Big Challenges of Big Data analysis in natural science

               The big challenges of big data in natural sciences

Big data in natural sciences:

                        Big data is an excogitate phrase that describes any prolific amount of painstaking and un painstaking data that has the conceivable for information to become a crucial basis of competition, underpinning new waves of productivity growth is difficult to process using traditional database and software techniques.

 

Big Data challenges in natural sciences 

Biological exploration in the information age is immensely aggressive whether in an academic, government or a commercial setting. Staying current with the gospel relevant to an appropriate research area can be very difficult, but understanding with multiplex biology is often critical to accomplish key research goals – so researchers need every advantage they can get. The difference acquire access to the right answers and  not acquire it could be a failed grant application, a forsaken publication In most establishment scenarios the volume of data is too big or it moves too fast or it exceeds current processing capacity and potential help to companies to revamp operations and make agile, more intelligent decisions.

 

The data is composed from a number of commencement of biology like bioinformatics in which Data mountains and analysis are revamping the way evolution and rearing the biologists who get neither their feet nor their hands wet also like to used different databases to complete their work basis on establishment of big data and databases like BGI, NGS, Galaxy, NIH, OMIM, PubMed and cloud computing are large data sets are processed on remote Internet servers rather than on researchers so large data can achieved by multi user to do different task in biology therefore this data can be conquer, formatted, wield, stored and then analyzed with huge amount that can help a different organization to gain useful intuitiveness to increase revenues to get or recognize customers and improve Storing and interpreting.

 

Big data takes both real and virtual bricks and mortars are much of the virtual construction in which data and software are situated and users can achieve on demand, so that they do not need to purchase self-hardware and maintain it on site operations. Data- demanding techniques, now widely referred to as big data allow for unique ways to address complexity in science.

 

Big-data science is very from other deductive uses of information technologies like to prefers computer simulations that used to sketch the multiplex and contextual nature established by dispelling the popular myth so big data is concerned with modeling in data-intensive science that lead to characterized as hierarchical and nested structure familiar from more conventional approaches has recently become a “big-data science” mainly supported by the advances in high-throughput experimental technologies.

         Figure:  Summary of data distribution considered a challenge in every year

Data-intensive science consists of commandeer, establishment, analysis and research that encapsulate with more than 25 million references in PubMed, and over 1 million new articles being published every year may cause challenge in life science to manage data so researchers devastate trying to find the specific information they need from the scientific literature to enable their research. Advances in Next Generation Sequencing (NGS) technologies allow us to generate data at remarkable speed at whole genomes and whole transcriptomes instead of at the single gene level so this technology not only impacts on research, but also involve how medical care is provided. However, the biggest challenge for utilizing the power of such data is our limited ability to quickly and reliably obtain insights from this data.

 

Science is going rapidly changing in increasing capabilities of the computers and software tools used to handling big data raise many new researches challenges to pursue in systems biology genomes, transcriptomes, proteomes, metabolomes, interactomes so on and big data challenges are not only their size but also their increasing intricacy or datasets is driving the advancement of new tech­nologies for finding items of attentiveness in data from DNA sequencing.

 

Bioinformatics has become an active area of research in the supercomputing com­munity and become a data intensive science and as a consequence biology and computer science have become complementary to each other bridged by other branches of science such as statistics, mathematics, physics, and chemistry.

 

The combination of versatile knowledge has caused the advent of big-data biology, network biology, and other new branches of biology facilitate the system-level understanding of the cell or cellular components and sub processes. The purpose of this field is to understand organisms or cells as a whole at various levels of functions and mechanisms facing the challenges of analyzing big molecular biological data and huge biological networks.

 

This summary gives an overview of the progress in big-data biology, data handling, challenges in new interface and also introduces some applications of health networks DNA, RNA platforms and multivariate analysis in systems biology.

                           Big challenges of big data in natural sciences

About Author 

Mr. Imran Zafar has completed his Bachelor of Science (BS) degree in Bioinformatics from COMSATS Institute of Information Technology Islamabad Sahiwal campus under supervision of Dr. Ahmad Ali and Master of Science (MS) in Bioinformatics from Department of Bioinformatics and Computational Biology, Virtual University of Pakistan, Lahore, Punjab, Pakistan under supervision of Dr. Muhammad Tariq Pervez. For research work during BS and MS he has also done internships from School of Biological Science (SBS), University of Veterinary and Animal Sciences (UVAS) and Center of Excellence in molecular biology (CEMB) Lahore. He has published several research articles and book chapters in reputed journals recognized from Higher Education Commission (HEC) of Pakistan.  His research is mainly focused on the field of Bioinformatics, Genomics, Computational Biology and Molecular Biology in the domain of life science to performed computational analysis. He is now working in Ministry of Education as a Science subject instructor in the Department of Education Punjab, Pakistan.