NS 4300
Big Data in Molecular Nutrition
This course will cover fundamental concepts of big data analysis at an introductory level in the context of gene expression at the mRNA and protein levels. Programming in R will be required, but no prior experience is necessary. Programming in this course will focus methods to parse large data sets and perform informatics analyses.
Topics
- Students will be able to run R on their own systems and access and use programs written in this language.
- Students will be able to constructively critique potential hypotheses or conclusions based on mRNA or protein abundance patterns.
- Students will be able to find public data sets and determine the numerical format of public data sets.
- Students will be able to hypothesize the impacts on metabolism of up- or down- regulation of proteins and transcripts corresponding to genes of metabolic pathways.
- Students will be able to propose pairs or groups of genes that regulate aspects of metabolism based on their mRNA or protein abundance patterns.
- Students will be able to: (1) run Python and R on their own systems and (2) access and use programs written in these languages.
- Students will be able to: (1) find public data sets and (2) determine the numerical format of public data sets.
Official roster learning outcomes
Prerequisites & course information
BIOMG 1350 or BIOG 1440; NS 3200 or BIOMG 3300; STSCI 2150 or PUBPOL 2100 or AEM 2100; or equivalents.
Sources
- Course catalogue ↗2026-2027
- Catalogue entry ↗
Catalogue variants
- Proteins, Transcripts, and Metabolism: Big Data in Molecular Nutrition · 3.0 · Undergraduate