Biology subjects

Moseley, H. N. B.

Publications and source records attributed to Moseley, H. N. B..

At least 19 recordsLinked to original sources

bioRxiv2026

The recount3 Python package for programmatic access to uniformly processed RNA-seq data

The recount3 online resource provides tens of thousands of uniformly processed RNA-seq samples across human and mouse from major sequencing repositories like the Sequence Read Archive. While access to these datasets has traditionally been centered in the R/Bioconductor ecosystem, the growing prominence of Python in bioinformatics and machine learning necessitates native, efficient tooling for Python users. Therefore, we present the recount3 Python package with robust application programming interface (API) and command-line interface (CLI) for discovering, downloading, and materializing recount3 resources. The software orchestrates uniform resource locator (URL) resolution, persistent on-disk caching, and the automatic parsing of data into analysis-ready data structures, including Pandas DataFrames and BiocPy RangedSummarizedExperiment objects. The recount3 Python package drastically lowers the barrier to entry for large-scale utilization of RNA-seq data in Python-based computational pipelines, bridging the gap between massive public transcriptomic data and modern machine learning ecosystems.

bioinformatics↗

bioRxiv2025

Major Update and Improved Validation Functionality in the mwtab Python Library and the Metabolomics Workbench File Status Website

The Metabolomics Workbench (MW) is a public scientific data repository consisting of experimental data and metadata from metabolomics studies collected with mass spectroscopy (MS) and nuclear magnetic resonance (NMR) analyses. Although not as rapidly as in the past, MW has steadily evolved; updating its mwTab and JSON deposition text file formats and its web-based infrastructure. However, the growth of MW has been exponential since its inception in 2013 and continues to be exponential, with the number of datasets hosted on the repository increasing by 50% since April 2024. As part of regular maintenance to keep up with changes to the mwTab file format and an earnest effort to use MW datasets in meta-analyses, the mwtab Python package has been updated. Updates include better error handling for batch processing, better parsing to read more files without error, and extensive improvements to the validation capabilities of the package. These updates also required our mwFileStatusWebsite to be updated and improved. We used the enhanced validation features of the mwtab package to evaluate all available datasets in MW to facilitate improved curation, FAIRness of the repository, and reuse for meta-analyses. Version 2.0.0 of the mwtab Python package is now officially released and freely available on GitHub and the Python Package Index (PyPI) under a Clear Berkeley Software Distribution (BSD) license with documentation available on GitHub. The updated mwFileStatusWebsite is also officially in its 2.0.0 version and is still available at https://moseleybioinformaticslab.github.io/mwFileStatusWebsite/.

bioinformatics↗

bioRxiv2025

High-Quality Predicted Pathway Annotations Greatly Improve Pathway Enrichment Analysis of Metabolomics Datasets

Background/ObjectivesMetabolism-level interpretation of metabolomics datasets requires aggregation analyses across metabolites. One highlyused aggregation analysis is pathway enrichment analysis (PEA), which involves detecting pathways enriched with metabolites that are differential between experimental groups. Annotating metabolites with pathway associations is a prerequisite for PEA. While several knowledgebases define pathways and include metabolite-pathway annotations, these definitions are often partially or even grossly incomplete due to limitations in current metabolic knowledge and its curation, which greatly limits the effectiveness of PEA. MethodsIn this work, we used a novel multitask classification, graph convolutional-like neural network to generate high-quality metabolite-pathway annotations for pathways defined across KEGG, MetaCyc, and Reactome. We then included these predicted metabolite-pathway annotations when performing PEA on 990 datasets deposited in Metabolomics Workbench. ResultsWe demonstrate an 8-fold increase in the median number of enriched pathways detected across these datasets compared to using only knowledgebase-derived annotations. ConclusionsThe significant increase in enriched pathways substantially improves the biological and biomedical interpretability of metabolomics datasets.

systems biology↗

bioRxiv2025

Chemical representation standardization needed to generalize metabolic pathway involvement prediction across the Kyoto Encyclopedia of Genes and Genomes, Reactome, and MetaCyc knowledgebases

MotivationDue to the utility of knowing the pathway involvement of compounds detected in biological experiments, knowledgebases such as the Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, and MetaCyc have aggregated pathway annotations of compounds. However, these annotations are largely incomplete and are costly to obtain experimentally and curate from published scientific literature. ResultsWe constructed a new dataset using compounds and their pathway annotations from KEGG, Reactome, and MetaCyc. Using this dataset, we trained and tested an extreme classification model that classifies 8,195 unique pathways based on compound chemical representations with a mean Matthews correlation coefficient (MCC) of 0.9036 {+/-} 0.0033. During model evaluation, we discovered an inconsistency in chemical representations across knowledgebases, which was alleviated by standardizing the chemical representations using InChI (IUPAC International Chemical Identifier) canonicalization. Next, we compared the MCC between compounds and their cross-knowledgebase references. The non-standardized chemical representations had a huge 0.2687 drop in MCC while the standardized chemical representations only had a 0.0384 drop in MCC. Thus, standardizing chemical representation is an essential step when predicting on novel chemical representations. Availability and implementationAll code and data for reproducing the results of this manuscript are available in the following figshare items: Manuscript main results: https://doi.org/10.6084/m9.figshare.28701845 CV analysis of model and dataset of prior studies: https://doi.org/10.6084/m9.figshare.28701590 Contacthunter.moseley@uky.edu Supplementary information

