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Data Analysis in Biological Research

Updated: Mar 1, 2022

Science has majorly been a data-driven endeavour. Owing to the recent advancement in research technologies, scientists now have access to a huge amount of diverse and complex data. As a result, data analysis skills are being widely recognized to play a significant role in future workplaces, and expertise in data science is becoming a prerequisite. Tools for gathering, evaluating, analysing, and interpreting data have become available as a result of comparable advancements in computer technology. Furthermore, statistical skills are no longer centered around improving science; they are instead an absolute need.

The application of computational tools in biological research has resulted in rapid progress in understanding physiological processes. Despite the fact that biology focuses on living organisms, statistical analysis gives a vital insight into many biological phenomena. Biologists benefit from having a thorough understanding of basic statistical principles in effectively planning experiments and interpreting as well as validating conclusions. Here, we will focus on biostatistics and its implication in biology.

Drug Development/Clinical Trials

Biostatistics involves the application of mathematical-statistical methods to solve medical and biological problems. For instance, to identify drug targets in silico, bioinformatics tools are used to generate promising drug candidate molecules, probe the structure of target molecules for probable binding sites and perform the consequent docking, check for ADMET (absorption, distribution, metabolism, excretion, potential for toxicity) characteristics, and rank molecules based on their binding energy. During drug clinical trials, owing to the varied biological factors across patients and the placebo effect, it is difficult to infer if therapy was effective or not based on a single case study. In such instances, biostatistics provides the appropriate means for converting clinical and laboratory findings into estimations of a therapy's impact on a group of patients.

AI Learning

In addition to the usage in medical research, bioinformatics tools have now become an indispensable aspect of biological data, not only in terms of handling and processing but also in terms of evaluating and verifying wet laboratory experiments. The advent of cloud computing, as illustrated by prominent platforms like Galaxy, allows researchers to select from a wide range of tools while using infrastructure provided by a service provider. Quantum computing, another noteworthy computational development, is likewise set to have an influence on biology. It may be available to help with tough problems that are computationally complex yet hard to solve with traditional computers.


Computer simulations are useful tools for studying the evolutionary and genetic ramifications of complicated processes whose interconnections are difficult to anticipate conceptually. Simulations have previously been utilised by a small group of programmers in population genetics, but the recent availability of configurable simulation software has made it an accessible alternative for researchers in a wide range of areas. For example, DNA methylation is a type of epigenetic marker that has been linked to a variety of biological processes and illnesses. It can now be investigated genome-wide, at high resolution, and in a huge number of samples because of recent technological advancements. Furthermore, in silico genetic data obtained through simulations is revolutionising genetic epidemiology, anthropology, evolutionary and population genetics, and conservation. Furthermore, the determination of the structure of the protein which used to be a time-consuming process has now become quite convenient owing to distributed computing. However, with the introduction of next-generation sequencing, a flood of data has been generated at many levels of cellular organization. As a result, data created by biological systems has entered the domain of big data analysis.

Systems Biology

Understanding the complexities of biological systems involves the employment of several software tools. Data management, network inference, deep curation, dynamical simulation, and model analysis are all processes in the systems biology computational workflow that necessitate the usage of such technologies. Additionally, efforts are being made to create integrated software platforms, so that tools used at different stages of the process and by different researchers may be easily coupled. Many statistical techniques have been designed to classify, organize, and predict the outcome from the expanded biological data. When statistics are coupled with the right programming language, we get a more analytical approach and a more exquisite outcome. As the task of curating the collected data is very tedious and repetitive, coding a solution in R and Python makes it easier for high-quality analysis.

To obtain useful information from biomedical research data, it must be properly managed and analysed. Each stage of processing enormous volumes of data has its own set of challenges that can only be solved with high-end computer solutions. To enhance their empirical capabilities, biostatisticians collaborate with professionals in a variety of areas, ranging from biologists and cancer specialists to surgeons and geneticists. However, biostatisticians are not mere number-crunchers. They play a critical role in the design of research to ensure that sufficient data and the appropriate information are collected. The data is then subsequently evaluated and interpreted, taking into consideration factors, biases, and missing data along the process.


Edited and approved by- Dr. Jyoti Chhibber-Goel and Dr. Bharti Singal

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