Biology subjects

Hashimoto-Roth, E.

Publications and source records attributed to Hashimoto-Roth, E..

5 recordsLinked to original sources

bioRxiv2026

Desktop iDMS: A stand-alone application of intelligent differential mobility spectrometry (iDMS), a neural net-work that predicts optimal separation and compensa-tion voltages for field asymmetric differential ion mobil-ity spectrometry

Summary: Mass spectrometry resolution of glycosphingolipids requires advanced separation methods. The virtual-ly identical structures of glycolipid epimers cannot be discriminated by routine multiple reaction monitoring-high-performance liquid chromatography-electrospray ionization-tandem mass spectrometry (MRM-LC-ESI-MS/MS). Sep-aration can be achieved using field asymmetric (differential) ion mobility mass spectrometry (FAIMS/DMS). A major bottleneck to MRM-LC-ESI-DMS-MS/MS method development lies in manually optimizing the compound-dependent parameters of separation voltage (SV) and compensation voltage (CoV). To address this challenge, we developed intelligent Differential Mobility Spectrometry (iDMS). iDMS is a supervised deep neural network that evaluates and predicts the optimal ion mobility SV and CoV parameters for any user-requested monoglycosphingolipid from a training set of measured signal intensities composed of 12 lipids (6 stereoisomer pairs). We initially introduced iDMS as a series of python scripts. Here, we share desktop applications of both iDMS and its pre-processing input data assembly modules (DMS Data Extraction Toolkit). Both software assemblies enable users to rapidly deploy a validated machine learning alternative to manual DMS optimization. The applications are user-friendly, operate with-out need for any technical operating environment, make training dataset assembly quick and simple, and return measured, normalized, and predicted ionograms at every user-interrogated SV, evaluation of separability at each SV and CoV combination, and prediction of optimal resolution parameters to accelerate users MRM-LC-ESI-DMS-MS/MS deployment. Availability and implementation: Desktop iDMS and DMS Data Extraction Toolkit are native desktop applications for macOS (Apple Silicon), freely available for download at https://www.neurolipidomics.com/platforms.html under the GNU v3.0 license. The source code is available from GitHub and Zenodo at https://github.com/Neurolipidomics/iDMS, https://zenodo.org/records/22214408, https://github.com/Neurolipidomics/DMSDataExtractionToolkit, and https://zenodo.org/records/22777403. Bench-marking metrics are available at https://www.biorxiv.org/content/10.64898/2026.08.26.747394v1.

bioinformatics↗

bioRxiv2026

Intelligent differential ion mobility spectrometry (iDMS): A deep neural network that predicts optimal space-resolved ion mobility parameters for isomeric monoglycosphingolipids

Simultaneous quantification of monoglycosphingolipid stereoisomers is required to monitor changes in defective enzymatic pathways linked to diseases such as Gaucher Disease, Parkinson's Disease, and Krabbe Disease. Resolution of beta-glucosyl and beta-galactosyl epimers cannot be achieved by standard liquid chromatography, electrospray ionization, tandem mass spectrometry (LC-ESI-MS/MS). Separation becomes possible when field asymmetric ion mobility spectrometry (FAIMS), also known as differential mobility mass spectrometry (DMS), is added as an orthogonal separation technique to LC. FAIMS/DMS separates epimeric ion clusters in a high versus low electric field (separation voltage, SV) then redirects the target epimeric ions to the mass spectrometer through the application of a direct current (compensation voltage, CoV). Resolving SVs and CoVs must be manually determined for each lipid. Manual derivation is a labour-intensive process that requires pure synthetic standards, limiting the number of stereoisomers a user can include in an assay. To address this problem, we introduce here intelligent DMS (iDMS). iDMS is an in silico supervised neural network model that learns the ion mobility relationships between SV and CoV and the monoglycosphingolipid structural features of sugar headgroup, N-acyl chain length, and N-acyl degree of unsaturation. iDMS predicts the SV and CoV combinations capable of resolving any stereoisomer pair from a training dataset of composed of measured signal intensities across a range of SVs and CoVs of 12 lipids. This machine learning alternative to manual DMS optimization promises to accelerate the deployment of multiple-reaction-monitoring mode (MRM) RPLC-ESI-DMS-MS/MS assays for the routine and rapid quantification of biologically relevant monoglycosphingolipid stereoisomers.