systems biology↗

bioRxiv2024

Predicting the Pathway Involvement of Compounds Annotated in the Reactome Knowledgebase

MotivationPathway annotations of non-macromolecular (relatively small) biomolecules facilitate biological and biomedical interpretation of metabolomics datasets. However, low pathway annotation levels of detected biomolecules hinder this type of interpretation. Thus, predicting the pathway involvement of detected but unannotated biomolecules has high potential to improve metabolomics data analysis and omics integration. Past publications have only made use of the Kyoto Encyclopedia of Genes and Genomes derived datasets to develop machine learning models to predict pathway involvement. However, to our knowledge, the Reactome knowledgebase has not been utilized to develop these types of predictive models. ResultsWe created a dataset ready for machine learning using chemical representations of all path-way-annotated compounds available from the Reactome knowledgebase. Next, we trained and evaluated a single multilayer perceptron binary classifier using combined metabolite-pathway paired feature vectors engineered from this new dataset. While models trained on a prior corresponding KEGG dataset with 502 pathways scored a mean Matthews correlation coefficient (MCC) of 0.847 and 0.0098 standard deviation, the models trained on the Reactome dataset with 3,985 pathways demonstrated improved performance with a mean MCC of 0.916, but with a higher 0.0149 standard deviation. These results indicate that the pathways in Reactome can also be effectively predicted, greatly increasing the number of human-defined pathways available for prediction. AvailabilityCode and data for fully reproducing the results in this work are available at https://doi.org/10.6084/m9.figshare.27478065. Contacthunter.moseley@uky.edu.

systems biology↗

bioRxiv2024

Predicting the pathway involvement of metabolites annotated in the MetaCyc knowledgebase

The associations of metabolites with biochemical pathways are highly useful information for interpreting molecular datasets generated in biological and biomedical research. However, such pathway annotations are sparse in most molecular datasets, limiting their utility for pathway level interpretation. To address these shortcomings, several past publications have presented machine learning models for predicting the pathway association of small biomolecule (metabolite and zenobiotic) using data from the Kyoto Encyclopedia of Genes and Genomes (KEGG). But other similar knowledgebases exist, for example MetaCyc, which has more compound entries and pathway definitions than KEGG. As a logical next step, we trained and evaluated multilayer perceptron models on compound entries and pathway annotations obtained from MetaCyc. From the models trained on this dataset, we observed a mean Matthews correlation coefficient (MCC) of 0.845 with 0.0101 standard deviation, compared to a mean MCC of 0.847 with 0.0098 standard deviation for the KEGG dataset. These performance results are pragmatically the same, demonstrating that MetaCyc pathways can be effectively predicted at the current state-of-the-art performance level. Author summaryMany thousands of different molecules play important roles in the processes of life. To generally handle the complexity of life, biological and biomedical researchers typically organize the molecular parts and pieces of biological processes into pathways of biomolecules and their myriad of molecular interactions. While the role of large macromolecules like proteins are well characterized within these pathways, the role of small biomolecules are not as comprehensively known. To close this knowledge gap, several machine learning models have been trained on data from a knowledgebase known as the Kyoto Encyclopedia of Genes and Genomes (KEGG) to predict which pathways a small biomolecule is associated with. More data generally improves these machine learning models. So in this work, we used the MetaCyc knowledgebase to increase the amount of data available by about ten-fold and then trained new machine learning models that demonstrate comparable prediction performance to models trained on KEGG, but covering 8-fold more pathways defined in MetaCyc vs KEGG.

systems biology↗

bioRxiv2024

Predicting The Pathway Involvement For All Pathway and Associated Compound Entries Defined in the Kyoto Encyclopedia of Gene and Genomes

