Use when you have tandem mass spectra data and need to predict a discrete molecular property (e.g., presence/absence of a sulfo group) while maintaining full interpretability of…
Use when you have multiple replicate MS/MS spectra for the same metabolic feature (e.g., 66 top-TIC spectra for feature 1982) and need to identify robust peaks by merging nearby…
Use when you have extracted peaks from multiple LC/HRMS batches (n > 500 samples across different analytical runs or days) and observe systematic retention time drift or offset…
Use when you have centroid-mode LC-MS AIF chromatograms processed through xcms and RAMClustR, a feature table with target m/z and retention time values, and access to fragment…
Use when you have untargeted metabolomics mass spectrometry data (MS2 spectra with m/z values and intensities) and an existing knowledge-driven metabolite network, and you need to…
Use when designing or validating a metabolomics pathway analysis experiment, especially when you have uncertainty about how many metabolites your detection platform will reliably…
Use when after applying CordBat batch correction to a log2-transformed metabolite matrix from multi-batch metabolomics data, you want to quantitatively and visually assess whether…
Use when you have tab-delimited metabolomics data with columns for aliquot identifiers, compound names, peak areas (primary and internal standard), sample type (QC, study sample,…
Use when after an end-to-end neural model (CNN + transformer) has generated predicted molecular structures (formula and connectivity) from 1D NMR spectra.
Use when after drift correction of LC-MS peak intensity data, when you need to identify metabolic features with excessive internal spread (within-group variability in QC samples)…
Use when you have genomic clusters (GCFs) and metabolomic features (MFs) from paired microbial datasets, each with strain membership information, and you need to score potential…
Use when apply Leiden clustering after constructing a k-nearest neighbor (kNN) graph from single-cell expression data when you need to partition cells into discrete, biologically…
Use when when you have raw mzML or mzXML mass spectrometry files with uncompressed numeric arrays (not pre-compressed with zlib or msnumpress) and need to reduce file size for…
Use when after importing raw LC-MS/MS data files into the SIRIUS Java framework, before constructing indexed spectrum objects or submitting data to CSI:FingerID, CANOPUS, or…
Use when when you have experimental MS/MS spectra and want to discover structurally similar compounds beyond exact spectral library matches—particularly useful for identifying…
Use when your metadata table contains compound names but lacks structure information (SMILES, InChI, molecular formula, or PubChem CID).
Use when after elution peaks have been detected on composite mass tracks using local maxima and prominence thresholds, and before mapping detected features back to indivi — from…
Use when when a user uploads a JSON project document to the Pairing Omics Data Platform and you need to determine whether it satisfies the platform's data structure requi — from…
Use when you have LC-MS/MS spectra (MGF, mzXML, mzML, or mzData format) and either raw genome nucleotide sequences or antiSMASH/BOA genome mining tool output, and you need to…
Use when when you have manually labeled LC-MS peaks as 'High quality' or 'Low quality' using NeatMS's annotation tool and need to create training/validation/test batches.
Use when when you have deposited a collection of JSON project documents in a platform or repository and need to verify that all conform to a published JSON Schema specifi — from…
Use when when performing targeted quantification of known compounds in LC-MS data using TARDIS, especially when the instrument acquired data with multiple overlapping m/z scan…
Use when after performing an ANOVA-style multi-group de_design() analysis on a LipidomicsExperiment object, when you need to determine whether a categorical sample variable (e.g.,…
Use when you are converting a processed Cardinal MSImagingExperiment object (containing normalized peaks, optional spatial shrunken centroids segmentation, and feature m/z…
Use when when processing a metabolomics feature table through multiple sequential transformations (e.g., imputation, normalization, batch correction, annotation) and you need to…
Use when you have a two-dimensional MS map (m/z vs retention time) from GC–MS or LC–MS data and need to discriminate analytes and identify marker features without false positives…
Use when after drift correction and before imputation when you have LC-MS data with designated QC samples and you need to remove features with poor reproducibility across QC…
Use when you have a trained baseline GNN model with established hyperparameters (dropout rate, learning rate, epochs, optimizer settings) and want to evaluate whether alternative…
Use when you have newly assayed 1H-NMR metabolomics data from Nightingale Health (CSV or TSV format) and need to apply one or more published metabolic risk scores (Deelen et al.
