Molecular biology, genomics, statistics, and computer science are all integrated in the intrinsically multidisciplinary field of bioinformatics to analyze and interpret biological data. Whether prior wet lab (experimental) experience offers a real advantage in bioinformatics analyses is a common question in both academic and industrial contexts.

The Interdisciplinary Nature of Bioinformatics

Bioinformatics functions at the nexus of computational analysis and biological data generation. Next-generation sequencing (NGS), transcriptomics, proteomics, and metagenomics are examples of high-throughput technologies that depend on intricate wet lab procedures to generate data. As a result, upstream experimental design and laboratory-specific technical variables essentially limit downstream computational analyses.

Those who perform wet laboratory-based experimental processes such as extracting DNA or RNA, building libraries, amplifying and sequencing an organism’s genetic material using polymerases often describe a “wet lab experience” as having direct contact with these processes; this type of experience plays an important role in how much influence the wet lab experience will have on the ability of a bioinformatician to evaluate and analyze raw sequence data, including how to identify potential sources of bias and artifact in the experiment.

Knowledge of wet laboratory methodologies will greatly enhance the design of bioinformatics pipelines, optimise pipeline parameters, and enable bioinformaticians to make accurate assessments of the biological significance of sequences based on the biological context of those sequences, thereby increasing the probability of obtaining valid results from computational analyses and increasing the robustness of transcriptomic/genomic research.

Advantages of Wet Lab Experience in Bioinformatics

Understanding Data Generation and Assessing Data Quality

Wet laboratory experience allows for better understanding of how biological data is produced and where technical artifacts may arise. Technical artifacts may occur during the experimental process but do not have to be applied only when performing data processing of computer data (e.g., integrity of RNA, preparation of library protocols, and reverse transcriptions); technical artifacts that affect the quality of RNA-seq’s obtained RNA-seq data may be interpreted more accurately by a person (bioinformatician) who has experience in the wet laboratories than by a person who does not have any wet laboratory experience.

Also because the bioinformatician has experience in the wet laboratory, they are better prepared to discern the variance of a biological process from noise created by the technical artifact of wet laboratory work and make an accurate interpretation of the output of quality control of a bioinformatics analysis process and its subsequent analyses.

Improved Experimental Design and Hypothesis Formulation

Wet lab experts who are bioinformaticians have advantages when it comes to contributing to experimental designs at an early stage. They can provide guidance to wet lab scientists on how many biological replicates to use, what types of controls to include, how deep the sequencing should be performed, and how randomly samples should be selected.

Instead of receiving data to analyze from the experimentalists directly, Bioinformaticians who understand the wet labs can play an active role in creating biologically testable hypotheses that make sense within the constraints of the experiment. Many studies and discussions in the scientific community demonstrate that integrating bioinformatics and wet lab perspectives early in the research process will give researchers higher-quality datasets and more interpretable results.

Enhanced Biological Interpretation of Computational Results

It is possible for a computer to provide a statistically valid output that is not biologically plausible based on analytical techniques alone. The combination of computational skills plus wet lab experience allows scientists to have a more accurate view of how molecular mechanisms interact through several levels of biological regulation and how various experimental constraints affect interpretation of results from bioinformatic analyses.

Currently, literature exists on how curating biological data and utilizing contextual models enhance the semantic interpretation of extensive biological datasets that are generated through multi-omics analyses. The application of domain knowledge to large datasets in this manner improves the ability to integrate disparate bioinformatics analyses and provide a clearer picture of how these datasets represent the biology associated with each other.

Improved Communication Between Wet Lab and Dry Lab Teams

Communication barriers between experimental biologists and computational scientists hinder research progress and cause mistakes. Researchers trained at both wet labs and computer science reduce the likelihood of this, as they are able to interpret the experimental limitations of scientists and transform these into computational specifications.

This common vocabulary aids the rapid elimination of confusion between researchers; aids in troubleshooting; and improves collaboration abilities among researchers who participate in translational research and clinical genomics.

End‑to‑End Ownership of the Research Pipeline

Through wet lab experience, bioinformaticians gain a complete understanding of the research pipeline, from sample collection to biological conclusions. This complete understanding of both the experimental and computational stages of the pipeline allows bioinformaticians to better document experiments, provide reproducible results, and assist with troubleshooting during both experimental and computational stages.

As the need for close alignment between analytical and experimental decision-making grows within the areas of precision medicine, viral genomic analysis, and metagenomic monitoring, there is an increased demand for individuals with an integrated skill set of both wet lab and bioinformatics experience.

Limitations and Critical Perspectives

Wet Lab Experience Is Not Strictly Required

While wet lab experience can be a valuable asset for bioinformatics professionals, there are many successful bioinformatics professionals who have no wet lab experience at all. Many high-impact bioinformatics researchers concentrate on creating algorithms, statistically modeling, and developing software. Thus, deep knowledge of computation often outweighs experience in the wet lab.

For analytical aspects, having a conceptual understanding of the processes used in a wet laboratory environment may be adequate for many professionals, allowing them to perform their job successfully.

Career and Industry Considerations

Industries such as Tool Development, Machine Learning and Infrastructure usually consider programming skills, scalability and performance optimization when hiring for these types of positions on primary criteria basis. Usually, Wet Lab Experience is considered an ancillary value rather than a primary qualification for these positions. Wet Lab Experience enhances the ability to think analytically, however, it should not come at the expense of building a high level of computational competency.

Having laboratory experience in biology is NOT necessary for people working in the field of bioinformatics; however, it does provide a major advantage when interpreting data, designing experiments, communicating between disciplines and contextualizing biology. When a person has worked within both the Laboratory and Computer environments, they have gained the skills needed to identify false findings in their work, construct biologically plausible hypotheses, and create a cohesive biological story to explain the data.

Bioinformaticists and bioinformatician workflows best reflect the principal ethics of balance since both the science behind the laboratory and the computational science come together to form a single process. As there is a continuing increase in the amount of information gathered through multi-omics and translational research, those individuals who can combine both the laboratory and the computational aspects of research will have an unprecedented and paramount role in the bioinformatics field.

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