Big Data Meets the Body

In April 2003, the International Human Genome Sequencing Consortium announced that the human genome had been successfully mapped [1]. This accomplishment was a milestone in scientific history and is one of the biggest successes to date in the field of bioinformatics. Bioinformatics is an evolving discipline which merges biotechnology and computer technologies in order to “organize, link, analyze, and visualize complex sets of biological data” [2]. While mapping the human genome is an impressive example of a breakthrough in bioinformatics, it is still one among many previous and ongoing developments in the field. For instance, in the Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech, the Bio-Medical Informatics and Bio-Imaging Laboratory is working to develop “guidelines for choosing bioinformatics tools,” “algorithms for integrated analysis of bioinformatics data,” and software tools “that leverage distributed computing resources for Big Data analysis” [3].

As per these goals, the Bio-MIB Lab has developed a number of bioinformatics tools. One such tool is caCORRECT, a web-based system designed to ensure the quality of high throughput –omics data. caCORRECT works by removing noise artifacts observed in microarray gene expression data to greatly improve the quality and reproducibility of data. Tools like caCORRECT are incredibly useful for ensuring not only the quality of biological data, but also the reliability of experimental results [3].

Another Bio-MIB Lab bioinformatics tool is OmniBiomarker, an online utility that seeks to improve the reproducibility of biological data, specifically biomarker identification. OmniBiomarker uses the National Cancer Institute’s Thesaurus of Cancer and Cancer Gene Index to identify the most clinically relevant gene ranking algorithms. In practical terms, OmniBiomarker can help “improve downstream prediction performance for applications such as cancer diagnosis” [3].

Bioinformatics is an incredibly dynamic field with limitless relevance to biomedical research. Researchers have already encountered great success using bioinformatics tools, like caCORRECT and OmniBiomarker, to improve the quality, reliability, and reproducibility of high throughput –omics data while translating raw data into reliable tools for clinical applications. As the development of widely accessible bioinformatics analysis platforms continues, the number and range of possible clinical applications grows. At the same time, the revolution in bioinformatics symbolized by the Human Genome Project is clearly still underway as dedicated researchers continue to work on optimizing the organization and analysis of biological data.

References

[1] International Consortium completes human genome project: All goals achieved; New vision for genome research unveiled. 2003. NIH News website. http://www.nih.gov/news/pr/apr2003/nhgri-14.htm.

[2] What is bioinformatics? Rochester Institute of Technology website. http://www.rit.edu/cos/bioinformatics/

[3] Wallace H. Coulter Department of Biomedical Engineering. Bio-Medical Informatics and Bio-Imaging Lab website. http://www.bio-miblab.org/research_bioinformatics.php

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