Kerksick Chad M, Tsatsakis Aristidis M, Hayes A Wallace, Kafantaris Ioannis, Kouretas Dimitrios
1Department of Exercise Science, School of Sport, Recreation and Exercise Sciences, Lindenwood University, St. Charles, Missouri; 2Department of Forensic Sciences and Toxicology, Laboratory of Toxicology, Medical School, University of Crete, Heraklion, Greece; 3Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts; 4Spherix Consulting, Inc., Bethesda, Maryland; and 5Department of Biochemistry and Biotechnology, University of Thessaly, Larissa, Greece.
J Strength Cond Res. 2015 Jan;29(1):270-8. doi: 10.1519/JSC.0000000000000730.
The past 2-3 decades have seen an explosion in analytical areas related to "omic" technologies. These advancements have reached a point where their application can be and are being used as a part of exercise physiology and sport performance research. Such advancements have drastically enabled researchers to analyze extremely large groups of data that can provide amounts of information never before made available. Although these "omic" technologies offer exciting possibilities, the analytical costs and time required to complete the statistical approaches are substantial. The areas of exercise physiology and sport performance continue to witness an exponential growth of published studies using any combination of these techniques. Because more investigators within these traditionally applied science disciplines use these approaches, the need for efficient, thoughtful, and accurate extraction of information from electronic databases is paramount. As before, these disciplines can learn much from other disciplines who have already developed software and technologies to rapidly enhance the quality of results received when searching for key information. In addition, further development and interest in areas such as toxicogenomics could aid in the development and identification of more accurate testing programs for illicit drugs, performance enhancing drugs abused in sport, and better therapeutic outcomes from prescribed drug use. This review is intended to offer a discussion related to how bioinformatics approaches may assist the new generation of "omic" research in areas related to exercise physiology and toxicogenomics. Consequently, more focus will be placed on popular tools that are already available for analyzing such complex data and highlighting additional strategies and considerations that can further aid in developing new tools and data management approaches to assist future research in this field. It is our contention that introducing more scientists to how this type of work can complement existing experimental approaches within exercise physiology and sport performance will foster additional discussion and stimulate new research in these areas.
在过去的二三十年里，与“组学”技术相关的分析领域出现了爆炸式增长。这些进展已经达到了这样一个程度，即它们的应用能够且正在被用作运动生理学和运动表现研究的一部分。这些进展极大地使研究人员能够分析极其庞大的数据集，这些数据集能够提供前所未有的大量信息。尽管这些“组学”技术提供了令人兴奋的可能性，但完成统计方法所需的分析成本和时间是巨大的。运动生理学和运动表现领域继续见证使用这些技术任意组合的已发表研究呈指数级增长。由于这些传统应用科学学科中有更多的研究人员使用这些方法，从电子数据库中高效、周全且准确地提取信息的需求至关重要。和以前一样，这些学科可以从其他学科中学到很多东西，那些学科已经开发了软件和技术，以在搜索关键信息时迅速提高所获得结果的质量。此外，对毒理基因组学等领域的进一步发展和关注，可能有助于开发和识别更准确的检测项目，用于检测非法药物、运动中滥用的提高成绩药物以及处方药使用的更好治疗效果。本综述旨在讨论生物信息学方法如何在与运动生理学和毒理基因组学相关的领域中协助新一代的“组学”研究。因此，将更多的重点放在已经可用于分析此类复杂数据的流行工具上，并突出其他策略和注意事项，这些可以进一步帮助开发新工具和数据管理方法，以协助该领域未来的研究。我们认为，让更多科学家了解这类工作如何补充运动生理学和运动表现中的现有实验方法，将促进更多的讨论并激发这些领域的新研究。
