FAIR chemical structures in the Journal of CheminformaticsSchymanski, Emma ; in Journal of Cheminformatics (2021), 13(1), 50 Abstract The ability to access chemical information openly is an essential part of many scientific disciplines. The Journal of Cheminformatics is leading the way for rigorous, open cheminformatics in many ... [more ▼] Abstract The ability to access chemical information openly is an essential part of many scientific disciplines. The Journal of Cheminformatics is leading the way for rigorous, open cheminformatics in many ways, but there remains room for improvement in primary areas. This letter discusses how both authors and the journal alike can help increase the FAIR ness (Findability, Accessibility, Interoperability, Reusability) of the chemical structural information in the journal. A proposed chemical structure template can serve as an interoperable Additional File format (already accessible ), made more findable by linking the DOI of this data file to the article DOI metadata, supporting further reuse . [less ▲] Detailed reference viewed: 49 (0 UL) Empowering large chemical knowledge bases for exposomics: PubChemLite meets MetFragSchymanski, Emma ; Kondic, Todor ; et alin Journal of Cheminformatics (2021), 13(1), 19 Abstract Compound (or chemical) databases are an invaluable resource for many scientific disciplines. Exposomics researchers need to find and identify relevant chemicals that cover the entirety of ... [more ▼] Abstract Compound (or chemical) databases are an invaluable resource for many scientific disciplines. Exposomics researchers need to find and identify relevant chemicals that cover the entirety of potential (chemical and other) exposures over entire lifetimes. This daunting task, with over 100 million chemicals in the largest chemical databases, coupled with broadly acknowledged knowledge gaps in these resources, leaves researchers faced with too much—yet not enough—information at the same time to perform comprehensive exposomics research. Furthermore, the improvements in analytical technologies and computational mass spectrometry workflows coupled with the rapid growth in databases and increasing demand for high throughput “big data” services from the research community present significant challenges for both data hosts and workflow developers. This article explores how to reduce candidate search spaces in non-target small molecule identification workflows, while increasing content usability in the context of environmental and exposomics analyses, so as to profit from the increasing size and information content of large compound databases, while increasing efficiency at the same time. In this article, these methods are explored using PubChem, the NORMAN Network Suspect List Exchange and the in silico fragmentation approach MetFrag. A subset of the PubChem database relevant for exposomics, PubChemLite, is presented as a database resource that can be (and has been) integrated into current workflows for high resolution mass spectrometry. Benchmarking datasets from earlier publications are used to show how experimental knowledge and existing datasets can be used to detect and fill gaps in compound databases to progressively improve large resources such as PubChem, and topic-specific subsets such as PubChemLite. PubChemLite is a living collection, updating as annotation content in PubChem is updated, and exported to allow direct integration into existing workflows such as MetFrag. The source code and files necessary to recreate or adjust this are jointly hosted between the research parties (see data availability statement). This effort shows that enhancing the FAIRness (Findability, Accessibility, Interoperability and Reusability) of open resources can mutually enhance several resources for whole community benefit. The authors explicitly welcome additional community input on ideas for future developments. [less ▲] Detailed reference viewed: 44 (1 UL) An annotation database for chemicals of emerging concern in exposome research; ; et al in Environment International (2021), 152 Detailed reference viewed: 65 (1 UL) patRoon: open source software platform for environmental mass spectrometry based non-target screening; ; 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 ▲] Detailed reference viewed: 64 (0 UL) LIPAD (LRRK2/Luebeck International Parkinson's Disease) Study Protocol: Deep Phenotyping of an International Genetic Cohort; ; et al in Frontiers in Neurology (2021), 12 Background: Pathogenic variants in the Leucine-rich repeat kinase 2 ( LRRK2) gene are the most common known monogenic cause of Parkinson's disease (PD). LRRK2 -linked PD is clinically indistinguishable ... [more ▼] Background: Pathogenic variants in the Leucine-rich repeat kinase 2 ( LRRK2) gene are the most common known monogenic cause of Parkinson's disease (PD). LRRK2 -linked PD is clinically indistinguishable from idiopathic PD and inherited in an autosomal dominant fashion with reduced penetrance and variable expressivity that differ across ethnicities and geographic regions. Objective: To systematically assess clinical signs and symptoms including non-motor features, comorbidities, medication and environmental factors in PD patients, unaffected LRRK2 pathogenic variant carriers, and controls. A further focus is to enable the investigation of modifiers of penetrance and expressivity of LRRK2 pathogenic variants using genetic and environmental data. Methods: Eligible participants are invited for a personal or online examination which comprises completion of a detailed eCRF and collection of blood samples (to obtain DNA, RNA, serum/plasma, immune cells), urine as well as household dust. We plan to enroll 1,000 participants internationally: 300 with LRRK2 -linked PD, 200 with LRRK2 pathogenic variants but without PD, 100 PD patients with pathogenic variants in the GBA or PRKN genes, 200 patients with idiopathic PD, and 200 healthy persons without