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 ( will demo a Shiny application intended to make spectroscopy analyses more accessible and highlight the usability features.

Recent Posts

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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.


My first scientific publication was published this month in PNAS: 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.


See here for more about tidy tuesday: 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(, 0) principleComponents <- prcomp(a) %>% .$x %>% as_tibble %>% bind_cols(name=a2$country, .


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.


library(tidyverse) library(magrittr) Read data into R rawData <- readxl::read_excel("../../static/data/tidy_tuesday_week2.xlsx") rawData ## # A tibble: 800 x 11 ## year Cornerback `Defensive Lineman` Linebacker `Offensive Lineman` ## <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 2011 11265916 17818000 16420000 15960000 ## 2 2011 11000000 16200000 15623000 12800000 ## 3 2011 10000000 12476000 11825000 11767500 ## 4 2011 10000000 11904706 10083333 10358200 ## 5 2011 10000000 11762782 10020000 10000000 ## 6 2011 9244117 11340000 8150000 9859166 ## 7 2011 8000000 10000000 7812500 9500000 ## 8 2011 7900000 9482500 7700000 9420000 ## 9 2011 7400000 8450000 7200000 8880000 ## 10 2011 7000000 8383266 7100000 8686750 ## # .



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