Journal of Medical Academics
Volume 6 | Issue 2 | Year 2023

Bioinformatics and Medicine: Editorial

Panav Rustagi1, Ritu Sharma2, Shaifaly M Rustagi3

1Department of Biotechnology, Jaypee Institute of Information Technology, Noida, Uttar Pradesh, India

2Department of Physiology, Army College of Medical Sciences, Delhi, India

3Department of Anatomy, Army College of Medical Sciences, Delhi, India

Corresponding Author: Shaifaly M Rustagi, Department of Anatomy, Army College of Medical Sciences, Delhi, India, Phone: 8376063069, e-mail: shaifalyrustagi@gmail.com

How to cite this article: Rustagi P, Sharma R, Rustagi SM. Bioinformatics and Medicine: Editorial. J Med Acad 2023;6(2):43–45.

Source of support: Nil

Conflict of interest: None

Bioinformatics as a field utilizes a combination of biology, computer science, information technology, and statistics to analyze and interpret biological data. Bioinformatics is used to analyze data derived from patients which includes genomic and proteomics information along with clinical records.1 Bioinformatics finds applications in various scientific and medical fields, contributing significantly to our understanding of biological processes and aiding in the development of innovative solutions. In genomics, bioinformatics plays a crucial role in analyzing vast datasets of DNA and RNA sequences, allowing for the identification of genes, regulatory elements, and variations associated with diseases.2 In proteomics, it helps unravel the complexity of protein structures and interactions, offering insights into cellular functions and potential drug targets.3 In drug discovery, bioinformatics enables the identification of promising compounds through virtual screening, structural analysis, and predictive modeling.1 Additionally, bioinformatics is integral to personalized medicine, where individual genetic information is analyzed to tailor treatments for optimal effectiveness and minimal side effects.4

Diseases such as inborn errors of metabolism for eg urea cycle disorders can be identified using various computational methods.5 Clinical bioinformatics utilizes the methods and tools of bioinformatics to provide solutions for questions that are relevant to clinical medicine and healthcare.4 Clinical bioinformatics plays a very important role in genomic medicine by analyzing and interpreting genomic data from patients. Healthcare analysts utilize methods from clinical bioinformatics to provide the best possible healthcare at the lowest cost which reduces the strain on the family economic system.


A method that is used to understand the structure, function, and features of the genomic data of a patient. The sequence analysis is conducted mostly by using the shotgun sequence technique.4 In the shotgun method, long sequences of DNA are broken apart randomly into many shorter fragments. Each fragment is then sequenced and a computer is used to order these pieces into a whole chromosome or genome, using sequence overlap to guide the assembly.6 There are many powerful tools that are present such as basic local alignment search tool, which is used to compare the sequences of proteins, nucleotides, DNA, and RNA. A software named HMMER - Hidden Markov model (HMM) is used for the identification and analysis of homologous proteins.4 These tools that are available have their own advantages and disadvantages.


Predicting protein structure is a critical aspect of bioinformatics, and it involves using computational methods to model the three-dimensional arrangement of atoms in a protein. Understanding the protein’s structure is essential for elucidating its function, interactions, and potential roles in various biological processes. The accuracy of protein structure prediction depends on the methods used, the availability of template structures, and the characteristics of the target protein. Advances in computational power, algorithm development, and the availability of large protein structure databases continue to improve the accuracy and reliability of protein structure prediction methods.7


Drug designing, also known as rational drug design, is a process that involves the use of computational and experimental approaches to identify, design, and optimize new molecules for use as therapeutic drugs. The goal is to develop compounds that selectively interact with specific targets in the body, such as proteins or nucleic acids, to modulate their activity and treat diseases. It is easy to breakdown the molecules of the new compound using the software compared with various practice approaches. The advances in software design and information technology led to the computer-aided drug design to design highly effective drugs.8


Bioinformatics contributes to drug design by analyzing biological data, predicting molecular interactions, and assessing the efficacy and safety of potential drug candidates. It aids in target identification, structure-based and ligand-based drug design, absorption distribution metabolism elimination toxicity *prediction, integration of genomics data, network pharmacology, and clinical trial design.1 Bioinformatics accelerates drug discovery by leveraging computational approaches to understand biological systems and optimize the selection of lead compounds for experimental validation. There are various databases that are developed by different organizations. Some of them are the potential drug target database, therapeutic target database, and manually annotated target and drugs online resources.4 This allows the drug developers to safely introduce the drug for utilization without causing major harm.

