Big Data: The Biggest Trait In Biology

Big data has prominent uses in Bioinformatics to visualize the data related to like proteomics, molecular pathways, metabolomics and more. Read here!

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Big Data is one of the promising research that can be the best solution in the field of bioinformatics and is also a “Self-start” for Researchers!

Big Data in Bioinformatics can be used to visualize the different types of data in bioinformatics like proteomics, molecular pathways, metabolomics, pharmacogenomics, and genomics. And as there will be more and more data generation in bioinformatics, there will be an increased demand in Big Data analytics.

Big Data: The Biggest Trait In Biology

If we peep back a decade ago, the field of sciences did not generate much data, hence nobody even the scientists had no troubles when it came to managing data and keeping up with it. But once the Human Genome Project was completed, the advancements in genomics and other technologies started to boom. And the biologists today are hampered with loads of Data which is increasing at a rapid speed. This massive amount of data is essential in the pharmaceuticals sector and healthcare fields.

And to overcome this bottleneck of all the overburdened data, many biologists and computer scientists are joining their hands with the Big Data Club.

The Growth of Data –

Biologists and life scientists are struggling with enormous data sets and are facing many challenges like handling the data, processing it and moving the data from one source to another. To come upon the estimation of the current year 2020, the growth of data generation may reach 4 trillion+ zettabytes. This estimation is clear to understand that a single sequenced human genome is about 140 gigabytes of data.

The choice and definition of keywords used to classify and retrieve data matters enormously to their subsequent interpretation. Linking diverse datasets means making decisions about the concepts through which nature is best represented and investigated.

In other terms, we can say that the networks of concepts associated with data in big data infrastructures should be viewed as theories like ways of seeing the biological world that guides scientific reasoning and the direction of research, which are often revised to take into consideration of the new discoveries.

Reshaping Biology with Big Data:

Big Data in biology is potentially transformative. There are many giants who have based their success on Big Data and its analysis. Industries and institutions can expect a lot from Big data which will be resulting in outcomes of improving efficiency, performance, and an increase in revenue. When it comes to Biological communities, they are not immune to this process and are facing a data-driven transformation which need to be actively addressed. Data sciences usually describes Big Data as data having four main characteristics – The 4 V’s


In biosciences, technologies are allowing a constant increase in data production of patients, samples, tissues which are sequenced and re-sequenced in bulk. Single-cell sequencing is expected to further skyrocket the amount of data produced. Big data domains are those which can store data in the order of magnitude of Peta to Exabyte.


It refers to speed efficiently transferring big files. Extreme data volumes require extreme remedies. In fact nowadays even with hi-speed internet, uploading 100 petabytes over the internet would take about 30 years. This shows itself the importance of Big Data.

These two V’s – Volume and Velocity and not yet a problem in Biosciences, but the other two V’s are still a challenge.


Veracity is about data uncertainty. Biases are inherent to genomic sequencing data and are occurring naturally due to error rates, using different statistical models and experimental batch effects.


It is the most impacting characteristic of bioscience data, data from this domain comes in many different forms. Since biological data is heterogeneous, and to this respect, Big Data means different signals and detection systems from the same source. But somewhere this heterogeneity of biological data makes data integration more interesting and needy for refining the data and discovering unpredictable results from it!


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