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IPSA SCIENTIA POTESTAS EST
Knowledge itself is power
The year 2003 will be remembered as a year when humanity made a tremendous leap forward. That year brought with it one of the most significant achievements in the history of science. On April 14, 2003, scientists from the National Center for Human Genome Research announced that they had finally completed mapping the entire human genome. The fallout was immediate; a broader understanding of disease, evolution, and advanced forensics now became a real possibility. At that moment, science was entering an important era. And thus, Bioinformatics had arrived.
The field of Bioinformatics continues to grow today. Scientists are now exploring everything from cancer causing mutations to medication design, detailed genetic testing to comparisons of genetic relatedness among species, synthetic biology and more. But all of these advancements come at a hefty price. From a backend perspective, several issues present a number of technological challenges for scientists in the field. Moreover, Bioinformatics scientists are forced to continuously develop new techniques for data management and analysis in order to meet the growing demands, without exceeding current technological restraints.
Summary
Bioinformatics has experienced a series of notable transitions over the past fifteen years or so. Extraordinary applications have recently begun to emerge in areas of molecular medicine and microbial genome applications, and also agriculture, forensics, and comparative studies. Many of these advancements have created excitement within the science community. Next generation sequencing makes the possibility of highly effective personalized medicine, gene therapy, and preventative treatment just a short time away. With more potential still on the horizon, what are the possible limitations to these technologies? According to one source, “The global market for bioinformatics is forecast to reach US$5.0 billion by the year 2015. Key factors driving market growth include substantial development in the area of genomics, increasing application of genomics in R&D processes, availability of huge genomic information, and continuing demand for new drugs.” (PRWeb, 2011) It should be noted – while the Bioinformatics industry has had sustainable financial success on global scales, it has been especially successful in the U.S. A great portion of the Bioinformatics market is concentrated in the United States.
Indeed, growth is a positive indicator. But growth is usually accompanied by high levels of expectations. Technical and training related challenges (often coupled with unrealistic expectations) present a unique set of circumstances for researchers and scientists in the field. As noted, “The pace of discovery and wider applications in medical biotechnology were not delivering against high expectations, with the realization that the otherwise productive ‘shift from craft-based to more industrialized experimentation’ encountered bottlenecks downstream in the discovery process.” (Ouzounis, 2012)
Implications
Biological information is compiled of huge amounts of computational data. As data accumulates over time, so too must adequate technology be available to manage and process that biological information efficiently. Today’s modern computer systems are certainly equipped with potent processing power and substantial amounts of data storage. Plus, hardware capacity grows at exponential rates (ref. Moore’s Law, Kryder’s Law, and Butter’s Law). But biological data is collected in raw forms. The goal of Bioinformatics is to utilize the maximum potential of currently available hardware in order to develop effective tools for the reconstruction of raw data into concise, readable formats that is useful in biological analysis and comparison.
Managing these vast amounts of data has been one the most challenging obstacles of all. In recent past, “there has been no standard approach to collecting the data, assessing its quality or describing identified features.” (Ouzounis, 2012) In some cases, older mathematical models designed for bioprocessing large data sets are vastly inefficient; resulting in biased conclusions, errors in analysis, and poor computational performance. For example, multiple sequence alignment is still a challenging method because of its computing intensity, which manifests with the advancement of the next-generation sequencing. (Zhang et al., 2010) In addition, downloading, updating, and backing up large databases across the internet can be quite cumbersome. Current Bioinformatics infrastructures for sharing genome databases make this unavoidable. And furthermore, computational biology software aimed at data management, gene sequencing, phylogenetic inference, and so forth, has saturated the market. That is, the constant stream of new Bioinformatics software makes software selection rather difficult. There is certainly a need for a standard-based approach towards more robust software development and software interoperability. (Ranganathan et al., 2010)
Resolutions
The truth of the matter is – there isn’t one easy solution. It will require several initiatives from various sectors of Bioinformatics and Information Technology communities. To tackle some these challenges head on, a combination of backend improvements along with a complete revamping of current system infrastructures may be needed. For one, developing more efficient algorithms and mathematical models for genome analysis could reduce rigorous processing times. Replacing outdated user interfaces/platforms with these newer models would not only enhance the user experience, but ensure high accuracy and optimal functionality.
From a technological standpoint, cloud computing could also relieve some of the tedious bottlenecks of database sharing over the internet. (Stein, 2010) Secondly, narrowing the scope of available platforms can create ideal settings for standardized systems and collaborations among Bioinformatics establishments. In this regard, open source Bioinformatics platforms are most useful, as it allows end users to contribute to the improvement of master platforms. Perhaps even a standard web based model would be the best solution for a uniform approach, setting in motion good, cooperative sustainable progress.
Conclusions
Bioinformatics scientists must continue to adapt and remain in a contingent state of improvement in order to keep up with the ever-sophisticated field of computational biology. Even though the industry has been recipient of much success, there is definitely room for improvement. Moving forward, Bioinformatics researchers and scientists must concede to the notion that older models are no longer adequate. Improvements are needed. This can only be achieved through willingness, cooperation, and innovation. Once again, expectations are high for Bioinformatics. Now is time for us to answer the call.
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