The growth of the digital realm and development of the uses of big data in business, education, government, and healthcare yield many challenges for programmers and researchers. More specifically, the challenges big data presents to bioinformatics are due to the wide range of data results and unstructured patterns.

The goal of bioinformatics is to develop software and tools that can be used by other researchers in biotechnology to study selected data and discover hidden patterns. Bioinformatics is a branch of computer science that focuses on the analysis of genetic sequences and other genomic data. It was founded in the early 1960s following the application of computational methods to sequences of proteins.

The bioinformatics developed due to parallel advances in two fields: molecular biology, which made it easier to manipulate DNA and sequence it, and computer science, which led to increasingly powerful and miniaturized computers, and new software that suited bioinformatics tasks more effectively.

According to the International Society for Computational Biology bioinformatics is used by three categories of people: bioinformatics user, bioinformatics scientist, and bioinformatics engineer.

By using the latest techniques, skills, and tools necessary for computational biology practice, the above three categories apply statistical methods in the contexts of molecular biology, genomics, medical research, and population genetics research, and general biology, to profoundly understand at least one area of biology, and the biological data generation technology.

More specifically, a bioinformatics scientist must identify and define the computing requirements appropriate to solve a problem, while a bioinformatics engineer must apply mathematical foundations, algorithmic principles, and computer science theory to model and design computer-based systems that demonstrate an understanding of the choices involved in design choices.

During the 1990s and 2000s, major advances in sequencing technology were combined with reduced costs, resulting in an exponential growth of data. The advent of big data has created new challenges in terms of data mining and management, requiring more expertise from the field of computer science. In conjunction with an ever-increasing number of bioinformatics tools, biological big data continues to have profound impact on the predictive power and reproducibility of bioinformatics research. University biology programs are now fully integrating this discipline into their curricula as part of handling the challenges.