Microbial bioinformatics
The exponential growth of publicly available biological data enables researchers to perform data-driven (hypothesis-generating) analyses as well as hypothesis-driven analyses. Biostatistics and bioinformatics are essential for handling complex and large biological data. In this courses, students will study about practical data skills for turning large datasets into reproducible and robust findings. Students will tackle biostatistics/bioinformatics problems using freely available open source tools (i.e. Unix Shell and R/RStudio). Students from all disciplines will use the biostatistics/bioinformatics methods to tackle problems in their fields (biology, material, architecture, urban design, climate, social science, and so on). For examples, students will use microbiome data from soils and built environments (subway stations, bus terminals, schools, hospitals, International Space Station, and so on) to assess taxonomic and functional compositions of microbial communities, antimicrobial resistance (AMR), and mobile genetic elements such as plasmids and viruses including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Furthermore, one may identify covariates which influence the microbial community structures of their samples: city, population density, building surface types and materials, climate, economic and social factors. For the molecular evolutionary analyses, students will compare publicly available sequences of antimicrobial resistance genes and genes encoding single-stranded DNA-binding proteins (SSBPs) for identifying conserved regions (motifs) and inferring the phylogenetic trees.