pathogenic variants. Results: The eCRF consists of an investigator-rated (1 h) and a self-rated (1.5 h) part. The first part includes the Movement Disorder Society Unified Parkinson's Disease Rating, Hoehn \&Yahr, and Schwab \& England Scales, the Brief Smell Identification Test, and Montreal Cognitive Assessment. The self-rating part consists of a PD risk factor, food frequency, autonomic dysfunction, and quality of life questionnaires, the Pittsburgh Sleep Quality Inventory, and the Epworth Sleepiness as well as the Hospital Anxiety and Depression Scales. The first 15 centers have been initiated and the first 150 participants enrolled (as of March 25th, 2021). Conclusions: LIPAD is a large-scale international scientific effort focusing on deep phenotyping of LRRK2 -linked PD and healthy pathogenic variant carriers, including the comparison with additional relatively frequent genetic forms of PD, with a future perspective to identify genetic and environmental modifiers of penetrance and expressivity Clinical Trial Registration: ClinicalTrials.gov , NCT04214509. [less ▲] Detailed reference viewed: 33 (0 UL) Recent analytical methods for risk assessment of emerging contaminants in ecosystems; Schymanski, Emma ; in Pollution Assessment for Sustainable Practices in Applied Sciences and Engineering (2021) Detailed reference viewed: 19 (0 UL) Development and Application of Liquid Chromatographic Retention Time Indices in HRMS-Based Suspect and Nontarget Screening; ; Schymanski, Emma et alin Analytical Chemistry (2021), 93(33), 11601--11611 There is an increasing need for comparable and harmonized retention times (tR) in liquid chromatography (LC) among different laboratories, to provide supplementary evidence for the identity of compounds ... [more ▼] There is an increasing need for comparable and harmonized retention times (tR) in liquid chromatography (LC) among different laboratories, to provide supplementary evidence for the identity of compounds in high-resolution mass spectrometry (HRMS)-based suspect and nontarget screening investigations. In this study, a rigorously tested, flexible, and less system-dependent unified retention time index (RTI) approach for LC is presented, based on the calibration of the elution pattern. Two sets of 18 calibrants were selected for each of ESI+ and ESI-based on the maximum overlap with the retention times and chemical similarity indices from a total set of 2123 compounds. The resulting calibration set, with RTI set to range between 1 and 1000, was proposed as the most appropriate RTI system after rigorous evaluation, coordinated by the NORMAN network. The validation of the proposed RTI system was done externally on different instrumentation and LC conditions. The RTI can also be used to check the reproducibility and quality of LC conditions. Two quantitative structure−retention relationship (QSRR)-based models were built based on the developed RTI systems, which assist in the removal of false-positive annotations. The applicability domains of the QSRR models allowed completing the identification process with higher confidence for substances within the domain, while indicating those substances for which results should be treated with caution. The proposed RTI system was used to improve confidence in suspect and nontarget screening and increase the comparability between laboratories as demonstrated for two examples. All RTI-related calculations can be performed online at http://rti.chem.uoa.gr/. [less ▲] Detailed reference viewed: 22 (0 UL) Retrospective Non-target Analysis to Support Regulatory Water Monitoring: From Masses of Interest to Recommendations via in silico workflowsLai, Adelene ; Singh, Randolph ; et alin Preprint (2020) Detailed reference viewed: 74 (9 UL) Empowering Large Chemical Knowledge Bases for Exposomics: PubChemLite Meets MetFragSchymanski, Emma ; Kondic, Todor ; et alE-print/Working paper (2020) Detailed reference viewed: 83 (0 UL) Update on NORMAN-SLE / SusDat for NORMAN-CWG-NTS Meeting (17 Nov 2020)Schymanski, Emma ![]() Scientific Conference (2020, November 17) Detailed reference viewed: 29 (0 UL) Digital Detective Work: Connecting Cheminformatics, Mass Spectrometry and our Environment via Open Data (RSC Open Data for Chemistry)Schymanski, Emma ![]() Scientific Conference (2020, November 10) Detailed reference viewed: 28 (1 UL) Digital Detective Work: Connecting Cheminformatics, Mass Spectrometry and our Environment (analytica Conference)Schymanski, Emma ; Scientific Conference (2020, October 20) Detailed reference viewed: 33 (1 UL) Data Science and Environmental Cheminformatics (SanDAL Workshop, Uni Lu)Schymanski, Emma ![]() Presentation (2020, October 13) Detailed reference viewed: 40 (1 UL) Opponent Talk: Toxicometabolomics and biotransformation product screening in single zebrafish embryosSchymanski, Emma ![]() Presentation (2020, September 24) Detailed reference viewed: 48 (0 UL) Measuring the Environmental Exposome (ISES2020)Schymanski, Emma ![]() Scientific Conference (2020, September 21) Detailed reference viewed: 54 (2 UL) Schadstoffen auf der Spur mit UmweltcheminformatikSchymanski, Emma ![]() Scientific Conference (2020, September 17) Detailed reference viewed: 51 (0 UL) MetFrag: Annotating "Unknowns" - Exposome Boot Camp 2020 Virtual EditionSchymanski, Emma ![]() Presentation (2020, July 24) Detailed reference viewed: 76 (2 UL) Finding Small Molecules (and PFAS) with High Resolution Mass SpectrometrySchymanski, Emma ![]() Presentation (2020, May 05) Detailed reference viewed: 78 (1 UL) Interactive MS/MS Visualization with the Metabolomics Spectrum Resolver Web Service; ; et al E-print/Working paper (2020) Detailed reference viewed: 38 (0 UL) Mining the NIST Mass Spectral Library Reveals the Extent of Sodium Assisted Inductive Cleavage in Collision-Induced Fragmentation; ; et al E-print/Working paper (2020) Detailed reference viewed: 41 (1 UL) |
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