title: Teaching neuroinformatics with an emphasis on quantitative locus analysis.
abstract: Although powerful bioinformatics tools are available for free on the web and are used by neuroscience professionals on a daily basis, neuroscience students are largely ignorant of them. This Neuroinformatics module weaves together several bioinformatics tools to make a comprehensive unit. This unit encompasses quantifying a phenotype through a Quantitative Trait Locus (QTL) analysis, which links phenotype to loci on chromosomes that likely had an impact on the phenotype. Students then are able to sift through a list of genes in the region(s) of the chromosome identified by the QTL analysis and find a candidate gene that has relatively high expression in the brain region of interest. Once such a candidate gene is identified, students can find out more information about the gene, including the cells/layers in which it is expressed, the sequence of the gene, and an article about the gene. All of the resources employed are available at no cost via the internet. Didactic elements of this instructional module include genetics, neuroanatomy, Quantitative Trait Locus analysis, molecular techniques in neuroscience, and statistics-including multiple regression, ANOVA, and a bootstrap technique. This module was presented at the Faculty for Undergraduate Neuroscience (FUN) 2011 Workshop at Pomona College and can be accessed at http://mdcune.psych.ucla.edu/modules/bioinformatics.
authors.affiliation: Department of Psychology, UCLA, Los Angeles, CA 90095-1563;
authors.forename: William
authors.initials: W
authors.lastname: Grisham
authors.forename: Christopher A
authors.initials: CA
authors.lastname: Korey
authors.forename: Natalie A
authors.initials: NA
authors.lastname: Schottler
authors.forename: Lisa Beck
authors.initials: LB
authors.lastname: McCauley
authors.forename: Jackson
authors.initials: J
authors.lastname: Beatty
keywords: analysis of variance (ANOVA); genetics; in situ hybridization; microarrays; multiple regression; neuroinformatics; neuroscience; online learning; phenotypic expression; quantitative trait locus (QTL)