Data Science Challenges and Solutions for Student Microbiome Research

Microbiome studies utilize sequencing of the conserved 16S ribosomal RNA (rRNA) gene. The steps of this analysis are sample collection, DNA extraction, PCR amplification, sequencing and analysis of sequenced data. There are few undergraduate biology genomics or computational biology courses implementing this type of microbiome analysis. Faculty lack the required data science skill set to teach and train students or the computational resources needed to run an analysis. Here, a three day workshop for undergraduate students was designed to perform both the wet-lab and computational steps for microbiome analysis. In the wet lab, experimental design and sample collection was planned out. Students learned an introduction to basic command line skills from an adapted Software and Data Carpentry lesson to get confident in executing scripts. The bioinformatics pipeline, Quantitative Insights Into Microbial Ecology (QIIME) was used to perform microbiome analysis from raw DNA sequencing data. The challenged and solutions led to a development of an analysis in Jupyter Notebooks and motivation for the creation of a new DNA Subway line utilizing CyVerse resources.