An introduction to data science tools used in analyses of Environmental-Omics datasets, covering conceptual and practical aspects. This course will establish foundational scientific programming skills (Unix command line, R, Python, Git), and introduce common bioinformatic tools used in metabarcoding, metagenomic, and metatranscriptomic studies of marine, terrestrial, and host-associated ecosystems.
Athena Title
Environmental Genomics
Semester Course Offered
Offered spring
Grading System
A - F (Traditional)
Student learning Outcomes
Upon successful completion of this course, the student will understand fundamental principles and approaches in Environmental-Omics and be able to apply these to their own graduate research.
Upon successful completion of this course, the student will be able to design customized bioinformatics workflows to test ecological and evolutionary hypotheses in metabarcoding, metagenomic, and transcriptomic studies.
Upon successful completion of this course, the student will be able to explain the utility and limitations of public sequence database resources for environmental studies of largely undescribed organisms.
Upon successful completion of this course, the student will be able to evaluate different software tools and be able to choose the most appropriate one for their research needs.
Upon successful completion of this course, the student will have developed a working knowledge of foundational programming skills (Unix, R, Python, Git), and be able to apply these skills in a practical data analysis exercise
Upon successful completion of this course, the student will be able to compare and contrast analytical tools and sequencing approaches used in different types of Environmental-Omics studies.
Upon successful completion of this course, the student will be able to design a rigorous Environmental-Omics study, including fieldwork, hypothesis testing, sequencing strategy, and bioinformatic data analysis approaches.
Topical Outline
The Unix Shell
Version Control with Git
Programming with Python
Programming with R and R Studio
HPCC computing and introduction to the GACRC
Intro to Environmental-Omics workflows, and compare and contrast methodological approaches
Current landscape of sequencing platforms and technologies (long-read vs. short-read)
Metabarcoding study design and computational workflows
Metagenomic study design and computational workflows
Metatranscriptomic study design and computational workflows
Machine Learning and Artificial Intelligence software tools
Environmental-Omics Capstone Project – design a data analysis workflow and apply it to a problem of relevance related to the student’s graduate research; students will present their approach and analysis results to the entire class
