Informatics Lab Modules:
Bowling, Schultheis, and Strome at Northern Kentucky University have published undergraduate in silico laboratory modules directed at investigating genes of unknown function in S. cerevisiae, resulting in the incorporation of an authentic research experience into a core genetics course (Bowling et al., 2015). The modules provide introductory explanations and directions for implementation of bioinformatics algorithms, including multiple sequence alignment, conserved domain identification, signal sequence prediction, and cellular localization, to determining the function of yeast ORFans. The modules, which direct students in interpretation of algorithm results, result in a final product of student generated hypotheses of gene function. Dr. Erin Strome has made the modules are available for public use through SGD(http://yeastgenome.org/) at http://wiki.yeastgenome.org/index.php/Educational_Resources
Module 1. Introduction to Saccharomyces cerevisiae
This module provides an introduction to the SGD site, and the sequence tools available at SGD.
Module 2: Structure-Based Evidence Part 1
This module guides students through finding functional predictions, based on homology to protein motifs and families, using the online tools TIGRFAM, Pfam, and PDB.
Module 3: Structure-Based Evidence Part 2
This module is a continuation of the previous module. Like Module 2, Module 3 guides students through finding functional predictions, based on homology to protein motifs and families, using databases and tools such as SUPERFAMILY, SMART, GENE3D, and PANTHER.
Module 4: Multiple Sequence Alignment
This module guides students through using protein sequence alignment tools, such as T-COFFEE and GENE CONTEXT, to view amino acid conservation and mutations.
Module 5: Cellular Localization Data Part 1
This module guides students through a number of algorithms used to predict protein membrane topology and cellular localization. The tools and algorithms use in this modules are: Transmembrane Helices Hidden Markov Models (TMHMM), TargetP (predicts the subcellular location of eukaryotic proteins), Phobius (a tool that combines data from TMHMM and TargetP to form a graphical output), and SignalP (predicts presence of a signal peptide).
Module 6: Cellular Localization Data Part 2
This module is a continuation of the previous module; it continues to guide students through a number of algorithms used to predict protein membrane topology and cellular localization. The tools and algorithms use in this modules are: Philius (an alternative algorithm for transmembrane topology), TargetP (predicts the subcellular location of eukaryotic proteins), NucPred (analyzes a eukaryotic protein sequence for nuclear localization signals), and the Yeast Protein Localization Database (YPL), a compilation of GFP-fusion data.
Module 7: Gene Deletion Phenotypes
This module guides students through tools that summarize the phenotypic data available for each ORF deletion. PROPHECY provides quantitative information about phenotypes for the complete collection of deletion strains. The fitness database provides observed phenotypes when a given ORF deletion strain is exposed to a panel of drugs and chemicals. The yeast phenotype and publication summaries on SGD(http://yeastgenome.org/) are also covered in this module.
Module 8: Genetic and Physical Interactors and Expression Data
This module explains the bioinformatics tools available for investigating physical and genetic interactions and co-expression data with a selected ORF. Specifically, the module directs students through the use of GeneMania, which looks for correlations or associations amongst large sets of functional data and SPELL (Serial Pattern of Expression Levels Locator), which looks for correlations in microarray expression data.
Wet Lab Modules: