Analysis of Postdoctoral Training Outcomes That Broaden Participation in Science Careers

Postdoctoral training is an optimal time to expand research skills, develop independence, and shape career trajectories, making this training period important to study in the context of career development. Seeding Postdoctoral Innovators in Research and Education (SPIRE) is a training program that balances research, teaching, and professional development. This study examines the factors that promote the transition of postdocs into academic careers and increase diversity in science, technology, engineering, and mathematics. Data indicate that SPIRE scholars (n = 77) transition into faculty positions at three times the national average with a greater proportion of underrepresented racial minorities (URMs) and females represented among SPIRE scholars. Logistic regression models indicate that significant predictors are the intended career track at the start of the postdoctoral training and the number of publications. Factors necessary for successful transition are teaching experience as independent instructors, professional development opportunities, and the experience of balancing teaching with research. Scholars’ continued commitment to increasing diversity in their faculty roles was demonstrated by their attainment of tenure-track positions at minority-serving institutions, continued mentorship of URMs, and engagement with diversity initiatives. These results suggest that a postdoctoral program structured to include research, teaching, and diversity inclusion facilitates attainment of desired academic positions with sustained impacts on broadening participation

Towards a genome-wide transcriptogram: the Saccharomyces cerevisiae case

A genome modular classification that associates cellular processes to modules could lead to a method to quantify the differences in gene expression levels in different cellular stages or conditions: the transcriptogram, a powerful tool for assessing cell performance, would be at hand. Here we present a computational method to order genes on a line that clusters strongly interacting genes, defining functional modules associated with gene ontology terms. The starting point is a list of genes and a matrix specifying their interactions, available at large gene interaction databases. Considering the Saccharomyces cerevisiae genome we produced a succession of plots of gene transcription levels for a fermentation process. These plots discriminate the fermentation stage the cell is going through and may be regarded as the first versions of a transcriptogram. This method is useful for extracting information from cell stimuli/responses experiments, and may be applied with diagnostic purposes to different organisms

A Database and Evaluation for Classification of RNA Molecules Using Graph Methods

In this paper, we introduce a new graph dataset based on the representation of RNA. The RNA dataset includes 3178 RNA chains which are labelled in 8 classes according to their reported biological functions. The goal of this database is to provide a platform for investigating the classication of RNA using graph-based methods. The molecules are represented by graphs representing the sequence and base-pairs of the RNA, with a number of labelling schemes using base labels and local shape. We report the results of a number of state-of-the-art graph based methods on this dataset as a baseline comparison and investigate how these methods can be used to categorise RNA molecules on their type and functions. The methods applied are Weisfeiler Lehman and optimal assignment kernels, shortest paths kernel and the all paths and cycle methods. We also compare to the standard Needleman-Wunsch algorithm used in bioinformatics for DNA and RNA comparison, and demonstrate the superiority of graph kernels even on a string representation. The highest classication rate is obtained by the WL-OA algorithm using base labels and base-pair connections