Use when when you have implemented or modified a data ingestion module (e.g., mzML parser) and need to verify that file deserialization produces correct internal representations.
Use when when developing or validating a DNA methylation array analysis pipeline using ChAMP, you need an independent ground-truth dataset to confirm that DMR detection is working…
Use when you have multiple MSP (mass spectrum) library files to read and merge in R, and your computational task is time-consuming (e.g., structure extraction, SMILES assignment,…
Use when you have transcript-level abundance estimates and inferred counts from salmon/kallisto/Sailfish (with or without Gibbs/bootstrap replicates) that must be summarized to…
Use when you have MS/MS spectra in .msp format and need to retrieve similar compounds or compute spectral similarities for compound identification.
Use when when you need to enable non-programmers or domain experts to formulate complex, unambiguous queries over specialized data (e.
Use when when you have an observed MS/MS spectrum and need to annotate fragment peaks against a known modified peptide sequence.
Use when after importing MSI data as an msimat object and having a list of detected peak masses, but before annotating which mass differences correspond to biologically plausible…
Use when you have raw mass spectrometry outputs (peak areas/heights across samples and fragmentation spectra) that need to be formatted and validated before running the tima…
Use when processing raw GC-MS data in NetCDF format where peaks have been detected but lack standardized retention indices.
Use when after retention-time and m/z-based peak alignment has been completed across a cohort of LC-MS samples, and you need to create a unified quantitative matrix for…
Use when you have raw mass spectra or processed feature matrices from liquid chromatography–mass spectrometry (LC-MS) or direct infusion MS that must be ingested by a deep…
Use when a deep learning model for molecular structure prediction (e.g., NMR2Struct) has been trained and evaluated on a limited molecular size range (e.
Use when you have developed or adapted a peak detection method for chromatography–mass
Use when you have aligned MS/MS feature tables (e.g., from MSDial ver. 4.80) representing unknown metabolites suspected to be Phase I/II transformation products of xenobiotics,…
Use when you have GC–MS or LC–MS data represented as a two-dimensional map (m/z vs retention time) and need to identify analyte signals and marker features while minimizing false…
Use when you are building a visualization library that must support multiple plot kinds (chromatogram, spectrum, mobilogram, peakmap) across multiple rendering backends…
Use when evaluating whether a published computational method can be independently executed: (1) source code is claimed to be available but repository structure, build…
Use when your metabolomics dataset (LC/MS or GC/MS) contains missing values encoded as NA or zero that represent compounds below the instrument's limit of detection (LOD) or limit…
Use when you have peak-picked features with m/z, drift_time, retention_time, and intensity columns, and you need to identify monoisotopic peaks and their charge-state-specific…
Use when you have LC-MS/MS data acquired in DDA mode from untargeted metabolomics experiments and need to remove chimeric (co-fragmented) MS/MS spectra that result from multiple…
Use when you have molecular structures (SMILES or graph representations) and need to predict or analyze infrared spectral properties using message passing neural networks.
Use when you have raw mass spectrometry instrument output (mzML, vendor binary formats, or mzPeak archives) and need to load spectrum metadata, chromatogram data, or signal arrays…
Use when when you have a collection of N-Me derivatized unsaturated sterol structures from tissue samples or standards that must be fed into MS/MS fragmentation prediction or…
Use when when you have raw mass spectrometry imaging data (full m/z profiles with intensity arrays) and want to classify spatial regions (e.
Use when when you have deconvolved GC-MS spectra (post-deconvolution output compatible with GNPS_GC input specification) and need to group them by chemical similarity to construct…
Use when immediately after loading a raw GC-MS CSV file and before executing the spreadOut() function. Use it when you have received peak table data from an instrument vendor (e.
Use when after forking and cloning a repository (e.g., scverse/scanpy) to verify that the development environment is correctly configured, or after implementing a feature or…
Use when when preparing to run Over-representation Analysis (ORA) on metabolomics pathway data, after you have loaded both a metabolomics pathway database (e.g., KEGG, MetExplore)…
Use when you need to determine the complete set of validated instrument/vendor and acquisition mode combinations for a mass spectrometry analysis tool, when assessing whether your…
Use when after you have identified a set of differentially methylated bases or regions (e.