Predicting the Association of Metabolites with Both Pathway Categories and Individual Pathways.

Metabolism is a network of chemical reactions that sustain cellular life. Parts of this metabolic network are defined as metabolic pathways containing specific biochemical reactions. Products and reactants of these reactions are called metabolites, which are associated with certain human-defined metabolic pathways. Metabolic knowledgebases, such as the Kyoto Encyclopedia of Gene and Genomes (KEGG) contain metabolites, reactions, and pathway annotations; however, such resources are incomplete due to current limits of metabolic knowledge. To fill in missing metabolite pathway annotations, past machine learning models showed some success at predicting the KEGG Level 2 pathway category involvement of metabolites based on their chemical structure. Here, we present the first machine learning model to predict metabolite association to more granular KEGG Level 3 metabolic pathways. We used a feature and dataset engineering approach to generate over one million metabolite-pathway entries in the dataset used to train a single binary classifier. This approach produced a mean Matthews correlation coefficient (MCC) of 0.806 ± 0.017 SD across 100 cross-validation iterations. The 172 Level 3 pathways were predicted with an overall MCC of 0.726. Moreover, metabolite association with the 12 Level 2 pathway categories was predicted with an overall MCC of 0.891, representing significant transfer learning from the Level 3 pathway entries. These are the best metabolite pathway prediction results published so far in the field.

Exploring the biogeography, morphology, and phylogeny of the condylostomatid ciliates (Alveolata, Ciliophora, Heterotrichea), with establishment of four new Condylostoma species and a revision including redescriptions of five species found in China.

Species of the ciliate class Heterotrichea Stein, 1859 are a cosmopolitan group of unicellular eukaryotic microorganisms, many of which have been widely used as models in various fields of research such as regenerative biology, functional ecology, environmental toxicology, and symbiotic behavior. However, species identification in the heterotrich family Condylostomatidae, especially the most species-rich and type genus Condylostoma Bory de Saint-Vincent, 1824, remains challenging due to incomplete original descriptions, few reliable distinguishing characters, and overlapping features between different species. This study presents an updated revision of Condylostoma and its related genus Condylostomides da Silva Neto, 1994 based on descriptions of five species, including nine populations collected from China, using both morphological and molecular methods. The main findings are as follows: (1) 43 nominal species and about 130 populations are reviewed, resulting in the recognition of 30 valid species of Condylostoma and eight valid species of Condylostomides; (2) keys, synonyms, biogeographic distributions and amended/improved diagnoses of all valid species are provided; (3) based on the available data, four new Condylostoma species (C. marinum sp. nov., C. petzi sp. nov., C. villeneuvei sp. nov., and C. microstomum sp. nov.), one new combination (Condylostomides minimus (Dragesco, 1954) comb. nov. & nom. corr.), and two corrected names (Condylostoma ancestrale Villeneuve-Brachon, 1940 nom. corr. and Condylostomides nigrus (Dragesco, 1960) nom. corr.) are suggested; (4) cryptic species are detected and proposed for the first time to form the Condylostoma curvum species complex; (5) three highly confusing Condylostoma species, C. kris, C. spatiosum, and C. minutum, are redefined for the first time based on modern taxonomic methods; (6) a 'flagship' species, Condylostomides coeruleus, is recorded for the first time from the continent of Asia, substantially expanding its biogeography; (7) ciliature adjacent to the distal end of the paroral membrane within the family Condylostomatidae is uniformly defined as frontal membranelles and is classified into three patterns according to the arrangement of kinetosomes, which serve as important key features. Supplementary Information: The online version contains supplementary material available at 10.1007/s42995-024-00223-3.

Multi-gene-based investigation on the molecular phylogeny of the hypotrichous family Strongylidiidae (Protista, Ciliophora), with notes on the ontogeny of a new genus and new species.

