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How Big Data is Influencing Genetic Research?


November 3, 2021

From start-ups to Fortune 500 companies, big data and its applications are changing the way we do business throughout the world. Data collecting, data analytics, and the comprehension of that data have grown more accessible, with far-reaching implications, regardless of what area you work in or the size of your company. In today’s technology-enabled world, a number of data-producing platforms, such as websites, social networks, and online commerce, can provide business intelligence that can be utilised to enhance company processes and interactions. Big data’s influence isn’t limited to commercial industries; it’s also helping to improve genetic research.


What is Big Data?

Massive data sets which are too large or intricate to be analysed by typical data applications are referred to as “big data.” To extract value from these big datasets, businesses rely on storage and processing capacity, as well as powerful data analytics and capabilities. Because of the value, it generates, big data is used in practically every business throughout the world, including healthcare, bioengineering, and genetic research.


What is CRISPR?

Genome editing, also known as gene editing, is a set of technologies that allow researchers to alter an organism’s DNA by inserting, deleting, or modifying genetic material at specified points within the genome. CRISPR-Cas9, which stands for clumped regularly interspaced small palindromic repetitions and CRISPR-associated protein 9, is one of the numerous gene-editing techniques.


Big Data and Genetic Research

Scientists may now swiftly produce, store, and analyse data that previously would have taken years to assemble due to technological developments. New biomedical procedures, such as the next genome sequencing, are producing massive amounts of data and resulting in scientific discoveries, but researchers are trying to keep up. The National Institutes of Health, for instance, launched the ‘Big Data to Knowledge (BD2K) Initiative’ and the ‘Precision Medicine Initiative’ with the goal of developing biologically guided diagnosis with personalised, precision medicine for better prevention, early detection, and treatment for chronic disease processes. They plan to do so by compiling and connecting a million Americans’ electronic medical records and statistics in classifying and capturing whole genome sequences, cellular functions, proteins, toxins, RNA, DNA, and behavioural information. That’s a lot of information. The applicability of data mining in genetic research is extensive, but the key problem is turning enormous data into usable insights that can be used for science and innovation.

Being a genetic scientist in today’s fast-changing, big data-fuelled environment entails dealing with algorithms that handle large data in genetics, as well as data processing tools.

Machine learning (ML) is the use of data-analytical techniques to multi-dimensional datasets in order to build prediction models and acquire insights from the data.

Machine learning aids scientists in the study and understanding of complicated biological processes such as genome or gene editing, as well as the creation of models that learn from large data sets and provide predictable results.


Big Data in CRISPR Genetic Research

Data genetics and genetic research are collaborating to improve science’s knowledge of illnesses. The huge amounts of data currently available to scientists, as well as the ability of machine learning and data technology to analyse it, is hastening the creation of novel medications and personalised therapies. The emergence of individualised therapy based on a person’s unique genetic profile exemplifies this.

Big data also gives doctors the knowledge they need to prescribe dosages that are personalised for each patient, lowering the likelihood of adverse effects and medication resistance. The high expense of this individualised medicine method has led to significant opposition to its adoption. The cost of sequencing and genome engineering technologies like CRISPR, on the other hand, is steadily decreasing. The potential of CRISPR to modify genomes and DNA at a low cost means that customised solutions will become more economical to create and produce, as well as more accessible to the general population.


Big Data is Influencing Genetic Research

CRISPR is gaining popularity among scientists all over the world. Associate Professor Richard Kandasamy of the Norwegian University of Science and Technology’s (NTN) Centre of Molecular Inflammation Research (CEMIR) has studied the inflammatory responses that occur in a variety of disorders. Kandasamy has merged current technology with more conventional genetic mapping to identify a minute-by-minute script of what happens when the immune system responds to the existence of a virus within a cell, using big data, enormous computer systems, and CRISPR.

CropsOS, based in the United States, has integrated CRISPR genome editing transposable elements with big data and ML to boost agricultural innovation and provide decision-making data to plant scientists. They designed a genome editing system that allows for the modification of plant attributes like flavour, nutritional density, and durability using CRISPR technology and machine learning-based predictive modelling. This significantly reduces the expensive research and development expenses that have hitherto limited sophisticated genomic innovation to a few researchers.



Big data is having an influence on every discipline of study, from social and political science to genetics and individualised treatment. Big data continues to provide the potential for genetic research and related fields. Researchers in the field of genetics should maintain a flexible approach to big data and keep current on new data analysis techniques and open resources. They will be able to make use of the genuine value of real-time big data for proactive genetic research decision-making in this manner.

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