Editorial: Curriculum Applications in Microbiology: Bioinformatics in the Classroom

John Naisbitt stated in his 1982 book Megatrends, “We are drowning in information but starved for knowledge.” The statement, made nearly 40 years ago, seems acutely applicable in today's scientific and academic world. Reviews by Barba et al. and van Dijk et al., provide a nice historical perspective on the growth of sequencing technology. Over three decades, sequencing technology has improved greatly from 1987 when the first ABI automated sequencing machine went to market up through the mid 2010s when next generation sequencing platforms from 454 Life Sciences, Illumina and other companies were outputting up to 1,800 Gb per run (Barba et al., 2014; van Dijk et al., 2014). Technology has since progressed even further with the development of long-read and single molecule (Pacific Biosciences, Illumina, Oxford Nanopore, and 10X Genomics) sequencing systems that can output terabytes of data, per run, in a matter of days (van Dijk et al., 2018). Specifically, in the areas of genomics, proteomics, and transcriptomics, we are now producing upwards of 1 zetta-bases/year (Stephens et al., 2015). The explosion of data has increased the demand for hardware and software development to manage and analyze the data as well as qualified personnel in bioinformatics to sift through the outputs to draw meaningful conclusions. A report from Reports and Data states the global bioinformatics market is projected to reach 18.96 billion USD by 2026 (Reports and Data, 2019) and this means re-thinking not only how we store data but how we train the next generation of scientists. The greatest needs identified in various surveys compiled by the NSF, ELIXIR-UK, and EMBL-ABR include: (i) data quality and control (ii) data analysis skills in visualization and interpretation, (iii) data mining, manipulation and management, (iv) analysis reproducibility, and (v) statistics (Kanwal et al., 2017; Kim et al., 2018; Attwood et al., 2019).