References of "Helmus, Rick"
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See detailpatRoon 2.0: Improved non-target analysis workflows including automated transformation product screening
Helmus, Rick; van de Velde, Bas; Brunner, Andrea M. et al

in Journal of Open Source Software (2022), 7(71), 4029

Non-target analysis (NTA) via chromatography coupled to high resolution mass spectrometry (HRMS) is used to monitor and identify organic chemicals in the environment. Biotic and abiotic processes can ... [more ▼]

Non-target analysis (NTA) via chromatography coupled to high resolution mass spectrometry (HRMS) is used to monitor and identify organic chemicals in the environment. Biotic and abiotic processes can transform original chemicals (parents) into transformation products (TPs). These TPs can be of equal or more concern than their parent compounds and are therefore critical to monitor and identify in the environment (Escher & Fenner, 2011; Farré et al., 2008), often with NTA. Given the amount of data generated by NTA, advanced automated data processing workflows are essential. The open-source, R-based (R Core Team, 2021) platform patRoon (Helmus, ter Laak, et al., 2021) offers automated, straightforward, flexible and comprehensive NTA workflows. This article describes improvements introduced in patRoon 2.0, including extensive TP screening and simultaneous processing of positive and negative HRMS data. The updated documentation and code are available via https://rickhelmus.github.io/patRoon and archived in Helmus, Velde, et al. (2021). [less ▲]

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See detailpatRoon: open source software platform for environmental mass spectrometry based non-target screening
Helmus, Rick; ter Laak, Thomas L.; van Wezel, Annemarie P. et al

in Journal of Cheminformatics (2021), 13(1), 1

Abstract Mass spectrometry based non-target analysis is increasingly adopted in environmental sciences to screen and identify numerous chemicals simultaneously in highly complex samples. However, current ... [more ▼]

Abstract Mass spectrometry based non-target analysis is increasingly adopted in environmental sciences to screen and identify numerous chemicals simultaneously in highly complex samples. However, current data processing software either lack functionality for environmental sciences, solve only part of the workflow, are not openly available and/or are restricted in input data formats. In this paper we present patRoon , a new R based open-source software platform, which provides comprehensive, fully tailored and straightforward non-target analysis workflows. This platform makes the use, evaluation and mixing of well-tested algorithms seamless by harmonizing various common (primarily open) software tools under a consistent interface. In addition patRoon offers various functionality and strategies to simplify and perform automated processing of complex (environmental) data effectively. patRoon implements several effective optimization strategies to significantly reduce computational times. The ability of patRoon to perform time-efficient and automated non-target data annotation of environmental samples is demonstrated with a simple and reproducible workflow using open-access data of spiked samples from a drinking water treatment plant study. In addition, the ability to easily use, combine and evaluate different algorithms was demonstrated for three commonly used feature finding algorithms. This article, combined with already published works, demonstrate that patRoon helps make comprehensive (environmental) non-target analysis readily accessible to a wider community of researchers. [less ▲]

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See detailVirtual Podium Keynote: Compound Identification and Exposomics: DIY Databases?
Schymanski, Emma UL; Bolton, Evan; Helmus, Rick et al

Presentation (2020, April 10)

In light of recent events, many of us have been impacted by the cancellation of conferences and meetings. We are not only losing the opportunity to present our research, but a chance to connect with our ... [more ▼]

In light of recent events, many of us have been impacted by the cancellation of conferences and meetings. We are not only losing the opportunity to present our research, but a chance to connect with our community. Virtual Podium is a platform and opportunity to present and learn about compelling scientific research. Our third session will be focused on Compound Identification. Our keynote speaker this week will be Emma Schymanski who is the PI of Environmental Cheminformatics at the University of Luxembourg. Session 3: Compound Identification Friday, April 10, 2020 at 12:00-1:00PM PDT (3:00-4:00PM EDT) Session 3 - Compound Identification: https://www.eventbrite.com/e/101426613732 [less ▲]