Ciliates in the subclass Hypotrichia have long been difficult to classify as they are one of the most polymorphic and highly differentiated groups, leading to their systematics remaining unresolved. Phylogenetic relationships within the hypotrich family Strongylidiidae have been ambiguous due to discordance between the morphological and genetic data. In this study, a new strongylidiid genus Heterouroleptus is established, mainly based on the novel mode of origin of the ventral cirral rows: left ventral cirral row (LVR) originates from frontal-ventral-transverse cirral anlagen (FVTA) III (anterior portion), IV (middle portion), and V (rear portion); right ventral cirral row comes from the entire FVTA VI. A new species, Heterouroleptus weishanensis gen. nov., sp. nov., is investigated along with the morphometric and molecular data from a population of Strongylidium wuhanense. Eight new sequences and nuclear gene markers (single-gene and multi-gene) are provided to analyze the phylogenetic relationships of strongylidiids, with the COI gene utilized to uncover further genetic information at species level and below. The results reveal that: (1) Strongylidiidae is monophyletic and has a close relationship with Dorsomarginalia; (2) Heterouroleptus gen. nov. forms a clade that is sister to all the other strongylidiids; (3) Hemiamphisiella Foissner, 1988 and Pseudouroleptus Hemberger, 1985 should not be synonyms, and both genera should be subdivided due to their variable morphological characteristics; (4) LVR originating from three anlagen is a plesiomorphy of Strongylidiidae. The discovery of the origin of the LVR not only contributes to the establishment of the genus Heterouroleptus, but also helps to improve the diagnosis of the family Strongylidiidae. Supplementary Information: The online version contains supplementary material available at 10.1007/s42995-024-00243-z.

Predicting the Pathway Involvement of Metabolites in Both Pathway Categories and Individual Pathways.

Metabolism is the network of chemical reactions that sustain cellular life. Parts of this metabolic network are defined as metabolic pathways containing specific biochemical reactions. Products and reactants of these reactions are called metabolites, which are associated with certain human-defined metabolic pathways. Metabolic knowledgebases, such as the Kyoto Encyclopedia of Gene and Genomes (KEGG) contain metabolites, reactions, and pathway annotations; however, such resources are incomplete due to current limits of metabolic knowledge. To fill in missing metabolite pathway annotations, past machine learning models showed some success at predicting KEGG Level 2 pathway category involvement of metabolites based on their chemical structure. Here, we present the first machine learning model to predict metabolite association to more granular KEGG Level 3 metabolic pathways. We used a feature and dataset engineering approach to generate over one million metabolite-pathway entries in the dataset used to train a single binary classifier. This approach produced a mean Matthews correlation coefficient (MCC) of 0.806 ± 0.017 SD across 100 cross-validations iterations. The 172 Level 3 pathways were predicted with an overall MCC of 0.726. Moreover, metabolite association with the 12 Level 2 pathway categories were predicted with an overall MCC of 0.891, representing significant transfer learning from the Level 3 pathway entries. These are the best metabolite-pathway prediction results published so far in the field.

Adjunctive treatment of atopic dermatitis with novel at-home handheld narrow-band UVB phototherapy.

While conventional in-office phototherapy has long been utilized as a successful treatment for atopic dermatitis (AD), it is associated with potential barriers including inconvenience, poor adherence, time and financial expense. In this retrospective study, we examine the efficacy, adherence, and patient-satisfaction of using adjunctive at-home, self-administered phototherapy utilizing a novel handheld narrow-band ultraviolet B (NB-UVB) device for the treatment of refractory mild to severe AD. Included AD patients were initially trained on proper use of the device. These patients treated involved areas three times per week for a period of 12 weeks. Phototherapy dosing protocol was based on skin type. The cohort included 52 patients, who were aged 20-69 and represented all skin types. They were initially categorized by disease involvement as mild, moderate, and severe. Patients were also queried to self-score their disease severity and level of satisfaction. Compared to baseline, at 12 weeks, 48% percent of patients indicated that at least one site was Clear/Almost Clear, 38% stated that more than 50% of body locations were Clear/Almost Clear, and 28% reported that 100% (all) treated sites were Clear/Almost Clear. After using at-home hand-held NB-UVB for the study duration, 67% (35/52) of patients experienced disease improvement. Mean overall satisfaction was extremely high at 4.43 on a 5-point scale. Skin type, age, gender, and disease severity at inception did not significantly affect patient satisfaction scores. Overall adherence rate among participants across all groups was 73%. In this small retrospective study, at-home handheld NB-UVB phototherapy was found to be an effective, well-tolerated, adjunctive treatment method for patients with refractory AD, which was associated with a high level of patient satisfaction and adherence.

