Here, we introduce the mbX package: an R-based tool designed to streamline 16S rRNA gene microbiome data analysis following taxonomic classification. It automates key post-sequencing steps, including taxonomic data cleaning and visualization, addressing the need for reproducible and user-friendly microbiome workflows. mbX’s core functions, ezclean and ezviz, take raw taxonomic output (such as those from QIIME 2) and sample metadata to produce a cleaned relative abundance dataset and high-quality stacked bar plots with minimal manual intervention. We validated mbX on 14 real microbiome datasets, demonstrating significant improvements in efficiency and consistency of post-processing of DNA sequence data. The results show that mbX ensures uniform taxonomic formatting, eliminates common manual errors, and quickly generates publication-ready figures, greatly facilitating downstream analysis. For a dataset with 20 samples, both functions of mbX ran in less than 1 s and used less than 1 GB of memory. For a dataset with more than 1170 samples, the functions ran within 125 s and used less than 4.5 GB of memory. By integrating seamlessly with existing pipelines and emphasizing automation, mbX fills a critical gap between sequence classification and statistical analysis. An upcoming version will have an added function which will further extend mbX to automated statistical comparisons, aiming for an end-to-end microbiome analysis solution by integrating mbX with currently available pipelines. This article presents the design of mbX, its workflow and features, and a comparative discussion positioning mbX relative to other microbiome bioinformatics tools. The contributions of mbX highlight its significance in accelerating microbiome research through reproducible and streamlined data analysis.

Cattle feed efficiency can influence operating costs and revenues, and it is a complex phenotype that can be measured by different metrices. The objective of this study was to determine which microbial taxa in the ruminal and fecal microbiomes of Angus bulls are associated with residual feed intake (RFI), residual average daily gain (RADG), feed conversion ratio (FCR), and adjusted feed conversion ratio (AFCR) by using a large sample size. The metrics RFI and RADG are calculated from the difference between an animal’s actual feed intake and actual gain, to their predicted intake and gain, respectively, and AFCR is the FCR adjusted for the animal’s size. Rumen (n = 1176) and fecal (n = 1179) samples were collected from Angus bulls from 10 contemporary groups located across the continental U.S. Animals were assigned to three classifications (High, Medium, or Low) for each feed efficiency metric within each contemporary group, according to their individual performance data. To compare the feed efficiency metrics, a cost analysis scenario was performed, and bulls classified by RADG had the greatest correlation with predicted profit (r² = 0.98). For alpha diversity, only classification by FCR resulted in differences within both rumen and fecal samples. In the rumen environment, only ‘X112’ was significant (p = 0.02) by RFI classification, and Succinivibrionaceae was significant (p = 0.01) by FCR classification. In the fecal environment, a total of 19 families significantly differed, with bulls classified by RFI having the greatest number of different families. Bull feed efficiency status had greater impacts in the fecal microbiome compared to the rumen, but results should be taken in context since the animals were classified within each contemporary group. Still, differences detected within the fecal microbiome may allow for more assessable microbiome applications; however, the metric used to classify cattle as feed efficient will determine which microbial families are relevant.

High-throughput 16S rRNA gene sequencing has become a cornerstone of microbiome research, but executing complex analysis pipelines like QIIME 2 can be challenging for researchers unfamiliar with command-line interfaces. Here we present a Shiny-based web application, the QIIME 2 Pipeline Code Generator, which simplifies the construction of QIIME 2 commands for taxonomic analysis and visualization. Focusing on the initial stages of a typical QIIME 2 workflow – from post-demultiplexing sequence processing to taxa bar plot generation – our tool provides an interactive graphical interface for specifying input files and parameters. In real time, it produces a ready-to-run shell script of QIIME 2 commands that performs sequence denoising with DADA2, generates feature tables and representative sequences, assigns taxonomy with a pre-trained classifier, and creates interactive taxonomy bar plot visualizations. By eliminating manual command scripting, the application reduces syntax errors and lowers the barrier of entry for biologists, microbiologists, and other scientists who may lack advanced programming skills. The tool has been adopted by approximately 20–40 researchers at our institution, highlighting its utility in improving the accessibility and reproducibility of microbiome data analysis. We describe the motivation, design, and usage of the code generator, and discuss its role in bridging the gap between powerful bioinformatics platforms and the broader scientific community.

Fescue toxicosis results from ingestion of tall fescue infected with the ergot alkaloid (EA)-producing endophyte Epichloë coenophiala. The gastrointestinal microbiota is the first point of interaction with EAs and may be modulated by them. To investigate this, rumen and fecal samples were collected from steers grazing toxic (E+) or non-toxic fescue for 14 days, followed by a pasture switch. Microbial communities were characterized using 16S rRNA amplicon sequencing (bacterial V4, archaeal V6-V8 regions). Short-term E+ grazing did not affect alpha diversity in either matrix, but beta diversity differences were significant, though of small magnitude (rumen: R² < 0.07; feces: R² < 0.06), consistent with the overlap of communities between treatments. Relative abundance (RA) of dominant taxa remained unchanged, but low-abundance taxa were enriched in E+. In the rumen, these included proteolytic, amino acid decarboxylating, and methyl-compound forming microbes (e.g., Clostridia, Prevotellaceae, Lachnospiraceae (Butyrivibrio, Eubacterium), Streptococcaceae, and Gamma/Alphaproteobacteria), while fibrolytic taxa (e.g., Ruminococcus flavefaciens) declined. Hydrogenotrophic methanogens (Methanobrevibacter_A) decreased, while methylotrophic methanogens and hydrogen-utilizing bacteria (e.g., Selenomonadaceae) increased, indicating altered hydrogen flow in the rumen. Similar subtle but significant shifts were observed in fecal communities, including members of Lachnospiraceae (Eubacterium) and Rikenellaceae (Mucinivorans). Comparable RA of core (dominant) rumen and fecal microbiomes in steers previously exposed and never exposed to E+ fescue suggests no lasting effects on the dominant community. Short-term exposure to E+ tall fescue was associated with enrichment of low-abundance taxa whose previously described metabolic functions may contribute to maintaining rumen homeostasis under E+-induced dietary stress.