Bioinformatics: What the Clinical Laboratorian Needs to Know and Prepare For

Abstract

The introduction into the clinical laboratory of novel molecular diagnostics technologies that cover a wide variety of applications has occurred at a record-setting pace and has led to revolutionary changes in the field. Microarrays, for example, are routinely used in the clinical laboratory as the first line of testing for constitutional abnormalities associated with developmental delay and autism. In addition, microarrays employing millions of probes for each analysis are used for large-scale genotyping and for gene expression profiling in association with specific clinical algorithms. Next generation or massively parallel sequencing (NGS)6 is also becoming routine in the clinical laboratory for targeted whole-gene, exome, and genome sequencing. The amount of data generated by these analyses is unprecedented and requires a sophisticated knowledge of bioinformatics for the proper storage, analysis, and mining of these data sets. While clinical laboratories have experience with informatics and in handling large numbers of results, the systems used for those tasks are inadequate for handling the data from omics studies. In this Q&A, several leading investigators from academia and industry, who routinely use bioinformatics for omics studies, were invited to discuss the importance of bioinformatics and how clinical laboratorians can best prepare themselves for handling the increasing amount and complexity of data generated by their laboratories in conducting these studies. Bioinformatics and biostatistics are often used interchangeably despite substantial differences. Can you define these terms and indicate how bioinformatics may impact the clinical laboratory? Elizabeth Chao: Biostatistics applies statistics to biological topics. The emphasis is placed on using statistical analysis to design experiments involving large populations. Biostatistics highlights significant results through statistical inference, thus sorting out signal from noise. Bioinformatics, on the other hand, is a modern interdisciplinary science that unites biology, computer science, applied mathematics, and statistics into one discipline. The primary aim of bioinformatics …