ranacapa: An R package and Shiny web app to explore environmental DNA data with exploratory statistics and interactive visualizations.

Environmental DNA (eDNA) metabarcoding is becoming a core tool in ecology and conservation biology, and is being used in a growing number of education, biodiversity monitoring, and public outreach programs in which professional research scientists engage community partners in primary research. Results from eDNA analyses can engage and educate natural resource managers, students, community scientists, and naturalists, but without significant training in bioinformatics, it can be difficult for this diverse audience to interact with eDNA results. Here we present the R package ranacapa, at the core of which is a Shiny web app that helps perform exploratory biodiversity analyses and visualizations of eDNA results. The app requires a taxonomy-by-sample matrix and a simple metadata file with descriptive information about each sample. The app enables users to explore the data with interactive figures and presents results from simple community ecology analyses. We demonstrate the value of ranacapa to two groups of community partners engaging with eDNA metabarcoding results

ranacapa: An R package and Shiny web app to explore environmental DNA data with exploratory statistics and interactive visualizations [version 1; referees: 1 approved, 2 approved with reservations]

Environmental DNA (eDNA) metabarcoding is becoming a core tool in ecology and conservation biology, and is being used in a growing number of education, biodiversity monitoring, and public outreach programs in which professional research scientists engage community partners in primary research. Results from eDNA analyses can engage and educate natural resource managers, students, community scientists, and naturalists, but without significant training in bioinformatics, it can be difficult for this diverse audience to interact with eDNA results. Here we present the R package ranacapa, at the core of which is a Shiny web app that helps perform exploratory biodiversity analyses and visualizations of eDNA results. The app requires a taxonomy-by-sample matrix and a simple metadata file with descriptive information about each sample. The app enables users to explore the data with interactive figures and presents results from simple community ecology analyses. We demonstrate the value of ranacapa to two groups of community partners engaging with eDNA metabarcoding results

Genomic diversity of bacteriophages infecting Microbacterium spp

The bacteriophage population is vast, dynamic, old, and genetically diverse. The genomics of phages that infect bacterial hosts in the phylum Actinobacteria show them to not only be diverse but also pervasively mosaic, and replete with genes of unknown function. To further explore this broad group of bacteriophages, we describe here the isolation and genomic characterization of 116 phages that infect Microbacterium spp. Most of the phages are lytic, and can be grouped into twelve clusters according to their overall relatedness; seven of the phages are singletons with no close relatives. Genome sizes vary from 17.3 kbp to 97.7 kbp, and their G+C% content ranges from 51.4% to 71.4%, compared to ~67% for their Microbacterium hosts. The phages were isolated on five different Microbacterium species, but typically do not efficiently infect strains beyond the one on which they were isolated. These Microbacterium phages contain many novel features, including very large viral genes (13.5 kbp) and unusual fusions of structural proteins, including a fusion of VIP2 toxin and a MuF-like protein into a single gene. These phages and their genetic components such as integration systems, recombineering tools, and phage-mediated delivery systems, will be useful resources for advancing Microbacterium genetics

Adenovirus: A Versatile Tool for Studying and Treating Diseases

Adenoviruses have taught us much about transcriptional regulation and cell cycle control because the cells they commonly infect are end-differentiated non-cycling cells. In order for these cells to be adequate hosts for viral replication, the virus must force the cells into the cell cycle. Adenoviruses express the small e1a protein immediately upon infection, which is responsible for initiating cell replication. Small e1a interacts with both the transcriptional co- activators p300/CBP and the transcriptional repressor RB-family proteins in order to induce epigenetic reprogramming that results in activation of cell cycle genes and inactivation of genes detrimental to adenoviral replication.This work investigated how the specific interactions between e1a and p300/CBP or e1a and RB-family proteins affect the distribution of the H3K18ac marker throughout the genome and the subsequent changes in expression.Cell cycle arrested cells were infected with adenovirus e1a mutants that either cannot interact with p300/CBP, or cannot interact with RB-family proteins. The genome wide distribution of the histone marker H3K18ac following infection with wild-type e1a or the e1a mutants, compared to mock infected cells, was determined by chromatin immunoprecipitation with anti-H3K18ac antibody followed by massive parallel sequencing (ChIP-seq). Correlations between H3K18ac and expression levels were established through whole transcriptome sequencing (RNA-seq).We have found that a simple model can only begin to describe the complex interactions between e1a and it's cellular partners. The e1a interaction with p300/CBP appears to be more important for global hypoacetylation than the interaction with RB-family proteins, but surprisingly, the e1a and RB interaction is required for hyperacetylation and activation of cell cycle promoters.The second half of this work focuses on the construction and validation of an adenovirus "helper virus" for targeting the Helper Dependent Adenovirus/Epstein Barr virus (HDAd/EBV) hybrid system targeted to Hematopoietic stem cells by a chimeric Ad5/35 fiber. The challenges associated with developing an adenovirus replication permissive cell line (HEK293) with adequate levels of FLPe expression to limit helper virus contamination in the HDAd/EBV vector stock are also discussed

