For me, I had an extensive background in computers about an aligner. I taught myself Basic at age 7, started using Linux around age 12, and I enjoyed all things electronic. However, just being a Systems Admin/Engineer, and later a developer wasn’t fulfilling to me. I also worked primarily in security, and it just wasn’t exciting after a while. So I left and (after a lot of training) became a volunteer Firefighter/Paramedic. So, I enjoyed learning about humans, pharmacology, chemistry, etc.
Aligner: Bioinformatics And Geometrics
To keep it short, Genomics is nowadays a sub-topic for research in Bioinformatics. Bioinformatics is a highly interdisciplinary area where you try to solve biological problems by developing algorithms that will use the original features of the problem as input and try to make an informed prediction about the outcome for the challenge. The elements can be anything like DNA sequence, Protein sequence, Gene expression data, Interaction information, also Codon frequency, etc. Also, Depending on the features supplied, different algorithms try to find a solution to the same problem in different ways and ultimately contribute to the predictive power for that particular problem. If the prediction accuracy is high enough, experimental researchers would be more keen on investing some time invalidating the predictions using wet-lab techniques, which can also be iteratively incorporated into the existing algorithms to improve their accuracy.
However, This ongoing process of improvement of algorithms and novel methods for solving seemingly insurmountable biological problems has made Bioinformatics, the leader of Health Science and Drug industries. On the other hand, if the problem in hand requires analysis of Genomes using computational methods, the problem gets classified as a “Genomics problem.” But, still, it is a part of Bioinformatics as long as it uses a purely computational approach to find a solution.
Bioinformatics And Neuroinformatics
Bioinformatics is an umbrella term describing the application of computer science and statistics to problems in biology. Neuroinformatics can consider as being within the scope of bioinformatics in general; it has a slightly narrower range in that it establishes a domain of focus. Put simply, it’s a subspecialty.
Another way you could think about it. It is that all neuroinformatics could generally describe as bioinformaticians. But not all bioinformaticians are neuroinformaticians. Of course, you may be wondering what that translates to practice. One way that comes to mind is that the data a traditional bioinformatician (i.e., one that works in the `omics space) tends to work with is different from a neuroinformatics. For example, a bioinformatician may regularly work with whole-exome sequencing data to establish a genetic association between some genes and a given disease phenotype. In contrast, a neuroinformatics may work exclusively with MRI imaging data to create an association between specific patterns of brain activity and a behavioral phenotype.