InChI isotopologue and isotopomer specifications.

This work presents a proposed extension to the International Union of Pure and Applied Chemistry (IUPAC) International Chemical Identifier (InChI) standard that allows the representation of isotopically-resolved chemical entities at varying levels of ambiguity in isotope location. This extension includes an improved interpretation of the current isotopic layer within the InChI standard and a new isotopologue layer specification for representing chemical intensities with ambiguous isotope localization. Both improvements support the unique isotopically-resolved chemical identification of features detected and measured in analytical instrumentation, specifically nuclear magnetic resonance and mass spectrometry. SCIENTIFIC CONTRIBUTION: This new extension to the InChI standard would enable improved annotation of analytical datasets characterizing chemical entities, supporting the FAIR (Findable, Accessible, Interoperable, and Reusable) guiding principles of data stewardship for chemical datasets, ultimately promoting Open Science in chemistry.

A cautionary tale about properly vetting datasets used in supervised learning predicting metabolic pathway involvement.

The mapping of metabolite-specific data to pathways within cellular metabolism is a major data analysis step needed for biochemical interpretation. A variety of machine learning approaches, particularly deep learning approaches, have been used to predict these metabolite-to-pathway mappings, utilizing a training dataset of known metabolite-to-pathway mappings. A few such training datasets have been derived from the Kyoto Encyclopedia of Genes and Genomes (KEGG). However, several prior published machine learning approaches utilized an erroneous KEGG-derived training dataset that used SMILES molecular representations strings (KEGG-SMILES dataset) and contained a sizable proportion (~26%) duplicate entries. The presence of so many duplicates taint the training and testing sets generated from k-fold cross-validation of the KEGG-SMILES dataset. Therefore, the k-fold cross-validation performance of the resulting machine learning models was grossly inflated by the erroneous presence of these duplicate entries. Here we describe and evaluate the KEGG-SMILES dataset so that others may avoid using it. We also identify the prior publications that utilized this erroneous KEGG-SMILES dataset so their machine learning results can be properly and critically evaluated. In addition, we demonstrate the reduction of model k-fold cross-validation (CV) performance after de-duplicating the KEGG-SMILES dataset. This is a cautionary tale about properly vetting prior published benchmark datasets before using them in machine learning approaches. We hope others will avoid similar mistakes.

Predicting the Pathway Involvement of Metabolites Based on Combined Metabolite and Pathway Features.

A major limitation of most metabolomics datasets is the sparsity of pathway annotations for detected metabolites. It is common for less than half of the identified metabolites in these datasets to have a known metabolic pathway involvement. Trying to address this limitation, machine learning models have been developed to predict the association of a metabolite with a "pathway category", as defined by a metabolic knowledge base like KEGG. Past models were implemented as a single binary classifier specific to a single pathway category, requiring a set of binary classifiers for generating the predictions for multiple pathway categories. This past approach multiplied the computational resources necessary for training while diluting the positive entries in the gold standard datasets needed for training. To address these limitations, we propose a generalization of the metabolic pathway prediction problem using a single binary classifier that accepts the features both representing a metabolite and representing a pathway category and then predicts whether the given metabolite is involved in the corresponding pathway category. We demonstrate that this metabolite-pathway features pair approach not only outperforms the combined performance of training separate binary classifiers but demonstrates an order of magnitude improvement in robustness: a Matthews correlation coefficient of 0.784 ± 0.013 versus 0.768 ± 0.154.

Predicting The Pathway Involvement Of Metabolites Based on Combined Metabolite and Pathway Features.