The abnormally increased quantities of proteins are often associated with diseases. The comparison of proteomes between patient and control is an effective tool in identifying drug targets. Proteomic data has been deposited in public databases such as Protein Abundances Across Organisms (PaxDB), which help in the development and application of indices predicting the rate of new protein formation.


Deoxyribonucleic acid (DNA) methylation and histone modification are the two components of epigenetic modification. These two processes mediate the silencing of apoptotic pathways leading to some cancers.9 To reactivate the apoptosis pathway histone deacetylase and its inhibitors have been used as a drug target.10

The important question is how methylation of DNA alters epigenetic modifications. For eg in β-thalassemia patients, the enhancer that controls the expression of the β-globin gene is mutated or deleted or the enhancer that is brought close to the promotor of the β-globin gene is relocated somewhere else due to abnormal protein/DNA binding.11

Environmental factors also affect epigenetic modification as is reflected in different manifestations of the same disease in monozygotic twins. DNA methylation is affected by the presence of adequate S-adenosyl l-methionine and the optimum activity of methyl adenosine phosphorylase (MTAP). MTAP deletion is common in cancer cells.12 Thus, genes affecting methionine metabolism could be drug targets, and bioinformatics, with databases such as Kyoto Encyclopedia of Genes and Genomes13 can identify such genes effectively.

As per bioinformatics, there are two approaches to treating cancer cells. The first is to restore the gene expression of cancer cells to that of normal cells. The second, when the first is not achievable, is to kill cancer cells by inducing apoptosis. These two approaches imply two criteria in phenotypic screening for anticancer drugs—(1) increased similarity in gene expression between cancer cells and normal cells and (2) increased similarity in gene expression between cancer cells and apoptotic cells.14


Bioinformatics is integral to personalized medicine, a paradigm that tailors healthcare strategies to individual characteristics for optimal outcomes. In this context, bioinformatics plays a crucial role in analyzing genomic data to identify genetic variations associated with diseases and assess individual disease risks. Tumor profiling in cancer treatment relies on bioinformatics to analyze genomic and molecular data, enabling the selection of targeted therapies based on the specific genetic alterations present in tumors. Bioinformatics integrates diverse data sources, including clinical, genetic, and lifestyle information, to develop personalized treatment plans that consider individual patient characteristics. Clinical decision support systems powered by bioinformatics assist healthcare professionals in making informed choices about diagnostics and treatments.15 Data security and privacy measures are implemented in bioinformatics to protect sensitive genetic and health information. Bioinformatics also contributes to patient engagement by providing individuals with insights into their genetic makeup and health risks. Furthermore, bioinformatics supports research efforts to uncover new associations between genetic variations and diseases, contributing to the development of novel targeted therapies and interventions. Overall, bioinformatics is at the forefront of transforming healthcare into a more personalized and precise model, enhancing treatment outcomes, and patient well-being.


In conclusion, the intersection of bioinformatics and healthcare represents a transformative force that has significantly advanced our understanding and approach to medical science. Bioinformatics has become an indispensable tool in the era of precision medicine, allowing healthcare professionals to analyze vast and complex datasets, integrate diverse sources of information, and tailor interventions to individual characteristics. From genomics to personalized medicine, bioinformatics has enabled breakthroughs in the identification of disease markers, the design of targeted therapies, and the optimization of treatment plans.

