Using DNA metabarcoding to characterize national scale diatom-environment relationships and to develop indicators in streams and rivers of the United States.

Recent advancements in DNA techniques, metabarcoding, and bioinformatics could help expand the use of benthic diatoms in monitoring and assessment programs by providing relatively quick and increasingly cost-effective ways to quantify diatom diversity in environmental samples. However, such applications of DNA-based approaches are relatively new, and in the United States, unknowns regarding their applications at large scales exist because only a few small-scale studies have been done. Here, we present results from the first nationwide survey to use DNA metabarcoding (rbcL) of benthic diatoms, which were collected from 1788 streams and rivers across nine ecoregions spanning the conterminous USA.

TreeKernel: interpretable kernel machine tests for interactions between -omics and clinical predictors with applications to metabolomics and COPD phenotypes.

Background: In this paper, we are interested in interactions between a high-dimensional -omics dataset and clinical covariates. The goal is to evaluate the relationship between a phenotype of interest and a high-dimensional omics pathway, where the effect of the omics data depends on subjects' clinical covariates (age, sex, smoking status, etc.).

PaIRKAT: A pathway integrated regression-based kernel association test with applications to metabolomics and COPD phenotypes.

High-throughput data such as metabolomics, genomics, transcriptomics, and proteomics have become familiar data types within the "-omics" family. For this work, we focus on subsets that interact with one another and represent these "pathways" as graphs. Observed pathways often have disjoint components, i.

tidyMicro: a pipeline for microbiome data analysis and visualization using the tidyverse in R.

Background: The drive to understand how microbial communities interact with their environments has inspired innovations across many fields. The data generated from sequence-based analyses of microbial communities typically are of high dimensionality and can involve multiple data tables consisting of taxonomic or functional gene/pathway counts. Merging multiple high dimensional tables with study-related metadata can be challenging.