A major limitation of most metabolomics datasets is the sparsity of pathway annotations of detected metabolites. It is common for less than half of identified metabolites in these datasets to have known metabolic pathway involvement. Trying to address this limitation, machine learning models have been developed to predict the association of a metabolite with a "pathway category", as defined by one of the metabolic knowledgebases like the Kyoto Encyclopedia of Gene and Genomes. Most of these models are implemented as a single binary classifier specific to a single pathway category, requiring a set of binary classifiers for generating predictions for multiple pathway categories. This single binary classifier per pathway category approach both multiplies the computational resources necessary for training while diluting the positive entries in gold standard datasets needed for training. To address the limitations of training separate classifiers, we propose a generalization of the metabolic pathway prediction problem using a single binary classifier that accepts both features representing a metabolite and features representing a generic pathway category and then predicts whether the given metabolite is involved in the corresponding pathway category. We demonstrate that this metabolite-pathway features-pair approach is not only competitive with the combined performance of training separate binary classifiers, but it outperforms the previous benchmark models.

Jejunal perforation and septic abdomen resulting from a choristoma in a dog.

A 4.6-year-old spayed female German shepherd dog was admitted to a specialty hospital emergency service upon referral for suspected gastrointestinal foreign body obstruction. Free abdominal fluid was collected, and results of cytologic evaluation were consistent with a septic abdomen. An abdominal barium study revealed free gas and intraperitoneal barium, along with an obstructive gas pattern within the small bowel. Ultrasonography revealed a full-thickness jejunal perforation. On exploratory laparotomy, the perforation was noted to be located mid-jejunum with no associated mass or foreign material. A resection and anastomosis were completed. Histopathologic evaluation of the affected jejunal tissue showed aberrant gastric glandular epithelium consistent with a gastric choristoma, or heterotopic gastric tissue. Key clinical message: Clinicians should consider gastric glandular choristoma as a differential diagnosis in cases of seemingly idiopathic small intestinal perforation with no known cause (i.e., foreign body penetration, neoplasia, NSAID use), and histopathologic evaluation should always be done to obtain a definitive diagnosis.

Perforation jéjunale et abdomen septique résultant d'un choristome chez un chien. Une chienne berger allemand stérilisée âgée de 4,6 ans a été admise dans le service d'urgence d'un hôpital spécialisé après avoir été référée pour une suspicion d'obstruction gastro-intestinale par un corps étranger. Du liquide abdominal libre a été prélevé et les résultats de l'évaluation cytologique étaient compatibles avec un abdomen septique. Un examen abdominal à l'aide de baryum a révélé du gaz libre et du baryum intrapéritonéal, ainsi qu'un patron de gaz obstructif dans l'intestin grêle. L'échographie a révélé une perforation sur toute l'épaisseur jéjunale. Lors d'une laparotomie exploratoire, il a été constaté que la perforation était située au milieu du jéjunum, sans masse ni corps étranger associé. Une résection et une anastomose ont été réalisées. L'évaluation histopathologique du tissu jéjunal affecté a montré un épithélium glandulaire gastrique aberrant compatible avec un choristome gastrique ou un tissu gastrique hétérotopique.Message clinique clé :Les cliniciens doivent considérer le choristome glandulaire gastrique comme diagnostic différentiel dans les cas de perforation de l'intestin grêle apparemment idiopathique sans cause connue (i.e. pénétration d'un corps étranger, néoplasie, utilisation d'AINS), et une évaluation histopathologique doit toujours être effectuée pour obtenir un diagnostic définitif.(Traduit par Dr Serge Messier).

md_harmonize: A Python Package for Atom-Level Harmonization of Public Metabolic Databases.

A major challenge to integrating public metabolic resources is the use of different nomenclatures by individual databases. This paper presents md_harmonize, an open-source Python package for harmonizing compounds and metabolic reactions across various metabolic databases. The md_harmonize package utilizes a neighborhood-specific graph coloring method for generating a unique identifier for each compound via atom identifiers based on a compound's chemical structure. The resulting harmonized compounds and reactions can be used for various downstream analyses, including the construction of atom-resolved metabolic networks and models for metabolic flux analysis. Parts of the md_harmonize package have been optimized using a variety of computational techniques to allow certain NP-complete problems handled by the software to be tractable for these specific use-cases. The software is available on GitHub and through the Python Package Index, with end-user documentation hosted on GitHub Pages.

