CURE on yeast genes of unknown function increases students' bioinformatics proficiency and research confidence

Course-based undergraduate research experiences (CUREs) can reduce barriers to research opportunities while increasing student knowledge and confidence. However, the number of widely adopted, easily transferable CUREs is relatively small. Here, we describe a CURE aimed at determining the function of poorly characterized Saccharomyces cerevisiae genes. More than 20 years after sequencing of the yeast genome, nearly 10% of open reading frames (ORFs) still have at least one uncharacterized Gene Ontology (GO) term. We refer to these genes as "ORFans" and formed a consortium aimed at assigning functions to them. Specifically, over 70 faculty members attended summer workshops to learn the bioinformatics workflow and basic laboratory techniques described herein. Ultimately, this CURE was adapted for implementation at 34 institutions, resulting in over 1,300 students conducting course-based research on ORFans. Pre-/post-tests confirmed that students gained both (i) an understanding of gene ontology and (ii) knowledge regarding the use of bioinformatics to assign gene function. After using these data to craft their own hypotheses, then testing their predictions by constructing and phenotyping deletion strains, students self-reported significant gains in several areas, including computer modeling and exposure to a project where no one knows the outcome. Interestingly, most net gains self-reported by ORFan Gene Project participants were greater than published findings for CUREs assessed with the same survey instrument. The surprisingly strong impact of this CURE may be due to the incoming lack of experience of ORFan Project participants and/or the independent thought required to develop testable hypotheses from complex data sets.

Don't let spurious accusations of pseudoreplication limit our ability to learn from natural experiments (and other messy kinds of ecological monitoring)

Pseudoreplication is defined as the use of inferential statistics to test for treatment effects where treatments are not replicated and/or replicates are not statistically independent. It is a genuine but controversial issue in ecology particularly in the case of costly landscape-scale manipulations, behavioral studies where ethics or other concerns may limit sample sizes, ad hoc monitoring data, and the analysis of natural experiments where chance events occur at a single site. Here key publications on the topic are reviewed to illustrate the debate that exists about the conceptual validity of pseudoreplication. A survey of ecologists and case studies of experimental design and publication issues are used to explore the extent of the problem, ecologists' solutions, reviewers' attitudes, and the fate of submitted manuscripts. Scientists working across a range of ecological disciplines regularly come across the problem of pseudoreplication and build solutions into their designs and analyses. These include carefully defining hypotheses and the population of interest, acknowledging the limits of statistical inference and using statistical approaches including nesting and random effects. Many ecologists face considerable challenges getting their work published if accusations of pseudoreplication are made - even if the problem has been dealt with. Many reviewers reject papers for pseudoreplication, and this occurs more often if they haven't experienced the issue themselves. The concept of pseudoreplication is being applied too dogmatically and often leads to rejection during review. There is insufficient consideration of the associated philosophical issues and potential statistical solutions. By stopping the publication of ecological studies, reviewers are slowing the pace of ecological research and limiting the scope of management case studies, natural events studies, and valuable data available to form evidence-based solutions. Recommendations for fair and consistent treatment of pseudoreplication during writing and review are given for authors, reviewers, and editors.