You may have heard that we can predict protein structures in minutes using computer algorithms. Before these technologies emerged, predicting protein structures required years of laborious work in the laboratory. So what has changed to make our work so much easier? Thanks to computational methods and advanced algorithms, it now seems possible to personalise treatment plans specific to our diseases based on our genetic information. These developments also allow us to reduce the cost of costly and lengthy drug discovery processes.

Today, the amount of biological data produced is growing exponentially every day. This data production process, which began with the completion of the Human Genome Project in 2003, has led to the accumulation of an unprecedented amount of biological big data, thereby creating challenges in organising and interpreting this data[1]. The interpretation of these massive data sets has become possible through the use of computational approaches, thereby giving rise to the field of bioinformatics[1].

Definition of Bioinformatics

Bioinformatics is a multidisciplinary field of science that emerged as a result of the synthesis of biology, medical sciences, information technology, mathematics, and biostatistics, using computational and analytical tools for the acquisition and interpretation of biological data. In short, bioinformatics is the science of examining and understanding biological data using computers.

The objectives of bioinformatics can be summarised under the following three headings:

1. Data organisation

2. System development

3. System implementation[2]

Data organisation aims to organise biological data (DNA, protein sequences, etc.) to make it easily accessible and to establish simple databases for this purpose. This will allow researchers to easily access the data and, when necessary, transfer their new results to this database[2].

The first biological sequence database established for the purpose of organising data was the ‘Protein Identification Resource’ established by Margaret Dayhoff in 1972. In the following years, the first DNA database was also established. Today, there are numerous databases available for free access via the internet. The most important databases for DNA sequences are GenBank, EMBL, and DDBJ[3].

When it comes to organising and presenting protein data, there are many protein banks around the world. The most important of these are the SwissProt, PIR International, and Protein DataBank (PDB) protein banks.

The development of systems, refers to the development of various software, algorithms, and methods for understanding and interpreting collected biological data. It focuses on the development of tools and resources for analysing biological data that is quite large and complex in structure[2].

Algorithms developed to compare the sequence of an analysed protein with previously characterised sequences are an example of this goal of bioinformatics. These algorithms enable the identification of homologous regions between sequences.

BLAST, which is widely used today, is the most common and basic tool for quickly comparing protein or DNA sequences in large databases to find similar sequences. ClustalW is also one of the widely used tools.

The application of these systems involves the meaningful analysis and interpretation of real biological problems using the developed methods and tools[2].

There are various databases containing different types of data. You can follow this link to access a list of the most commonly used databases.

What are the Applications of Bioinformatics?

Bioinformatics applications can be broadly categorised under three main techniques: sequence analysis, structural analysis, and functional analysis. Within these techniques, the primary application areas of bioinformatics have developed along the genomic and proteomic axes.

Genomic Applications

Genomics is a bioinformatics application area that examines all structural and functional aspects of genomes belonging to different species. The prominent applications in this field are as follows:

Genome Sequencing and Comparison: By sequencing the genomes of various organisms and identifying sequence similarities between genomes, the genetic structure of organisms can be understood. This enables the identification of genetic variations, leading to the development of more effective treatments for diseases such as cancer, heart disease, and diabetes[4].

Gene Function Analysis: Genes can be identified and classified according to their functions[4]. For example, by identifying genes involved in regulating the immune system, treatments for autoimmune diseases such as rheumatoid arthritis, lupus, and multiple sclerosis can be developed[4].

Gene Expression Analysis: By analysing gene expression data, it is possible to understand how genes are regulated in cells and tissues[4]. By analysing gene expression data obtained from cancer patients, the expression levels of genes in cancer cells can be compared with those in normal cells, and biomarkers can be developed[4].

Proteomics Applications

Proteomics analyses are used to predict the structure and function of proteins based on amino acid sequences. Bioinformatics tools can predict the 3D structure of a protein molecule, which helps researchers design drugs that target specific proteins involved in diseases such as HIV, Alzheimer’s and cancer[4].