Benchmark Dataset for Training Machine Learning Models to Predict the Pathway Involvement of Metabolites.

Metabolic pathways are a human-defined grouping of life sustaining biochemical reactions, metabolites being both the reactants and products of these reactions. But many public datasets include identified metabolites whose pathway involvement is unknown, hindering metabolic interpretation. To address these shortcomings, various machine learning models, including those trained on data from the Kyoto Encyclopedia of Genes and Genomes (KEGG), have been developed to predict the pathway involvement of metabolites based on their chemical descriptions; however, these prior models are based on old metabolite KEGG-based datasets, including one benchmark dataset that is invalid due to the presence of over 1500 duplicate entries. Therefore, we have developed a new benchmark dataset derived from the KEGG following optimal standards of scientific computational reproducibility and including all source code needed to update the benchmark dataset as KEGG changes. We have used this new benchmark dataset with our atom coloring methodology to develop and compare the performance of Random Forest, XGBoost, and multilayer perceptron with autoencoder models generated from our new benchmark dataset. Best overall weighted average performance across 1000 unique folds was an F1 score of 0.8180 and a Matthews correlation coefficient of 0.7933, which was provided by XGBoost binary classification models for 11 KEGG-defined pathway categories.

Benchmark dataset for training machine learning models to predict the pathway involvement of metabolites.

Metabolic pathways are a human-defined grouping of life sustaining biochemical reactions, metabolites being both the reactants and products of these reactions. But many public datasets include identified metabolites whose pathway involvement is unknown, hindering metabolic interpretation. To address these shortcomings, various machine learning models, including those trained on data from the Kyoto Encyclopedia of Genes and Genomes (KEGG), have been developed to predict the pathway involvement of metabolites based on their chemical descriptions; however, these prior models are based on old metabolite KEGG-based datasets, including one benchmark dataset that is invalid due to the presence of over 1500 duplicate entries. Therefore, we have developed a new benchmark dataset derived from the KEGG following optimal standards of scientific computational reproducibility and including all source code needed to update the benchmark dataset as KEGG changes. We have used this new benchmark dataset with our atom coloring methodology to develop and compare the performance of Random Forest, XGBoost, and multilayer perceptron with autoencoder models generated from our new benchmark dataset. Best overall weighted average performance across 1000 unique folds was an F1-score of 0.8180 and Matthews correlation coefficient of 0.7933, which was provided by XGBoost binary classification models for 11 KEGG-defined pathway categories.

New evidence of consistency between phylogeny and morphology for two taxa in ciliated protists, the subclasses Oligotrichia and Choreotrichia (Protista, Ciliophora).

Marine planktonic ciliates are largely oligotrichs and choreotrichs, which are two subclasses of the class Spirotrichea. The current phylogenetic assignments of oligotrichs and choreotrichs are inconsistent with previous results based on morphological features, probably hindered by the limited information from a single gene locus. Here we provide 53 new sequences from small subunit ribosomal RNA (SSU rDNA), ITS1-5.8S rDNA-ITS2, and large subunit ribosomal RNA (LSU rDNA) gene loci in 25 oligotrich and choreotrich species. We also predict RNA secondary structures for the ITS2 regions in 55 species, 48 species of which are reported for the first time. Based on these novel data, we make a more comprehensive phylogenetic reconstruction, revealing consistency between morphological taxonomy and an updated phylogenetic system for oligotrichs and choreotrichs. With the addition of data from ciliature patterns and genes, the phylogenetic analysis of the subclass Oligotrichia suggests three evolutionary trajectories, among which: 1) Novistrombidium asserts an ancestral ciliary pattern in Oligotrichia; 2) the subgenera division of Novistrombidium and Parallelostrombidium are fully supported; 3) the three families (Tontoniidae, Pelagostrombidiidae and Cyrtostrombidiidae) all evolved from the most diverse family Strombidiidae, which explains why strombidiids consistently form polyphyletic clades. In the subclass Choreotrichia, Strombidinopsis likely possesses an ancestral position to other choreotrichs, and both phylogenetic analysis and RNA secondary structure prediction support the hypothesis that tintinnids may have evolved from Strombidinopsis. The results presented here offer an updated hypothesis for the evolutionary history of oligotrichs and choreotrichs based on new evidence obtained by expanding sampling of molecular information across multiple gene loci.

