Bioinformatics
Bioinformatics is the science that analyses large amounts of information through integrating other disciplines like computer science and statistics. The science stores and retrieves information while involving specialists such as molecular life scientists and biologists who analyze the biomolecular sequence data. The term has evolved to encompass chemical biology, computational structural biology, and system biology.Bioinformatics is applied to analyze data in the efforts of improving human health. The interdisciplinary study uses the biomedical data, knowledge, and information for decision making and problem-solving to contribute to the innovations in healthcare. For example, the science has been applied in the 100,000 Genomes Project the gaps in medical informatics. The data from patients involving the diagnosis, treatment, and prevention methods are bridged with biometrics data to find the possible solutions (Gu and Bourne,2009).
Expert scientists who are practitioners of bioinformatics are known as bioinformaticians. These scientists develop and conduct research using the bioinformatics approach that combines medicine, biology information technology, and health research. A study done by bioinformaticians found that the sequence of biological data is proliferating, thus leading to the development of bioinformatics tools and algorithms. The algorithms developed include the BLAST (primary local alignment sequence too), Clustal W Solutions, and FASTA that can be used (Gu and Bourne,2009) in combination with other techniques to analyze information quickly. The algorithms are used to obtain essential data on protein and gene levels in a time saving and cost-efficient way. Traditionally bioinformatics was used to store and analyze biomolecular sequence data. However, science is now utilized on a broad perspective because of the advancements.For instance, science has been used to develop future veterinary vaccines based on the new tools from sequenced biological data of the organisms.
Bioinformatics is essential because it analyzes the wealth of data produced by genome sequencing. In the past, the analysis could be carried out in the laboratory, but the increase in information requires the incorporation of computers in the research and analysis processes.
About two decades ago, biology and computer science were seen as two separate fields, but science combines the two fields in the reach processes. Bioinformatics is beneficial to biologists because the knowledge acquired can get utilized immensely in research and experiments (Gu and Bourne,2009). Also, science can be used in the preventive and precision medicine to develop healthcare techniques that can me use in the treatments and practices customized for individual patients. Bioinformatics allows the genetic alterations to be carried out in patients; thus the science can be used to develop measures to prevent the analyzed diseases. Moreover, science can be used to identify the disease before it happens. Early determination leads to treatment that helps improve humans’ health (Larranaga, 2006).
Bioinformatics can be approached by understanding the biology in genetics and genomics. The starting steps require one to study the carious synthetic processes of the DNA, RNA, and protein structures. At this stage, the learner can utilize data mining and machine learning processes techniques to analyze various patterns. Some of the present bioinformatics techniques are hidden networks include; Markov models and clustering. Uncovering biometrics requires scientists to understand statistics to analyze the data based on the requirements. According to (Gentleman,2014), the learner will need programming skills to determine the sequence of biological study,
Bioinformatics is a beneficial field because of its convenient and accurate verification processes. However, searching the database can be a time-consuming process. The inputs and outputs processes can be hard to manipulate. The dynamic fields make it hard to keep up with the variations and standard input files. Another disadvantage is the loss of information due to privacy issues. The genetic information can get lost, especially if there are no hardcore copies of the data. The reliability of the sciences can sometimes be questioned. Research shows that bioinformatics can be used for cloning, thus resulting to eugenic practices (Lesk,2019). Bioinformatics is responsible for genetically modified crops, thus leading to genetic variety among the consumers. Currently, bioinformatics can only present the outputs basing on the available inputs. Therefore, science can experience a lack of data and drawbacks on genuine data that can lead to pseudogenes. Searching the bioinformatics database system can be time-consuming. (Lesk,2019) Says that the results of the analysis are simulation-based science; thus, the prediction cannot offer accurate hypotheses without physical lab tests. The science cannot replicate the cell environment; therefore, bioinformatics tools can model experiments, but they cannot perform using them.
Bioinformatics includes the collection, storage, and retrieval of biological data. The knowledge extracted can be manipulated and modeled for analysis and prediction. The visualization process is done through the development of software and algorithms. The benefit of science is that it combines biology, computer science, and statics to attain results. The science is beneficial because it can be used in medical sectors to create preventive measures against grave diseases like cancer. The benefits of bioinformatics include; convenient verifications, large accessible databases, time efficiency, and accurate verifications. However, the discipline has disadvantages such as; questionable and pseudo results, loss of data, identity theft, and time-consuming analysis processes.
References
Lesk, A. (2019). Introduction to bioinformatics. Oxford University Press.
Larranaga, P., Calvo, B., Santana, R., Bielza, C., Galdiano, J., Inza, I., … & Robles, V. (2006). Machine learning in bioinformatics. Briefings in bioinformatics, 7(1), 86-112.
Gentleman, R. C., Carey, V. J., Bates, D. M., Bolstad, B., Dettling, M., Dudoit, S., … & Hornik, K. (2014). Bioconductor: open software development for computational biology and bioinformatics. Genome Biology, 5(10), R80.
Gu, J., & Bourne, P. E. (Eds.). (2009). Structural bioinformatics (Vol. 44). John Wiley & Sons.
