Bioinformatics with Life
The explosion of data from high throughput biological experiments like sequencing and micro-arrays has led to the science called Bioinformatics. Bioinformatics is an interdisciplinary science that is similar to Data Science for solving biological problems and a big deal with data are managed, analyze, annotate, compared, and many other forms of biological data treatment.
Many fields are merged in Bioinformatics and Computational Biology like:
“Bioinformatics is an interdisciplinary science, ultimately aiming to understand biology”.
Bioinformatics is an interdisciplinary field that develops and applies computational methods to analyze large collections of biological data, such as genetic sequences, cell populations, or protein samples, to make new predictions or discover new biology. The computational methods used include analytical methods, mathematical modeling, and simulation. and many other examples according to your thinking of mind.
Our human body can be broken down into small types of machinery of cells which is involved in complex processes. These cells are controlled by the central processing unit called DNA (De-oxyribo Nucleic Acid). Understanding DNA can reveal a lot about the organism as well as the chances of diseases in the future. Current technologies including NGS (Next Generation Sequencing) have generated a large amount of data. These big data (Genome, Transcriptome, Proteome, and Metabolome) should be organized into databases and must be analyzed. The outcomes of analysis of these large data (termed as Big Data) are utilized in healthcare, preventive medicine, and drug discovery.
Bioinformatics with Data Science:
If you remove the domain-specific requirements from the bioinformatics skill set, you are left with most of the data science skillset and some more. People who make the switch from bioinformatics to data science will most likely need to adapt to the data organization and distribution environment of their employer. The problems will be from a different domain, so they would need to adapt to that as well. But the same would be true, at least to some degree, for the data scientists switching jobs between different employers.
In my opinion, a bioinformatician who has regularly done end-to-end data analysis from raw data to insight will have no trouble performing in a “data science” position, I should emphasize that I’m not saying any bioinformatician can perform in data science roles. They may have to learn new domain-specific knowledge or tech stack (Hadoop, spark, etc.) relevant to the data organization of the company, but learning new approaches and implementing them is what they routinely do during their Ph.D. training and postdoctoral work. I think if you have the core skills mentioned above with a willingness to adapt and learn you can perform in any data analysis-related domain.
Application of Bioinformatics
Bioinformatics is applied in various areas like molecular medicine, personalized medicine, preventative medicine, gene therapy, drug development, waste cleanup, climate change studies, alternative energy sources, biotechnology, antibiotic resistance, forensic analysis of microbes, bio-weapon creation, crop improvement, and many other in plant biotechnology, health biotechnology, structural bioinformatics, system biology, integrative biology, drug discovery, computational biology, computational chemistry, a crystallographer, protein structure prediction, microbiology, genomics, proteomics, molecular cell biology, bioinformatics scripting development end, user end, molecular genetics, Evolutionary biology, NGS, neural networking, artificial intelligence, biomedical engineering, machine/deep learning and many more.
Written by: Muhammad Mazhar Fareed