Furthermore, bioinformatics has proven instrumental in drug discovery and development, streamlining the identification of potential drug candidates, predicting their interactions, and optimizing their properties. The field’s contribution to clinical trials, pharmacogenomics, and the exploration of drug repurposing has accelerated the translation of scientific discoveries into tangible therapeutic solutions.

However, as bioinformatics continues to propel advancements in healthcare, it is essential to address challenges such as data security, ethical considerations, and the need for ongoing education and collaboration across disciplines. The collaborative efforts between bioinformaticians, healthcare professionals, and researchers are crucial for navigating the complexities of this rapidly evolving field.

In essence, the marriage of bioinformatics and healthcare holds immense promise for the future. As technology continues to advance, bioinformatics will likely play an increasingly central role in shaping the landscape of medical research, diagnosis, and treatment. The ongoing synergy between these two fields promises to bring about a new era of precision, efficiency, and personalized care, ultimately improving patient outcomes and contributing to the advancement of global health.


1. Xia X. Bioinformatics and drug discovery. Curr Top Med Chem 2017;17(15):1709–1726. DOI: 10.2174/1568026617666161116143440

2. Saravanan KA, Panigrahi M, Kumar H, et al. Role of genomics in combating COVID-19 pandemic. Gene 2022;823:146387. DOI: 10.1016/j.gene.2022.146387

3. Al-Amrani S, Al-Jabri Z, Al-Zaabi A, et al. Proteomics: concepts and applications in human medicine. World J Biol Chem 2021;12(5):57–69. DOI: 10.4331/wjbc.v12.i5.57

4. Majhi V, Paul S, Jain R. Bioinformatics for Healthcare Applications. Dubai, United Arab Emirates; 2019. pp. 204–207.

5. Thistlethwaite LR, Li X, Burrage LC, et al. Clinical diagnosis of metabolic disorders using untargeted metabolomic profiling and disease-specific networks learned from profiling data. Sci Rep 2022;12:6556. DOI: 10.1038/s41598-022-10415-5

6. Alberts B, Johnson A, Lewis J, et al. Molecular Biology of the Cell, 4th edition. New York: Garland Science; 2002.

7. Fiser A. Template-based protein structure modeling. Methods Mol Biol 2010;673:73–94. DOI: 10.1007/978-1-60761-842-3_6

8. Sliwoski G, Kothiwale S, Meiler J, et al. Computational methods in drug discovery. Pharmacol Rev 2013;66(1):334–395. DOI: 10.1124/pr.112.007336

9. Wade PA, Wolffe AP. Recognizing methylated DNA. Nat Struct Biol 2001;8(7):575–577. DOI: 10.1038/89593

10. Bolden JE, Peart MJ, Johnstone RW. Anticancer activities of histone deacetylase inhibitors. Nat Rev Drug Discov 2006;5(9):769–784. DOI: 10.1038/nrd2133

11. Kioussis D, Vanin E, deLange T, et al. Beta-globin gene inactivation by DNA translocation in gamma beta-thalassaemia. Nature 1983;306(5944):662–666. DOI: 10.1038/306662a0

12. Bigaud E, Corrales FJ. Methylthioadenosine (MTA) regulates liver cells proteome and methylproteome: implications in liver biology and disease. Mol Cell Proteom 2016:15(5):1498–1510. DOI: 10.1074/mcp.M115.055772

13. Kanehisa M. Molecular network analysis of diseases and drugs in KEGG. Methods Mol Biol 2013;939:263–275. DOI: 10.1007/978-1-62703-107-3_17

14. Shoemaker RH. The NCI60 human tumour cell line anticancer drug screen. Nat Rev Cancer 2006;6(10):813–823. DOI: 10.1038/nrc1951

15. Castaneda C, Nalley K, Mannion C, et al. Clinical decision support systems for improving diagnostic accuracy and achieving precision medicine. J Clin Bioinform 2015;5:4. DOI: 10.1186/s13336-015-0019-3

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