Renuka Suravajhala and Prashanth Suravajhala
Amrita School of Biotechnology, Amrita Vishwa Vidyapeetham, Amritapuri 690525, Kerala, India
Correspondence: prash@am.amrita.edu
Ever since the word ‘Theoretical Biology’ was coined by PaulienHogeweg in1978, bioinformatics, the current word has steadfastly come into existence with many biologists taking a leaf out of this discipline. Researchers by now know that bioinformatics is a mere tool, whereas its sister concern, computational biology, is deemed as a discipline. With bioinformatics burgeoning in the late 1990s, we relate the commencement of data deluge to the animistic knowledge that bioinformatics has brought in, lessening the scale of experimentation. Authentic bioinformatics, however, will not gain significant interest for researchers, at least until the wet-laboratory biologists take a leap forward in acclimatizing the split half-term in bioinformatics. The figure of dogmas is pivotal in bringing the collaboration between biologists and cross-disciplinarians across biology as the event of dogmas in turn has introduced a plethora of new relationships between scientific studies and molecular biology. In effect, researchers have asked several questions on specialized mechanisms, if any that may be discovered in the advent of bioinformatics knowledge. This collaborative knowledge owes its impetus to the differentiation of an independent eccentric science, viz. systems biology (SB). So to ask whether bioinformatics into the enunciation and practice of the bioinformatics tools and scientific methods is a candid query.
Bioinformatics, since ages, has created a process of reasoning that was certainly not dependent on biology alone. Prior notions of intelligent algorithms clubbed with statisticians’ skills, IT scientists’ inclination, physicists’ predictions, chemists’ corner, and mathematicians’ mind are a necessity to perform bioinformatics research. Not all disciplines can be made up by an individual alone but needs unicentric efforts to meet the goals to derive bioinformatics knowledge. For example, the next generation sequencing (NGS) technologies have enabled non- sanger based sequencing technologies with an unprecedented speed, thereby enabling novel biological applications. However, before bioinformatics and NGS stepped into the limelight, it must be noted that the NGS had overcome torpor in the field with the help of several cross-disciplinarians. It would never have been easy to stir up this understanding without the rapid involvement of the multi- faceted scientists who have transformed biology as a whole. This obviously has advantages of building up cross-disciplines, thereby deepening the knowledge curve between eccentric biology and information science, the latter constantly teaming up with the former to signify its discoveries with dogmas. As bioinformatics has moved up the value chain, it is time we realise it’s potential in the areas of integrated systems genomic approaches ( Figure 1)
Figure 1: Bioinformatics moving up the value chain in the real of integrated systems genomic approaches
During the first three decades of nascent bioinformation, the greatest challenge faced by the molecular biology and biochemistry community was to make sense of the wealth of data produced by genome sequencing projects. Thanks to wonderful conventional biology research was deemed always to be in the laboratory, until the data deluge and explosion of genomic scale in the late 1990s. Thus, we are in an age of computing- to-research process. There are two different challenges one would pose: (1) sequence generation and (2) ensuing storage of the plethora of sequences generated in the laboratory with specific understanding and investigation using computers and artificial intelligence. That said, understanding the biology of an organism is a trivial issue as there are a number of focused research areas at different levels of ‘omics’-es, viz. Genomics, proteomics, functomics, transcriptomics, need to be carried out at different levels ( Figure 2). One of the foremost challenges today is to ensure that such data is efficiently stored, used through three forms of Es—extracting, envisaging, and elucidating this mass of data. A meaningful interpretation of such data must be done before one analyzes the complete volume for interpreting it or what we call ‘annotating’ manually. In conclusion, discerning the function using computer tools must be the focus so as to have meaningful biological information explained. A final goal set for this is to ensure we employ ‘bioinformatics/cheminformatics-first approach so that we lessen the scale of experimentation and make vivid use of research.
Long hail Bioinformatics!
Figure 2: The plethroa of omics’ technologies. ‘Ome’ means many and omics’ are the forces for applications in biology
Reference:
