Biology subjects

Broeckling, C. D.

Publications and source records attributed to Broeckling, C. D..

4 recordsLinked to original sources

bioRxiv2026

The Everything Bagel Feature Finder: Ultra-fast automated feature finding for untargeted metabolomics

Metabolomics studies are increasingly being applied with hundreds to thousands, even tens of thousands of samples that demand rapid, automated data processing while maintaining analytical sensitivity or quantitative accuracy. A major computational bottleneck is feature finding, which is the transformation of LC-MS and LC-MS/MS data into a set of analyte signals aligned and quantified across samples. Feature finding can be computationally intensive and often requires manual iterative parameter optimization. To accelerate this process, we present the Everything Bagel (EB) feature finder, an ultra-fast automated feature finding tool that integrates feature detection, retention-time alignment, and gap filling designed for run-time and memory efficiency. We benchmarked EB against two automated feature finding methods on eight benchmarking datasets. Specifically, we evaluated these three feature finding methods by measuring spike-in standard detection coverage, dilution series quantification accuracy, and yeast 12C/13C credentialed features. In this evaluation, the EB feature finder achieved performance comparable to, and often exceeding, existing methods while requiring up to 150-fold lower CPU hours and up to 113-fold lower wall time. We further demonstrated the bioanalytical validity of EB by reanalyzing published datasets used for biomarker discovery and reproduced biologically significant features that matched the published findings using manually tuned feature finding settings. Taken along with the speed improvements, we anticipate EB will enhance the ability to automatically analyze datasets with thousands to tens of thousands of samples for the community.

bioinformatics↗

bioRxiv2026

Comparison of Extraction Methods for the Quantification of Phytohormones from Tomato Fruits and Leaves by LC-MS/MS

Accurate, simultaneous, and efficient quantification of chemically diverse phytohormone species is a critical task towards understanding the complex system of phytohormone signaling pathways. Quantification of phytohormones with the commonly used technique liquid chromatography coupled to tandem mass spectrometry is susceptible to the influence of non-phytohormone components present in the sample, a phenomenon referred to as matrix effect. To reduce matrix effect, some phytohormone quantification methods include additional steps of cleanup of crude extracts. However, to what extent additional purification steps provide increased accuracy compared to simpler, less laborious methods is seldomly evaluated. We evaluated three previously described phytohormone extraction methods, two of which include solid-phase extraction and one that does not, in their ability to minimize matrix effect and generate accurate estimates of phytohormone species spanning six classifications, from fruit and leaf tissue of Solanum lycopersicum cv. Micro-Tom (tomato). Our results show that, while the methods that included solid phase extraction occasionally outperformed each other regarding matrix effect and/or recovery efficiency for broad range of phytohormones, they rarely outperformed the simpler single-phase extraction method. Short AbstractAccurate, simultaneous quantification of chemically diverse phytohormones by LC-MS/MS is frequently confounded by matrix effects, leading to the incorporation of additional purification steps. We systematically compared three published extraction protocols with or without solid-phase extraction in tomato tissues across six hormone classes. Solid-phase methods occasionally improved matrix suppression or recovery, but did not consistently outperform the single-phase approach, questioning the added value of extra cleanup steps, particularly when high-throughput is desired, as in the case of systems biology interrogations.

plant biology↗

bioRxiv2023

Introducing Molecular Hypernetworks forDiscovery in MultidimensionalMetabolomics Data

Orthogonal separations of data from high-resolution mass spectrometry can provide insight into sample composition and help address the challenge of complete annotation of molecules in untargeted metabolomics. "Molecular networks" (MNs), as used, for example, in the Global Natural Products Social Molecular Networking platform, are an increasingly popular computational strategy for exploring and visualizing molecular relationships and improving annotation. MNs use graph representations to show the re-lationships between measured multidimensional data features. MNs also show promise for using network science algorithms to automatically identify targets for annotation candidates and to dereplicate features associated to a single molecular identity. How-ever, more advanced methods may better represent the complexity present in samples. Our work aims to increase confidence in annotation propagation by extending molecular network methods to include "molecular hypernetworks" (MHNs), able to natively repre-sent multiway relationships among observations supporting both human and analytical processing. In this paper we first introduce MHNs illustrated with simple examples, and demonstrate how to build them from liquid chromatography-and ion mobility spectrometry-separated MS data. We then describe a method to construct MHNs di-rectly from existing MNs as their "clique reconstructions", demonstrating their utility by comparing examples of previously published graph-based MNs to their respective MHNs.

bioinformatics↗

bioRxiv2018

Resource diversity structures aquatic bacterial communities

Microbial diversity is strongly affected by the bottom-up effects of resource availability. However, because resource pools often exist as heterogeneous mixtures of distinct molecules, resource heterogeneity may also affect community diversity. To test this hypothesis, we surveyed bacterial communities in lakes that varied in resource concentration. In addition, we characterized resource heterogeneity in these lakes using an ecosystem metabolomics approach. Overall, resource concentration and resource heterogeneity affected bacterial resource-diversity relationships. We found strong relationships between bacterial alpha-diversity (richness and evenness) and resource concentration and richness, but richness and evenness responded in different ways. Likewise, we found associations between the composition of the bacterial community and both resource concentration and composition, but the relationship with resource composition was stronger. Last, in the surveyed communities the presence of resource generalists may have reduced the effect of resource heterogeneity on community composition. These results have implications for understanding the interactions between bacteria and organic matter and suggest that changes in organic matter composition may alter the structure and function of bacterial communities.

microbiology↗