Other Application Areas

In addition to genomics and proteomics, other important application areas of bioinformatics include:

● Transcriptomics, Metabolomics, and Epidemiology: These omics fields are also fundamental application areas of bioinformatics.

● Rational Drug Discovery: Used to identify potential drug targets and design new drugs. Bioinformatics accelerates the drug discovery process, enabling lower-cost and targeted drug design[5].

● Clinical Bioinformatics (Personalised Medicine): Medical treatment and prevention strategies are developed by considering each individual’s unique genetic makeup, environmental factors, and lifestyle[5].

● Agriculture and Food: Bioinformatics techniques are used to increase crop yields and develop new crop varieties[5].

The Future of Bioinformatics

The future of bioinformatics looks very promising with technological developments. The following are the prominent developments in this field:

Artificial Intelligence Integration

Artificial intelligence and machine learning algorithms enable many bioinformatics processes to be performed autonomously, saving time and effort in the analysis of complex biological information. An example of this is AlphaFold, developed in recent years. AlphaFold, a bioinformatics tool integrated with artificial intelligence, provides researchers with fast and effective use by predicting protein structures from the sequences at their disposal. Similarly, artificial intelligence is also integrated into personalised medicine and drug discovery processes, ensuring that treatment strategies are more predictable and effective[6].

Integration of Multi-Omics Data

This approach, also known as systems biology, involves integrating information from multiple ‘omics’ disciplines such as genomics, proteomics, and metabolomics to understand how different biological systems function as a whole. This integration is crucial for understanding the complexity of biological systems[7].

There are ongoing studies suggesting that a digital twin of an individual could be created in the future. By integrating gene expression, cellular pathways, and molecular interactions through experimental and computational methods, it is considered possible to create digital twins at the cellular and organ levels[8].

As the volume and complexity of biological data continue to grow, the importance of bioinformatics will continue to increase. These innovations not only promise to enhance our understanding of biology but also have the potential to reshape healthcare, personalised medicine, and drug development.

References

[1] Vincent, A. T., & Charette, S. J. (2015). Who qualifies to be a bioinformatician?. Frontiers in genetics, 6, 164. https://doi.org/10.3389/fgene.2015.00164

[2] Polat M, Karahan A. Multidisipliner Yeni Bir Bilim Dalı: Biyoinformatik ve Tıpta Uygulamaları. Med J SDU. 2009;16(3):41-50.

[3] Guenter Stoesser, Mary Ann Moseley, Joanne Sleep, Michael McGowran, Maria Garcia-Pastor, Peter Sterk, The EMBL Nucleotide Sequence Database, Nucleic Acids Research, Volume 26, Issue 1, 1 January 1998, Pages 8–15, https://doi.org/10.1093/nar/26.1.8

[4]”Applications of Bioinformatics in Various Fields.” BioinformaticsHome, https://bioinformaticshome.com/bioinformatics_tutorials/Applications%20of%20bioinformatics.html. Accessed 24 April 2025.

[5] Karazhanov D (2023) Techniques Involved in Bioinformatics and their Applications in the Field of Genomics. Int J Adv Technol. 14:226.

[6] Jumper, J., Evans, R., Pritzel, A. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021). https://doi.org/10.1038/s41586-021-03819-2

[7] “Bioinformatics: Key Techniques, Applications, and Future Trends in Biological Data Analysis.” E-SPIN Group, https://www.e-spincorp.com/bioinformatics-techniques-applications-future-trends/. Accessed 28 April 2025.

[8] Alsalloum, G. A., Al Sawaftah, N. M., Percival, K. M., & Husseini, G. A. (2024). Digital Twins of Biological Systems: A Narrative Review. IEEE open journal of engineering in medicine and biology, 5, 670–677. https://doi.org/10.1109/OJEMB.2024.3426916