Synthesizing Research Narratives to Reveal the Big Picture: a CREATE(S) Intervention Modified for Journal Club Improves Undergraduate Science Literacy.

Communicating science effectively is an essential part of the development of science literacy. Research has shown that introducing primary scientific literature through journal clubs can improve student learning outcomes, including increased scientific knowledge. However, without scaffolding, students can miss more complex aspects of science literacy, including how to analyze and present scientific data. In this study, we apply a modified CREATE(S) process (Concept map the introduction, Read methods and results, Elucidate hypotheses, Analyze data, Think of the next Experiment, and Synthesis map) to improve students science literacy skills, specifically their understanding of the process of science and their ability to use narrative synthesis to communicate science. We tested this hypothesis using a retrospective quasi-experimental study design in upper-division undergraduate courses. We compared learning outcomes for CREATES intervention students to those for students who took the same courses before CREATES was introduced. Rubric-guided, direct evidence assessments were used to measure student gains in learning outcomes. Analyses revealed that CREATES intervention students versus the comparison group demonstrated improved ability to interpret and communicate primary literature, especially in the methods, hypotheses, and narrative synthesis learning outcome categories. Through a mixed-methods analysis of a reflection assignment completed by the CREATES intervention group, students reported the synthesis map as the most frequently used step in the process and highly valuable to their learning. Taken together, the study demonstrates how this modified CREATES process can foster scientific literacy development and how it could be applied in science, technology, engineering, and math journal clubs

Synthesizing Research Narratives to Reveal the Big Picture: a CREATE(S) Intervention Modified for Journal Club Improves Undergraduate Science Literacy

ABSTRACT Communicating science effectively is an essential part of the development of science literacy. Research has shown that introducing primary scientific literature through journal clubs can improve student learning outcomes, including increased scientific knowledge. However, without scaffolding, students can miss more complex aspects of science literacy, including how to analyze and present scientific data. In this study, we apply a modified CREATE(S) process (Concept map the introduction, Read methods and results, Elucidate hypotheses, Analyze data, Think of the next Experiment, and Synthesis map) to improve students’ science literacy skills, specifically their understanding of the process of science and their ability to use narrative synthesis to communicate science. We tested this hypothesis using a retrospective quasi-experimental study design in upper-division undergraduate courses. We compared learning outcomes for CREATES intervention students to those for students who took the same courses before CREATES was introduced. Rubric-guided, direct evidence assessments were used to measure student gains in learning outcomes. Analyses revealed that CREATES intervention students versus the comparison group demonstrated improved ability to interpret and communicate primary literature, especially in the methods, hypotheses, and narrative synthesis learning outcome categories. Through a mixed-methods analysis of a reflection assignment completed by the CREATES intervention group, students reported the synthesis map as the most frequently used step in the process and highly valuable to their learning. Taken together, the study demonstrates how this modified CREATES process can foster scientific literacy development and how it could be applied in science, technology, engineering, and math journal clubs

PUMAA: A Platform for Accessible Microbiome Analysis in the Undergraduate Classroom

