Selected Publications

Herein we present a significant innovation to microbial identification methods that are currently used to analyze biological and chemical components of bacteria. We developed the first data acquisition and bioinformatics pipeline (IDBac) that couples in situ matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) fingerprinting of intact proteins and specialized metabolites. To demonstrate the effectiveness of this pipeline, we elucidated subtle intra-species variations in specialized metabolite production of closely related bacterial strains within Bacillus, Paenibacillus, and Micromonospora genera based on in situ antibiotic, siderophore, and motility factor production. Coupling analysis of the protein and specialized metabolite MS regions addresses an urgent community need to rapidly assess and differentiate bacteria by functional metabolism, as well as elucidate phylogenetic resolution superior to that obtained from sequencing highly conserved genetic markers alone. The IDBac pipeline is freely available and facilitates the profiling of 384 single colonies in under four hours.
In bioRxiv, 2017

Recent & Upcoming Talks

Chase Clark (https://chasemc.github.io/IDBac/) will demo a Shiny application intended to make spectroscopy analyses more accessible and highlight the usability features.

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I have run across this a few times and hope this might help someone. As Shiny apps get more complicated and you start to use Shiny modules you will likely run across use cases for using renderUI within modules. Sometimes it is desirable to be able to test these using packages like testthat. However, you will run into a problem because of a missing session$ns. library(testthat) library(shiny) Example adapted from: https://shiny.

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Part One: Scope This is going to be a fairly opinionated post about my not novel but recent revelations as to how I think Shiny apps should be formatted. By formatting I don’t mean UI formatting, code style, etc. I mean how one might organize, holistically, a Shiny app to help maximize readability, stability, and scalability. Again not novel, just me getting my thoughts “on paper’. Before now my server() sections kind of looked like this: I am aware that it is possible to “modularize” a shiny app and there is good info on that from RStudio here and Steph Locke here.

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My first scientific publication was published this month in PNAS: https://doi.org/10.1073/pnas.1801247115 and has been a long time coming. I began this project as an interim between my first and second rotations as a first-year PhD student, I am now in the second-half of my third year. The manuscript took some time to prepare and apart from the usual: waiting for instrument time, repairs (LCQ used for initial derepilcation of desferrioxamines), etc.

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See here for more about tidy tuesday: https://github.com/rfordatascience/tidytuesday library(tidyverse) library(plotly) library(ggrepel) Read in data, remove punctuation from column names inputData <- readxl::read_xlsx("data/4-19-18/global_mortality.xlsx") names(inputData) <- str_trim( str_remove_all(names(inputData), "[[:punct:]]") ) Mean of each category grouped by country isn’t great, but for a tidy-tuesday it’ll do… a2 <- inputData %>% group_by(country) %>% summarize_if(is.numeric,mean) a <- a2 %>% select(4:34) %>% replace(is.na(.), 0) principleComponents <- prcomp(a) %>% .$x %>% as_tibble %>% bind_cols(name=a2$country, .

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Load libraries library(Rtsne) library(createdatasets) library(tweenr) library(gganimate) library(ggplot2) set.seed(42) Create data for t-SNE # Get MNIST data for t-SNE df_mnist <- createMNIST() df_sampled <- df_mnist[sample(1:nrow(df_mnist), 1000), ] mat_mnist <- as.matrix(df_sampled[ , !(colnames(df_sampled) == "Class")]) whichNumber <- as.character(df_sampled[ , (colnames(df_sampled) == "Class")]) # Going to do parallel t-SNE later (and data has to be created for each instance on Windows R parallel, so lets save the data as rds saveRDS(mat_mnist, "data/mat_mnist.rds") saveRDS(df_sampled, "data/df_sampled.

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