bioinformatics↗

bioRxiv2024

Imputation for Lipidomics and Metabolomics (ImpLiMet): Online optimization and method selection for missing data imputation

MotivationMissing values are often unavoidable in modern high-throughput measurements due to various experimental or analytical reasons. Imputation, the process of replacing missing values in a dataset with estimated values, plays an important role in multivariate and machine learning analyses. Three missingness patterns have been conceptualized: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR). Each describes unique dependencies between the missing and observed data. As a result, the optimal imputation method for each dataset depends on the type of data, the cause of the missing data, and the nature of relationships between the missing and observed data. The challenge is to identify the optimal imputation solution for a given dataset. ResultsImpLiMet: is a user-friendly UI-platform that enables users to impute missing data using eight different methods. For the users dataset, ImpLiMet can suggest the optimal imputation solution through a grid search-based investigation of the error rate for imputation across three missingness data simulations. The effect of imputation can be visually assessed by histogram, kurtosis and skewness analyses, as well as principal component analysis (PCA) comparing the impact of the chosen imputation method on the distribution and overall behaviour of the data. Availability and implementationImpLiMet is freely available at https://complimet.ca/shiny/implimet/ with software accessible at https://github.com/complimet/ImpLiMet Contactsteffanyann.bennett@uottawa.ca and miroslava.cuperlovic-culf@nrc-cnrc.gca. Supplementary informationSupplementary data are available at Bioinformatics Advances online.

bioinformatics↗

bioRxiv2022

METAbolomics data Balancing with Over-sampling Al-gorithms (META-BOA): an online resource for addressing class imbalance

MotivationClass imbalance, or unequal sample sizes between classes, is an increasing concern in machine learning for metabolomic and lipidomic data mining, which can result in overfitting for the over-represented class. Numerous methods have been developed for handling class imbalance, but they are not readily accessible to users with limited computational experience. Moreover, there is no resource that enables users to easily evaluate the effect of different over-sampling algorithms. ResultsMETAbolomics data Balancing with Over-sampling Algorithms (META-BOA) is a web-based application that enables users to select between four different methods for class balancing, followed by data visualization and classification of the sample to observe the augmentation effects. META-BOA outputs a newly balanced dataset, generating additional samples in the minority class, according to the users choice of Synthetic Minority Over-sampling Technique (SMOTE), Borderline-SMOTE (BSMOTE), Adaptive Synthetic (ADASYN), or Random Over-Sampling Examples (ROSE). META-BOA further displays both principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) visualization of data pre- and post-over-sampling. Random forest classification is utilized to compare sample classification in both the original and balanced datasets, enabling users to select the most appropriate method for their analyses. Availability and implementationMETA-BOA is available at https://complimet.ca/meta-boa. Supplementary InformationSupplementary material is available at Bioinformatics online.

bioinformatics↗

bioRxiv2020

Computational identification of human biological processes and protein sequence motifs putatively targeted by SARS-CoV-2 proteins using protein-protein interaction networks

While the COVID-19 pandemic is causing important loss of life, knowledge of the effects of the causative SARS-CoV-2 virus on human cells is currently limited. Investigating protein-protein interactions (PPIs) between viral and host proteins can provide a better understanding of the mechanisms exploited by the virus and enable the identification of potential drug targets. We therefore performed an in-depth computational analysis of the interactome of SARS-CoV-2 and human proteins in infected HEK293 cells published by Gordon et al. to reveal processes that are potentially affected by the virus and putative protein binding sites. Specifically, we performed a set of network-based functional and sequence motif enrichment analyses on SARS-CoV-2-interacting human proteins and on a PPI network generated by supplementing viral-host PPIs with known interactions. Using a novel implementation of our GoNet algorithm, we identified 329 Gene Ontology terms for which the SARS-CoV-2-interacting human proteins are significantly clustered in the network. Furthermore, we present a novel protein sequence motif discovery approach, LESMoN-Pro, that identified 9 amino acid motifs for which the associated proteins are clustered in the network. Together, these results provide insights into the processes and sequence motifs that are putatively implicated in SARS-CoV-2 infection and could lead to potential therapeutic targets.

bioinformatics↗