First redescription and molecular phylogeny of Trimyema claviforme Kahl, 1933 with the description of a Chinese population of Plagiopyla nasuta Stein, 1860 (Ciliophora, Plagiopylea).

Ciliates belonging to the class Plagiopylea are obligate anaerobes that are often neglected due to their cryptic lifestyles, difficulty of observation, and overall under-sampling. Here, we investigate two species, namely Trimyema claviforme Kahl, 1933 and Plagiopyla nasuta Stein, 1860, collected in China from marine and freshwater anaerobic sediments, respectively. A complete morphological dataset, together with SSU rRNA gene sequence data were obtained and used to diagnose the species. No molecular sequencing had ever been performed on Trimyema claviforme, with its ciliature also previously unknown. Based on these novel data presented here, the ciliate is characterized by a claviform cell shape, with a size of 35-45 × 10-20 µm in vivo, 28-39 longitudinal somatic ciliary rows forming five ciliary girdles (four complete girdles and a shorter one), two dikinetids left to anterior end of oral kinety 1, and an epaulet. A Chinese population of the well-known ciliate P. nasuta was investigated, and morphological comparisons revealed phenotypic stability of the species. The phylogenetic analyses supported previous findings about the monophyly of the families Trimyemidae and Plagiopylidae, with Trimyema claviforme branching off early in the genus Trimyema. The Chinese population of P. nasuta clusters together with two other populations with full support corroborating their conspecificity.

Taxonomy and molecular phylogeny of pleurostomatid ciliates from China with a description of two new species.

Ciliates in the order Pleurostomatida are found free-living in many habitats including within biofilms, but some (e.g. Pseudoamphileptus spp.) are ectocommensal on various hosts. Due to issues involving overall undersampling, the exact diversity and molecular phylogeny of this group remain largely underexplored. To combat this deficiency, detailed investigations were undertaken in northern China. As a result of these studies, we provide the morphological descriptions of two new species. Pseudoamphileptus apomacrostoma sp. nov., a new ectocommensal species, is characterized by the broadly oval cell shape, numerous scattered contractile vacuoles, and unique densely bounded extrusomes; Amphileptus qingdaoensis sp. nov., a marine form, is characterized by possessing oblong extrusomes with a conical anterior end, a single contractile vacuole and 5-7 left and 18-23 right kineties. In addition, a new population of Amphileptus orientalis Zhang et al., 2022, a freshwater representative, was documented and an improved diagnosis is provided. The phylogenetic analyses based on the SSU rDNA sequences imply that the genus Pseudoamphileptus is monophyletic whereas the genus Amphileptus is paraphyletic. The new molecular sequences presented here further support the establishment of two new species.

Specific prostaglandins are produced in the migratory cells and the surrounding substrate to promote Drosophila border cell migration.

A key regulator of collective cell migration is prostaglandin (PG) signaling. However, it remains largely unclear whether PGs act within the migratory cells or their microenvironment to promote migration. Here we use Drosophila border cell migration as a model to uncover the cell-specific roles of two PGs in collective migration. Prior work shows PG signaling is required for on-time migration and cluster cohesion. We find that the PGE2 synthase cPGES is required in the substrate, while the PGF2α synthase Akr1B is required in the border cells for on-time migration. Akr1B acts in both the border cells and their substrate to regulate cluster cohesion. One means by which Akr1B regulates border cell migration is by promoting integrin-based adhesions. Additionally, Akr1B limits myosin activity, and thereby cellular stiffness, in the border cells, whereas cPGES limits myosin activity in both the border cells and their substrate. Together these data reveal that two PGs, PGE2 and PGF2α, produced in different locations, play key roles in promoting border cell migration. These PGs likely have similar migratory versus microenvironment roles in other collective cell migrations.

