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Advancements in Bioinformatic Technology

Paper Type: Free Essay Subject: Sciences
Wordcount: 1876 words Published: 8th Feb 2020

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The medical world depends on advanced technology and analytical techniques to be successful. In the 1990’s bioinformatics also known as computerized biology became the root of all biomedical research. When looking at this new and promising area of biomedical research (Bioinformatics), it deserves all applauds it is receiving because of how much it has proven itself a valuable asset. In the early 80’s, various methods used in genome sequencing became available for use in biomedical research, also sequencing at the molecular level experienced an exponential growth as well. Ever since the first microbial genome sequence in the year 1995, over 100 various living organisms had their genomes sequenced, though the procedure was still at its non-trivial state (Janssen et al., 2003).

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 Automated DNA sequencing has always been the starter and has had a massive effect on generations of biomedical data such as single-nucleotide polymorphisms (SNPs) and expressed sequence tags (ESTs). Consecutively, different methods such as serial analysis of gene expression (SAGE) and DNA microarrays have been developed to analyze the transcriptional program of a cell, tissue or organism at a genomic scale. (Velculescu et al., 1995)

 Sequence to Expression:

 The sole purpose of bioinformatics has been the processing, analyzing and displaying components of the nucleotide and protein sequences as well as their definitions. With the occurrence of different experimental techniques for large-scale, genome-wide transcriptional profiling via microarrays or gene chips, a new technique of presenting genome analysis emerged (Brilli, 2008). This new technique in the biomedical research community has given researchers more convenient procedures when experimenting on a cell, tissue or organ. As researchers experimented the microarray technology it eventually became obvious that simple data generation is not satisfying, and the challenges such as normalization analysis and visualization of results are increasing by the day. With these challenges being the case, an extensive progress has been made in the last couple of years to handle and analyze the millions of data points accumulated by state of the art microarray studies with tens of thousands of sequences per slide and maybe hundreds of slides (Brilli et al., 2008).

Integrative Genomics:

Genomes and their consecutive products do not operate autonomously. They all work hand in hand and are mutually dependent on each other to make and maintain the molecular system and network. The interpretation of these molecular systems/networks, their interactions, and properties need information from different areas in Bioinformatics to be successful. Such areas include proteomics, metabolomics or systematic phenotype profiles at the cell and organ level (Molidor et al., 2003). Technologies that are data based and computational have to be upgraded to ensure the interconnection and presentation of the data and technologies are successful and understandable. This ranges from the genomic data to the biological/molecular pathways (Molidor et al., 2003). The infusion of biological pathway information with the various gene expression studies for instance, has the tendency to show the various genes contained in those pathways under certain physiological conditions in a certain cell (Molidor et al., 2003). Also, intertwining a particular protein database to a certain genomic database can be difficult to answer when proteomic questions are asked. (Perez-Iratxeta, 2007). Advanced technologies that are computational must be created to allow biomedical researchers to create a relationship between genotype and study its corresponding biological functions, this will in turn produce new methodologies and hypothesis about physiological processes when biomedical research is being conducted.

Customized medicine:

Biomedical research conducted in the 20th century has given researchers a lot of solutions and cures to almost all of the major diseases of this era. But it is unfortunate that therapy does not give the researchers the desired results and sometimes it gives back some undesired side effects. Drugs of today have been used widely and have developed the tendency to show different effects on various individuals when used in therapeutic process. Research has shown that the effectiveness of these drugs differs among individuals due to various factors such as age, sex, nutrition rate, genetic makeup and environment (Molidor et al., 2003). With that being the case, the best solution is to focus on the therapies that are effective on a small portion of patients from a particular population which are showing the same disease phenotype and characterized by distinct genetic profiles. However upgraded methods in bioinformatics and analytical systems are required to increase the throughput and accuracy of drug effectiveness whiles the funding and complexity will be at a reduced rate. (Mancinelli et al., 2000).


The challenge any biomedical method faces in general is the ability to capitalize on the sunken potential to improve human health as well as their well being. Even though genomic-based analysis and computerized methods are vigorously saturating biomedical research, the challenges of creating a prosperous connection between genetic information and improved human health remain at large (Molidor et al., 2003).