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See detailpatRoon: Open Source Software Platform for Environmental Mass Spectrometry Based Non-target Screening
Helmus, Rick; ter Laak, Thomas; van Wezel, Annemarie et al

E-print/Working paper (2020)

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See detailEvaluation of reverse osmosis drinking water treatment of riverbank filtrate using bioanalytical tools and non-target screening
Albergamo, Vittorio; Escher, Beate I.; Schymanski, Emma UL et al

in Environmental Science: Water Research and Technology (2020), 6(1), 103--116

Stand-alone reverse osmosis (RO) has been proposed to produce high-quality drinking water from raw riverbank filtrate impacted by anthropogenic activities. To evaluate RO efficacy in removing organic ... [more ▼]

Stand-alone reverse osmosis (RO) has been proposed to produce high-quality drinking water from raw riverbank filtrate impacted by anthropogenic activities. To evaluate RO efficacy in removing organic micropollutants, biological analyses were combined with non-target screening using high-resolution mass spectrometry and open cheminformatics tools. The bank filtrate induced xenobiotic metabolism mediated by the aryl hydrocarbon receptor AhR, adaptive stress response mediated by the transcription factor Nrf2 and genotoxicity in the Ames-fluctuation test. These effects were absent in the RO permeate (product water), indicating the removal of bioactive micropollutants by RO membranes. In the water samples, 49 potentially toxic compounds were tentatively identified with the in silico fragmentation tool MetFrag using the US Environmental Protection Agency CompTox Chemicals Dashboard database. 5 compounds were confirmed with reference standards and 16 were tentatively identified with high confidence based on similarities to accurate mass spectra in open libraries. The bioactivity data of the confirmed chemicals indicated that 2,6-dichlorobenzamide and bentazone in water samples can contribute to the activation of AhR and oxidative stress response, respectively. The bioactivity data of 7 compounds tentatively identified with high confidence indicated that these structures can contribute to the induction of such effects. This study showed that riverbank filtration followed by RO could produce drinking water free of the investigated toxic effects. [less ▲]

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See detailNon-target screening reveals time trends of polar micropollutants in a riverbank filtration system
Albergamo, Vittorio; Schollée, Jennifer E.; Schymanski, Emma UL et al

in Environmental Science and Technology (2019), 53(13), 7584-7594

The historic emissions of polar micropollutants in a natural drinking water source were investigated by nontarget screening with high-resolution mass spectrometry and open cheminformatics tools. The study ... [more ▼]

The historic emissions of polar micropollutants in a natural drinking water source were investigated by nontarget screening with high-resolution mass spectrometry and open cheminformatics tools. The study area consisted of a riverbank filtration transect fed by the river Lek, a branch of the lower Rhine, and exhibiting up to 60-year travel time. More than 18,000 profiles were detected. Hierarchical clustering revealed that 43% of the 15 most populated clusters were characterized by intensity trends with maxima in the 1990s, reflecting intensified human activities, wastewater treatment plant upgrades and regulation in the Rhine riparian countries. Tentative structure annotation was performed using automated in silico fragmentation. Candidate structures retrieved from ChemSpider were scored based on the fit of the in silico fragments to the experimental tandem mass spectra, similarity to openly accessible accurate mass spectra, associated metadata, and presence in a suspect list. Sixty-seven unique structures (72 over both ionization modes) were tentatively identified, 25 of which were confirmed and included contaminants so far unknown to occur in bank filtrate or in natural waters at all, such as tetramethylsulfamide. This study demonstrates that many classes of hydrophilic organics enter riverbank filtration systems, persisting and migrating for decades if biogeochemical conditions are stable. [less ▲]

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See detailThe metaRbolomics Toolbox in Bioconductor and beyond
Stanstrup, Jan; Broeckling, Corey D.; Helmus, Rick et al

in Metabolites (2019), 9(10), 200

Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and ... [more ▼]

Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub. [less ▲]

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