The metabolomics workbench file status website: a metadata repository promoting FAIR principles of metabolomics data.

BACKGROUND: An updated version of the mwtab Python package for programmatic access to the Metabolomics Workbench (MetabolomicsWB) data repository was released at the beginning of 2021. Along with updating the package to match the changes to MetabolomicsWB's 'mwTab' file format specification and enhancing the package's functionality, the included validation facilities were used to detect and catalog file inconsistencies and errors across all publicly available datasets in MetabolomicsWB. RESULTS: The MetabolomicsWB File Status website was developed to provide continuous validation of MetabolomicsWB data files and a useful interface to all found inconsistencies and errors. This list of detectable issues/errors include format parsing errors, format compliance issues, access problems via MetabolomicsWB's REST interface, and other small inconsistencies that can hinder reusability. The website uses the mwtab Python package to pull down and validate each available analysis file and then generates an html report. The website is updated on a weekly basis. Moreover, the Python website design utilizes GitHub and GitHub.io, providing an easy to replicate template for implementing other metadata, virtual, and meta- repositories. CONCLUSIONS: The MetabolomicsWB File Status website provides a metadata repository of validation metadata to promote the FAIR use of existing metabolomics datasets from the MetabolomicsWB data repository.

MESSES: Software for Transforming Messy Research Datasets into Clean Submissions to Metabolomics Workbench for Public Sharing.

In recent years, the FAIR guiding principles and the broader concept of open science has grown in importance in academic research, especially as funding entities have aggressively promoted public sharing of research products. Key to public research sharing is deposition of datasets into online data repositories, but it can be a chore to transform messy unstructured data into the forms required by these repositories. To help generate Metabolomics Workbench depositions, we have developed the MESSES (Metadata from Experimental SpreadSheets Extraction System) software package, implemented in the Python 3 programming language and supported on Linux, Windows, and Mac operating systems. MESSES helps transform tabular data from multiple sources into a Metabolomics Workbench specific deposition format. The package provides three commands, extract, validate, and convert, that implement a natural data transformation workflow. Moreover, MESSES facilitates richer metadata capture than is typically attempted by manual efforts. The source code and extensive documentation is hosted on GitHub and is also available on the Python Package Index for easy installation.

A Case for Accelerating Standards to Achieve the FAIR Principles of Environmental Health Research Experimental Data.

BACKGROUND: Funding agencies, publishers, and other stakeholders are pushing environmental health science investigators to improve data sharing; to promote the findable, accessible, interoperable, and reusable (FAIR) principles; and to increase the rigor and reproducibility of the data collected. Accomplishing these goals will require significant cultural shifts surrounding data management and strategies to develop robust and reliable resources that bridge the technical challenges and gaps in expertise. OBJECTIVE: In this commentary, we examine the current state of managing data and metadata-referred to collectively as (meta)data-in the experimental environmental health sciences. We introduce new tools and resources based on in vivo experiments to serve as examples for the broader field. METHODS: We discuss previous and ongoing efforts to improve (meta)data collection and curation. These include global efforts by the Functional Genomics Data Society to develop metadata collection tools such as the Investigation, Study, Assay (ISA) framework, and the Center for Expanded Data Annotation and Retrieval. We also conduct a case study of in vivo data deposited in the Gene Expression Omnibus that demonstrates the current state of in vivo environmental health data and highlights the value of using the tools we propose to support data deposition. DISCUSSION: The environmental health science community has played a key role in efforts to achieve the goals of the FAIR guiding principles and is well positioned to advance them further. We present a proposed framework to further promote these objectives and minimize the obstacles between data producers and data scientists to maximize the return on research investments. https://doi.org/10.1289/EHP11484.