Background/ObjectivesPredicting the biochemical pathway involvement of a compound could facilitate the interpretation of biological and biomedical research. Prior prediction approaches have largely focused on metabolism, training machine learning models to solely predict on metabolic pathways. However, there are many other types of pathways in cells and organisms which are of interest to biologists; MethodsWhile several publications have made use of the metabolites and metabolic pathways available in the Kyoto Encyclopedia of Genes and Genomes (KEGG), we downloaded all the compound entries with pathway annotations available in KEGG. From this data, we constructed a dataset where each entry contained features representing compounds combined with features representing pathways followed by a binary label indicating whether the given compound is associated with the given pathway. We trained multi-layer perceptron binary classifiers on variations of this dataset; ResultsThe model trained on all KEGG compounds and pathways scored an overall mean performance of 0.847, median of 0.848, and standard deviation of 0.0098. ConclusionsThe mean performance of 0.847 with a standard deviation of 0.0098 for all KEGG pathways, compared to the performance of 0.800 and standard deviation of 0.021 of metabolic KEGG pathways only, demonstrates the capability to effectively predict biochemical pathways in general in addition to those specifically related to metabolism. Moreover, the improvement in the performance demonstrates additional transfer learning with the inclusion of non-metabolic pathways.

systems biology↗

bioRxiv2024

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.

systems biology↗

bioRxiv2024

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.

systems biology↗

bioRxiv2023

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 Gene 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 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.

bioinformatics↗

bioRxiv2023

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 reac-tions, metabolites being both the reactants and products of these reactions. But many public datasets include identified metabolites whose pathway involvement is unknown, hindering metabolic inter-pretation. 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 pre-dict 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 compu-tational 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 coeffi-cient of 0.7933, which was provided by XGBoost binary classification models for 11 KEGG-defined pathway categories.

bioinformatics↗

bioRxiv2023

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 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 (https://github.com/MoseleyBioinformaticsLab/MESSES) and is also available on the Python Package Index (https://pypi.org/project/messes) for easy installation.

bioinformatics↗

bioRxiv2022

md_harmonize: a Python package for atom-level harmonization of public metabolic data-bases

SummaryA big 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. md_harmonize utilizes a neighborhood-specific graph coloring method for generating a unique identifier for each compound via atom identifiers based on the compound structure. The resulting harmonized compounds and reactions can be used to construct metabolic networks and models for various downstream analyses, including metabolic flux analysis. AvailabilityThe md_harmonize package is implemented in Python and freely available at https://github.com/MoseleyBioinformaticsLab/md_harmonize Contacthunter.moseley@uky.edu Supplementary informationSupplementary data are available at https://doi.org/10.6084/m9.figshare.21699683.

bioinformatics↗

bioRxiv2022

kegg_pull: a Software Package for the RESTful Access and Pulling from The Kyoto Encyclopedia of Gene and Genomes

BackgroundThe Kyoto Encyclopedia of Genes and Genomes (KEGG) provides organized genomic, biomolecular, and metabolic information and knowledge that is reasonably current and highly useful for a wide range of analyses and modeling. KEGG follows the principles of data stewardship to be findable, accessible, interoperable, and reusable (FAIR) by providing RESTful access to their database entries via their web-accessible KEGG API. However, the overall FAIRness of KEGG is often limited by the library and software package support available in a given programming language. While R library support for KEGG is fairly strong, Python library support has been lacking. Moreover, there is no software that provides extensive command line level support for KEGG access and utilization. ResultsWe present kegg_pull, a package implemented in the Python programming language that provides better KEGG access and utilization functionality than previous libraries and software packages. Not only does kegg_pull include an application programming interface (API) for Python programming, it also provides a command line interface (CLI) that enables language agnostic utilization of KEGG for a wide range of data analysis pipeline use-cases. As kegg_pulls name implies, both the API and CLI provide versatile options for pulling (downloading and saving) an arbitrary (user defined) number of database entries from the KEGG API. Moreover, this functionality is implemented to efficiently utilize multiple central processing unit cores. Many options are provided to optimize fault-tolerant performance across a single or multiple processes, with recommendations provided based on extensive testing and practical network considerations. ConclusionsThe new kegg_pull package enables new flexible KEGG retrieval use cases not available in previous software packages. The most notable new feature that kegg_pull provides is its ability to robustly pull an arbitrary number of KEGG entries with a single API method or CLI command, including pulling an entire KEGG database. We provide recommendations to users for the most effective use of kegg_pull according to their network and computational circumstances.

bioinformatics↗

bioRxiv2022

Scan-Centric, Frequency-Based Method for Characterizing Peaks from Direct Injection Fourier transform Mass Spectrometry Experiments

We introduce a novel, scan-centric method for characterizing peaks from direct injection multi-scan Fourier transform mass spectra of complex samples that utilizes frequency values derived directly from the spacing of raw m/z points in spectral scans. Our peak characterization method utilizes intensity independent noise removal and normalization of scan-level data to provide a much better fit of relative intensity to natural abundance probabilities for low abundance isotopologues that are not present in all of the acquired scans. Moreover, our method calculates both peak- and scan-specific statistics incorporated within a series of quality control steps that are designed to robustly derive peak centers, intensities and intensity ratios with their scan-level variances. These cross-scan characterized peaks are suitable for use in our previously published peak assignment methodology, Small Molecule Isotope Resolved Formula Enumeration (SMIRFE).

