Applied Bioinformatics

Graduate

Principles of Bioinformatics and Big Data FAIR Principles (Findable, Accessible, Interoperable, Reusable) Complete introduction to the R statistical programming environment; designed throughout to be comfortable for participants who are new to R, bioinformatics and biostatistics. Introduction to applied statistical modeling Planning Omics Experiments, Accessing the UNIX Environment, Identifying Differentially Expressed Genes, Pathway Analysis of Gene Expression Data, Taking Advantage of Publicly Available Data, Ensuring Rigor and Reproducibility, Creating Publication Quality Visualizations of Complex Data, Sharing Code and Data, Analyzing Single-Cell RNA-seq Experiments, Analyzing Microbiome Data, Documenting Statistical Approach in a Publication, Developing a Data Management Plan Applying Machine-Learning and Data-Driven Approaches to Gene Expression Data, Microbiome Analysis: 16S Analysis A substantial amount of the course is dedicated to independent work on assigned problems. Offered Spring.