification of hallmark SNPs for a particular haplotype block are both actively influenced by bioinformatics. Dynamic programming is one bioinformatic approach to determining the optical alignment of genetic sequences.

Haplotyping, linkage disequilibrium tests, linkage analysis, and public data repository tools are further bioinformatic methods used for SNP analysis.


  • Genomics, bioinformatics and infectious diseases: The pathophysiology and mechanisms of many infectious diseases are now understood better because of the integration of genomes and bioinformatics. For one, M. tuberculosis causes the disease known as tuberculosis, which affects 9 million people worldwide and claims the lives of about 2 million each year. Since the first sequencing of the M. tuberculosis genome in 1998, scientists have managed to improve diagnostic and medication susceptibility tools and deepen their understanding of the interactions between humans and mycobacteria. For instance, whole-genome investigations of M. tuberculosis have demonstrated that SNPs are substantially in charge of this pathogen's treatment resistance to antimycobacterial agents. While these are subject specific uses of bioinformatics, in the field of hereditary qualities, it helps in sequencing as well as explaining genomes and their various mutations. Bioinformatics tools help in contrasting, investigating as well as analyzing hereditary and genomic information and are more or less in comprehension with sub atomic sciences. On a more holistic level, it focuses on the list of organic groupings and routes, which is an essential element of framework science. It assists in the replication and demonstration of DNA, RNA, proteins, and bimolecular interactions in underlying science.


In conclusion, bioinformatics is a vital science that is heavily relied upon in the field of genomics. Developments in bioinformatics has led to conducting a thorough study of various diseases and narrowing down the location of their origin in the human genome. The practical applications of bioinformatics studying genome and transcriptome of cancer tissues has largely helped to decipher genetic changes related to cancer risk. Furthermore, the statistical analysis of SNP data and employment of other bioinformatic methods has simplified the identification of risk of a disease in an individual alongside encouraging study of pathophysiology of various infectious diseases like M. tuberculosis. Hence, bioinformatics will serve as a bridge between molecular biologists and computational errors.


References:

  1. Bayat A. Science, medicine, and the future: Bioinformatics. BMJ. 2002 Apr 27;324(7344):1018-22. doi: 10.1136/bmj.324.7344.1018. PMID: 11976246; PMCID: PMC1122955.

  2. Cuffari, Benedette. (2021, January 12). Role of Bioinformatics in Genome Analysis. AZoLifeSciences. Retrieved on August 06, 2022 from: https://www.azolifesciences.com/article/Role-of-Bioinformatics-in-Genome-Analysis.aspx.

  3. Barsnes H (2021) Bioinformatics for Proteomics. J Pharm Prac Edu Vol.5 No.6: 20.

  4. Lutz A (2021) An Overview on Preliminaries of Bioinformatics and Genomics. J Mol Biol Biotech. 6 No.4:e001


- written by Mahima Kate and Aparajita Chakraborty


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