Data Integration:

The exponentially broadening of the biomedical results, reduction of computation costs, widening of cyber access and the emergence of the genomic analytic methods has led to the growth of electronically available data. Today, global databases contain biomedical results that range from medical data records obtained from patients and stored in medical information systems to the genetic structure of various species stored in molecular biology databases. The amount and availability of this data has out grown this process, which has allowed organizations to meet specific or local needs without requiring them to coordinate and standardize their database implementations. This process has resulted in diverse and heterogeneous database implementations, making access and aggregation very difficult (Molidor et al., 2003).

High-performance computing:

The emergence of advanced technologies such as sequencing and microarrays requires a lot data to be managed, compared and analyzed. The analysis of this amount of data will in turn require a more advanced and high performing computing system which will increase the expenditure of the research. This is depicted by the correlation of the exponential increase of GenBank entries and the number of transistors integrated on a single chip. To ensure the steady progress of bioinformatics and its advantages even more powerful systems are required to be designed and implemented (Molidor et al., 2003).

Ethical, legal, and social implications:

The concern of bioinformatics and the desire to retrieve and analyze biological data in regards to biomedical research raise major issues in patient confidentiality and medical ethics. The commitment to incorporate data from different sources, such as hospital discharge records and clinical surveys, increases the arguments in regards to this topic. Even if anonymization is enforced, a specific person could be traced b ack either exactly or probabilistically, due to the amount of remaining information available (Molidor et al., 2003). Although the integration of additional clinical information would have the potential to dramatically improve human health, nonetheless, it is crucial to ensure that the availability of clinical phenotypic data does under no circumstances lead to the loss of study-subject confidentiality or privacy. Biomedical researchers have to pay absolute attention to these ethical, legal, and social implicational issues and should not view them as impediments (Mancinelli et al., 2000).


It is universally agreed that bioinformatics has paved the path to both the genome sequence era and has shown promise that it will play a large part in the molecular science of tomorrow. Today, new challenges to bioinformatics have emerged which include the integration and translation of the genome. This new challenge if not solved will lead to the creation of more personalized medicine. The current examination of these challenges has given biomedical researchers improved reports of computerized data that assist in the translation of the genomic information into biological meaningful knowledge. This basic translation of genomic information will allow the researcher to understand the functioning of organisms in health and disease even to molecular level. For this to be accomplished, certain efforts need to be put into the research to provide the mandatory strong framework for perfect performance in computing, sophisticated algorithms, advanced data management capabilities, and-most of all proper and experienced personalities to plan, preserve, and benefit from these environments. The main purpose of bioinformatics should be to transform biology from a qualitative into a quantitative science such as mathematics and physics. Although there are still significant challenges, bioinformatics along with biological advances are expected to have an increasing impact on various aspects of human health (Molidor et al., 2003).


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  • Janssen, P., Audit, B., Cases, I., Darzentas, N., Goldovsky, L., Kunin, V., Lopez-Bigas, N., Peregrin-Alvarez, J.M., Pereira-Leal, J.B., Tsoka, S., Ouzounis, C.A., 2003. Beyond 100 genomes. Genome Biol. 4, 402–402.
  • Mancinelli, L., Cronin, M., Sadee, W., 2000. Pharmacogenomics: the promise of personalized medicine. AAPS PharmSci. 2 (1), E4.
  • Molidor, R., Sturn, A., Maurer, M., & Trajanoski, Z. (2003). Mini-Review: New trends in bioinformatics: from genome sequence to personalized medicine. Experimental Gerontology, 381031-1036. doi:10.1016/S0531-5565(03)00168-2
  • Perez-Iratxeta, C., Andrade-Navarro, M. A., & Wren, J. D. (2007). Evolving research trends in bioinformatics. Briefings in Bioinformatics, 8(2), 88-95. doi:10.1093/bib/bb1035
  • Velculescu, V.E., Zhang, L., Vogelstein, B., Kinzler, K.W., 1995. Serial analysis of gene expression. Science 270, 484–487.


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