The terms wet lab and dry lab are common in the context of natural science fields. Students may be wondering what they are, how those terms are related to each other, and how they evolved in the first place. The word wet lab refers to the laboratory comes to our minds whenever we think about a scientist doing experiments mixing different chemicals or reagents. On the other hand, dry lab is quite the opposite. Research in dry labs is conducted through computational, theoretical, or data-driven work rather than using liquids and chemicals. Wet and dry labs’ tasks are reciprocal since wet lab fuel dry lab with the data, and dry lab provide insights and make sense of this data serving the overall goal of scientific research. With the advancements in molecular biology, genetics, and the development of computers, the terms emerged to distinguish between the experimental and computational approaches in scientific research [1]. Since then, dry lab techniques and technology started to expand rapidly due to the increasing interest the cyber world, and its potential effects on different aspects of life including the research in molecular biology and genetics.
The best way to understand what bioinformatics is to decode its name first, since it represents the fields combined to give this discipline. The prefix Bio stands for biology, which is the main field to be investigated; while the word informatics stands for information science and computer science which use statistics, data science, and computer methods to power revealing results of biological incomes [2]. Bioinformatics have earlier prevailed as the foundations of computational biology through its vital applications (figure 1) in omics studies, homology modeling, protein structure prediction, functional annotation, sequence alignment, phylogenetics, and system biology. It is possible with bioinformatics to identify genes and sequence genomes through genomics and gene analysis, predict protein structure and protein-protein interactions through proteomics, analyze RNA sequencing data together with differentially expressed genes and detecting splicing variants, and using all the multi-omics tools to lead discoveries in personalized medicine, phylogenetics, and system biology [3]. All these current functions of bioinformatics are essential, but AI is revolutionizing all technologies and the domains of study including bioinformatics, molecular biology, and genetics.
Figure 1. Some of the bioinformatics
AI is invading the scene with its tools and abilities to duplicate the outcomes of bioinformatics in its different applications. Powered by its advances in machine learning, ML, pattern recognition, predictive modeling, automation, and learning from unlabeled data, AI is reshaping the expectation and utilization of bioinformatics. AI algorithms are used nowadays to enhance different fields of bioinformatics. To illustrate, there are algorithms in protein-protein interactions like Support Vector Machines (SVMs) which are applied in the drug discovery process through predicting the drug binding sites by classifying the protein-protein interactions. Other algorithms including Nearest Neighbours for phylogenetic assessments, Bayesian Networks for System Biology, and Deep Learning (ANN, CNN) for Personalized Medicine are all based on AI to automate and operate the data analysis conducted by bioinformatics tools [4]. These implementations of AI are the beginning of a new era, where the computational analysis does not depend only on the human and pure computer abilities, but extend to how AI modules are trained to play the vital role in the process of scientific discoveries.
In conclusion, the rapid developments in bioinformatics, AI, and computational biology in general will has its impact on the skillset requirements for future research positions or opportunities in the job market. Thus, it is essential to learn more, and be updated about the new tools and approaches in those fields.
References:
[1] Gauthier, J., Derome, N., Charette, S. J., & Vincent, A. T. (2018). A brief history of bioinformatics. Briefings in Bioinformatics, 20(6), 1981–1996. [CrossRef]
[2] B. Yingngam, “Introduction to Bioinformatics,” in Artificial Intelligence and Machine Learning in Drug Design and Development, A. Khanna, M. El Barachi, S. Jain, M. Kumar, and A. Nayyar, Eds. Hoboken, NJ, USA: Wiley, 2024, ch. 2. [CrossRef]
[3] Diniz, W. J. S., & Canduri, F. (2017). REVIEW-ARTICLE Bioinformatics: an overview and its applications. Genetics and Molecular Research, 16(1). [PubMed]
[4] Jamialahmadi, H., Khalili-Tanha, G., Nazari, E., & Rezaei-Tavirani, M. (2024). Artificial intelligence and bioinformatics: a journey from traditional techniques to smart approaches. Gastroenterology and Hepatology from Bed to Bench, 17(3). [PubMed]