bioinformatics↗

bioRxiv2022

Academic Tracker: Software for Tracking and Reporting Publications Associated with Authors and Grants

In recent years, United States federal funding agencies, including the National Institutes of Health (NIH) and the National Science Foundation (NSF), have implemented public access policies to make research supported by funding from these federal agencies freely available to the public. Enforcement is primarily through annual and final reports submitted to these funding agencies, where all peer-reviewed publications must be registered through the appropriate mechanism as required by the specific federal funding agency. Unreported and/or incorrectly reported papers can result in delayed acceptance of annual and final reports and even funding delays for current and new research grants. So, its important to make sure every peer-reviewed publication is reported properly and in a timely manner. For large collaborative research efforts, the tracking and proper registration of peer-reviewed publications along with generation of accurate annual and final reports can create a large administrative burden. With large collaborative teams, it is easy for these administrative tasks to be overlooked, forgotten, or lost in the shuffle. In order to help with this reporting burden, we have developed the Academic Tracker software package, implemented in the Python 3 programming language and supporting Linux, Windows, and Mac operating systems. Academic Tracker helps with publication tracking and reporting by comprehensively searching major peer-reviewed publication tracking web portals, including PubMed, Crossref, ORCID, and Google Scholar, given a list of authors. Academic Tracker provides highly customizable reporting templates so information about the resulting publications is easily transformed into appropriate formats for tracking and reporting purposes. The source code and extensive documentation is hosted on GitHub (https://moseleybioinformaticslab.github.io/academic_tracker/) and is also available on the Python Package Index (https://pypi.org/project/academic_tracker) for easy installation.

bioinformatics↗

bioRxiv2022

The Metabolomics Workbench File Status Website: A Metadata Repository Promoting FAIR Principles of Metabolomics Data

MotivationAn 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 MetabolomicsWBs mwTab file format and to enhance the packages functionality, the package included format validation facilities which were used to detect and catalog file inconsistencies and errors across all publically available datasets in MetabolomicsWB. ResultsThe Metabolomics Workbench File Status website was developed to provide continuous validation of MetabolomicsWB data files and a useful interface to find all inconsistencies and errors that are found. This list of detectable issues/errors include format parsing errors, format compliance issues, access problems via MetabolomicsWBs 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. AvailabilityMetabolomics Workbench file status website can be accessed at: https://moseleybioinformaticslab.github.io/mwFileStatusWebsite/. The mwtab Python library is available on GitHub: https://github.com/MoseleyBioinformaticsLab/mwtab, PyPI: https://pypi.org/project/mwtab/, and documentation is available on ReadTheDocs: https://mwtab.readthedocs.io/. Metabolomics Workbench analysis data files used for analysis presented here along with the generated HTML files of the website are available on FigShare: https://doi.org/10.6084/m9.figshare.19221159. Contacthunter.moseley@uky.edu Supplementary informationSupplementary data are available at FigShare online.

systems biology↗

bioRxiv2022

Information-Content-Informed Kendall-tau Correlation: Utilizing Missing Values

BackgroundAlmost all correlation measures currently available are unable to directly handle missing values. Typically, missing values are either ignored completely by removing them or are imputed and used in the calculation of the correlation coefficient. In either case, the correlation value will be impacted based on a perspective that the missing data represents no useful information. However, missing values occur in real data sets for a variety of reasons. In metabolomics data sets, a major reason for missing values is that a specific measurable phenomenon falls below the detection limits of the analytical instrumentation (left-censored values). These missing data are not missing at random, but represent potentially useful information by virtue of their "missingness" at one end of the data distribution. MethodsTo include this information due to left-censorsed missingness, we propose the information-content-informed Kendall-tau (ICI-Kt) methodology. We developed a statistical test and then show that most missing values in metabolomics datasets are the result of left-censorship. Next, we show how left-censored missing values can be included within the definition of the Kendall-tau correlation coefficient, and how that inclusion leads to an interpretation of information being added to the correlation. We also implement calculations for additional measures of theoretical maxima and pairwise completeness that add further layers of information interpretation in the methodology. ResultsUsing both simulated and over 700 experimental data sets from The Metabolomics Workbench, we demonstrate that the ICI-Kt methodology allows for the inclusion of left-censored missing data values as interpretable information, enabling both improved determination of outlier samples and improved feature-feature network construction. ConclusionsWe provide explicitly parallel implementations in both R and Python that allow fast calculations of all the variables used when applying the ICI-Kt methodology on large numbers of samples. The ICI-Kt methods are available as an R package and Python module on GitHub at https://github.com/moseleyBioinformaticsLab/ICIKendallTau and https://github.com/moseleyBioinformaticsLab/icikt, respectively.

bioinformatics↗