It is said that ‘’behind every great scientific finding of the modern age, there is a computer’’^[1]

For all living organisms, DNA is the code of life, it is made from about 3 billion nucleobases; adenine (A), thymine (T), guanine (G), and cytosine (C), and the combination of these nucleobases makes up the genome.^[2]

This combination can explain why some people have different hair or eyes colour, while others have the same, and many other phenotypic qualities, but to understand the interaction between these nucleobases is another thing-that is still being researched-here is where computers appears to be helpful, and where bioinformatics is born.^[2]

Bioinformatics will merge biology, computer science, mathematics, and statistics, in order to understand the biological data^[2] at high scale^[4]. The need of bioinformatics comes from the presence of huge datasets that only computers can analyse and track its trends.^[3]

Utilizing the software to help in biology is aided with the fact that some biological problems have a computational nature, where they can be represented computationally.

The use of coding in biology and life sciences is controversial for some, some say that it only serve as a technical service, while others think it is the heart of bioinformatics, thus, a full understanding of the code with its multiple functions is needed for the full picture to be formed.^[4]

Code roles

• Abstraction, where irrelevant data can be hidden, this role will serve as a value in the aspects of the system, infrastructure, application, and orchestration.
• Subdomains, which are areas that is categorized into core, supporting, and generic subdomains.
• Communication, in a machine and human level, using the code to communicate a problem and its solution, and not only to order the computer to do a certain task.

Mostly, the bioinformatics code will be found across all the roles and their levels. Helping in:

• Modelling intrinsically computational biological elements.
• Engineering scalable and reproducible solutions; aiming to process the continuously growing amount of biological data.
• Distributing the data across the servers.^[4]

Applications examples

• Predicting cancer development, and helping in early diagnosis, computational methods will analyse the large genomic data, as cancer causing mutations, to find variants by looking at hundreds or thousands of genomes and comparing between them, which can’t be done manually.
• Understanding the relationship between hundreds of environmental factors and the genome (genotypic-phenotypic), through statistical genomics and systems genomics.^[3]

Nowadays coding (software programing) plays an essential role to track and understand the patterns in the genomic data, this new approach to explore the DNA has affected how healthcare is being shaped for the future, as in personalized medicine. Thus, research is still ongoing in this field to increase the understanding of how coding can help with large biological data, where the outcomes of it will be important in aspects like prevention, early diagnosis, and treatments of the diseases.