Improvements in high-throughput sequencing makes targeted amplicon analysis an ideal method for the study of human and environmental microbiomes by undergraduates. Multiple bioinformatics programs are available to process and interpret raw microbial diversity datasets, and the choice of programs to use in curricula is largely determined by student learning goals. Many of the most commonly used microbiome bioinformatics platforms offer end-to-end data processing and data analysis using a command line interface (CLI), but the downside for novice microbiome researchers is the steep learning curve often required. Alternatively, some sequencing providers include processing of raw data and taxonomy assignments as part of their pipelines. This, when coupled with available web-based or graphical user interface (GUI) analysis and visualization tools, eliminates the need for students or instructors to have extensive CLI experience. However, lack of universal data formats can make integration of these tools challenging. For example, tools for upstream and downstream analyses frequently use multiple different data formats which then require writing custom scripts or hours of manual work to make the files compatible. Here, we describe a microbial ecology bioinformatics curriculum that focuses on data analysis, visualization, and statistical reasoning by taking advantage of existing web-based and GUI tools. We created the Program for Unifying Microbiome Analysis Applications (PUMAA), which solves the problem of inconsistent files by formatting the output files from several raw data processing programs to seamlessly transition to a suite of GUI programs for analysis and visualization of microbiome taxonomic and inferred functional profiles. Additionally, we created a series of tutorials to accompany each of the microbiome analysis curricular modules. From pre- and post-course surveys, students in this curriculum self-reported conceptual and confidence gains in bioinformatics and data analysis skills. Students also demonstrated gains in biologically relevant statistical reasoning based on rubric-guided evaluations of open-ended survey questions and the Statistical Reasoning in Biology Concept Inventory. The PUMAA program and associated analysis tutorials enable students and researchers with no computational experience to effectively analyze real microbiome datasets to investigate real-world research questions

BlueFeather, the singleton that wasn't: Shared gene content analysis supports expansion of Arthrobacter phage Cluster FE.

Bacteriophages (phages) exhibit high genetic diversity, and the mosaic nature of the shared genetic pool makes quantifying phage relatedness a shifting target. Early parameters for clustering of related Mycobacteria and Arthrobacter phage genomes relied on nucleotide identity thresholds but, more recently, clustering of Gordonia and Microbacterium phages has been performed according to shared gene content. Singleton phages lack the nucleotide identity and/or shared gene content required for clustering newly sequenced genomes with known phages. Whole genome metrics of novel Arthrobacter phage BlueFeather, originally designated a putative singleton, showed low nucleotide identity but high amino acid and gene content similarity with Arthrobacter phages originally assigned to Clusters FE and FI. Gene content similarity revealed that BlueFeather shared genes with these phages in excess of the parameter for clustering Gordonia and Microbacterium phages. Single gene analyses revealed evidence of horizontal gene transfer between BlueFeather and phages in unique clusters that infect a variety of bacterial hosts. Our findings highlight the advantage of using shared gene content to study seemingly genetically isolated phages and have resulted in the reclustering of BlueFeather, a putative singleton, as well as former Cluster FI phages, into a newly expanded Cluster FE

Model-Driven Engineering of Gene Expression from RNA Replicons

RNA replicons are an emerging platform for engineering synthetic biological systems. Replicons self-amplify, can provide persistent high-level expression of proteins even from a small initial dose, and, unlike DNA vectors, pose minimal risk of chromosomal integration. However, no quantitative model sufficient for engineering levels of protein expression from such replicon systems currently exists. Here, we aim to enable the engineering of multigene expression from more than one species of replicon by creating a computational model based on our experimental observations of the expression dynamics in single- and multireplicon systems. To this end, we studied fluorescent protein expression in baby hamster kidney (BHK-21) cells using a replicon derived from Sindbis virus (SINV). We characterized expression dynamics for this platform based on the dose–response of a single species of replicon over 50 h and on a titration of two cotransfected replicons expressing different fluorescent proteins. From this data, we derive a quantitative model of multireplicon expression and validate it by designing a variety of three-replicon systems, with profiles that match desired expression levels. We achieved a mean error of 1.7-fold on a 1000-fold range, thus demonstrating how our model can be applied to precisely control expression levels of each Sindbis replicon species in a system

ranacapa: An R package and Shiny web app to explore environmental DNA data with exploratory statistics and interactive visualizations.

Environmental DNA (eDNA) metabarcoding is becoming a core tool in ecology and conservation biology, and is being used in a growing number of education, biodiversity monitoring, and public outreach programs in which professional research scientists engage community partners in primary research. Results from eDNA analyses can engage and educate natural resource managers, students, community scientists, and naturalists, but without significant training in bioinformatics, it can be difficult for this diverse audience to interact with eDNA results. Here we present the R package ranacapa, at the core of which is a Shiny web app that helps perform exploratory biodiversity analyses and visualizations of eDNA results. The app requires a taxonomy-by-sample matrix and a simple metadata file with descriptive information about each sample. The app enables users to explore the data with interactive figures and presents results from simple community ecology analyses. We demonstrate the value of ranacapa to two groups of community partners engaging with eDNA metabarcoding results