Scope and Applications of Bioinformatics

Bioinformatics is a branch of science that incorporates multidisciplinary fields to develop protocols and softwares for gathering and understanding large, complex biological data sets. It uses the power of computation, machine learning and artificial intelligence to interpret complex biological data. Bioinformatics utilizes data and models derived from biological entities, analyzes and interprets the outputs using computer programs and applies the generated information for prediction and modelling through simulations.

Fig. 1: Bioinformatics (Source: Bayat, 2002)

The scope and application of bioinformatics is far reaching and ranges from biological literature search to simulation predicting biomolecular interactions which has been explained below:

1. Sequence Analysis: Bioinformatics tools can sequence, analyze, identify, annotate and compare the gene sequences as well as entire genomes of various organisms. Thus obtained information, in turn, helps researchers understand the genetics of various physiological as well as pathological disorders.

Fig. 2 Chromatogram depicting sequences.

For example, researchers can compare the sequence between normal healthy cells and cancerous cells to discover the underlying driver mutations. Apart from this, this information could also be used for translational and personalized medicines. As such, bioinformatics could be used to analyze DNA, RNA and protein sequences for generating valuable information.

With the advent of Next-generation sequencing (NGS) that is capable high-throughput, cost-effective, reliable and fast sequencing, it has become even more imperative for bioinformatics to develop efficient pipelines for conversion of raw data to applied and actionable knowledge.

Fig. 3 Oxford Nanopore MinION device. (Source: Wang et al., 2021)

2. Structural Bioinformatics: Bioinformatics could be applied to predict and model the 3D structure of nucleic acids, proteins and other complex biomolecules. Using advanced algorithms and high-powered computational techniques to study the macromolecular 3D structures, it delineates the relation between their structure and function. Apart from the macromolecular structure predictions, bioinformatics tools could also help to understand their function in relation to changing environment as well as with other macromolecules. Examples: Protein Data Bank (PDB), Nucleic Acid Database (NDB) etc. Using structural bioinformatics, prediction tools could be developed for protein structure prediction, ligand-receptor binding etc.

Fig. 4 Different structure of protein. (Source: Patel et al., 2019)

3. Evolutionary Biology: Phylogenetic trees can be built to analyze the evolutionary patterns of an organism as well as discover the relationships between seemingly distant species through analysis of their conserved elements. It can not only be used for ancestral relationships from the long past, but also recent changes resulting in the current genotype and phenotype. For example, the origin of COVID-19 in a non-human animal could be traced back closest to its non-human host due to the conserved nature of the sequences between the two strains of the virus.

Fig. 5 Schemcatic diagram of Phylogenetic Tree depicting relation between different species.

4. Genomic Annotation: As more and more information becomes available, it will be necessary to keep them structured, accessible and understandable. Bioinformatics helps in annotation of the genes and functions collated from theoretical and experimental data sets. DNA, its corresponding RNA and the subsequent proteins and its structure helps to understand the standpoint of the gene in terms of evolution as well as function with respect to their distribution among living organisms as well as the resulting phenotype.

Fig. 6 Genome Annotation (Source: https://theg-cat.com/tag/genome-annotation/)

5. Functional Genomics: Functional genomics pertains to the genetic, epigenetic, transcriptomic, proteomic, metabolomics, network and statistical approaches to study gene expression and its subsequent application in disease research and drug design and discovery. Driven by systems-level approach, this field of study could help to fast-track the different aspects of biomedicine and personalized medicines. Apart from improving human health, this could be applied to different field of studies including but not limited to crop improvement for sustainable agriculture, biodiversity and conservation etc.

Fig. 7 Functional Genomics (Source: https://www.ebi.ac.uk/training/online/courses/functional-genomics-i-introduction-and-design/what-is-functional-genomics/)

6. Proteomics: Bioinformatics infers functionality of the protein based on their structure, which in turn could help for drug discovery as well as protein engineering. It also helps to understand the resulting implications of the protein modifications as well as their interactions with various other proteins on a larger scale.

Fig. 8 Proteomics (Source:https://health.usf.edu/medicine/corefacilities/prote omics/introduction)

7. Systems Biology: Bioinformatics helps to study complex biological systems in its entirety, incorporating various types of data and information from varied sources so as to get the complete picture of the underlying biological processes. This helps to understand the actual functionality of the gene and under the big picture rather than look at individual parts. Systems approach to any biological processes works under the notion than the whole is always greater than the sum of its individual parts.

Fig. 9: Systems Biology (Source: https://www.mdedge.com/pediatrics/ar ticle/152364/infectious-diseases/systems-biology-primer)

8. Network and Pathway Analysis: Understanding the interactions between genes and proteins in networks and pathways play a vital role for analyzing the subsequent DNA-RNA, DNA-protein, protein-protein dynamics. This in turn helps to understand the underlying pathologies of various physiological conditions. How a change in upstream of any genes or pathways can severely hamper the working of the downstream genes or pathways provides pivotal information for understanding the interconnected of the pathways.

Fig. 10 Interconnected networks and pathways (Source: Xiang et al., 2019)

9. Comparative Genomics: Genomic comparison between two different strains of bacteria could help us to realize the key differences that could be making one strain susceptible to particular antibiotic while making the other strain resistant. Comparative genomics helps us to understand the homologies, differences and evolutionary trends within or between species and its subsequent effect on the phenotype and behavior.

Fig. 11: Comparative Genomics

(Source: https://ncbiinsights.ncbi.nlm.nih.gov/2023/06/01/revolutionize-rese arch-cgr/)

10. Databases and Ontologies: As the amount of data being generated is increasing exponential, the need for not just storage but also organization and retrieval of this vital information also rises exponentially. Hence, creating standardized terminologies to ensure no confusion while analyzing the annotated data could save countless hours spent due to redundancy. Hence, bioinformatics plays on the need to organize data in such a way that researchers around the world get updated and reliable information as and when needed. Examples: NCBI’s GenBank.

References:

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.
Wang, Y., Zhao, Y., Bollas, A. et al. Nanopore sequencing technology, bioinformatics and applications. Nat Biotechnol 39, 1348–1365 (2021). https://doi.org/10.1038/s41587-021-01108-x
Patel, B., Singh, V., Patel, D. (2019). Structural Bioinformatics. In: Shaik, N., Hakeem, K., Banaganapalli, B., Elango, R. (eds) Essentials of Bioinformatics, Volume I. Springer, Cham. https://doi.org/10.1007/978-3-030-02634-9_9
Xiang, Jin & Zhang, Yuhong & Tuo, Lin & Liu, Rui & Gou, Dongmei & Liang, Li & Chen, Chang & Xia, Jie & Tang, Ni & Wang, Kai. (2019). Transcriptomic changes associated with PCK1 overexpression in hepatocellular carcinoma cells detected by RNA-Seq. Genes & Diseases. 7. 10.1016/j.gendis.2019.04.004
https://theg-cat.com/tag/genome-annotation/
https://bioinformaticshome.com/bioinformatics_tutorials/Applications%20of%20bioinformatics.html
https://www.ebi.ac.uk/training/online/courses/functional-genomics-i-introduction-and-design/what-is-functional-genomics/
https://health.usf.edu/medicine/corefacilities/proteomics/introduction
https://www.mdedge.com/pediatrics/article/152364/infectious-diseases/systems-biology-primer
https://ncbiinsights.ncbi.nlm.nih.gov/2023/06/01/revolutionize-research-cgr/