Use when you have chemical entity records scattered across two or more public repositories (e.g., HMDB, ChEMBL, PubChem, KEGG) and need a single authoritative, deduplicated…
Use when after applying frequency domain calibration (Ledford, linear, or quadratic equation) to a raw FT-ICR mass spectrum, validate the calibration quality by measuring residual…
Use when after nontargeted peak detection has identified candidate peaks in LC-MS chromatograms, when you need to establish exact peak start/end retention times and extract…
Use when you have completed a ViMMS simulation run or processed real LC-MS/MS data and need to quantitatively assess whether one DDA controller (e.g., WeightedDEWController with…
Use when when you have a GTF genome annotation and need to catalog all transcript isoforms and local alternative splicing event variants (exon skipping, intron retention,…
Use when after peak detection and clustering have been completed on aligned and baseline-corrected GC-IMS data, and before imputation or statistical analysis.
Use when you have raw or processed mass spectrometry data in HDF5 (.h5) or mzML format and need to ingest it into DEIMoS for multi-dimensional analysis.
Use when you have a normalized peak-abundance matrix from FT-ICR MS data (peaks as rows, samples as columns) and need to compare the number and diversity of detected molecular…
Use when you have QCpool (pooled quality control) samples measured at regular intervals across one or more LC-MS/MS sequences and need to detect whether instrument performance…
Use when when setting up a multi-stage bioinformatics workflow (e.g., ENPKG) that calls external tools (MZmine, Sirius, SPARQL engines) and depends on specific Python libraries;…
Use when you have experimental MS/MS spectra and need to assign definitive molecular identities by matching against a curated spectral library.
Use when you have loaded raw mass spectrometry data (mzML, mzXML, or CDF format) into AutoTuner and need to identify peak regions in the TIC trace prior to extracted ion…
Use when after merging separate vocabularies for distinct data modalities (e.g., spectral tokens for m/z values and intensities, structural tokens for SMILES or graphs) and before…
Use when when you have statistically significant features from multi-assay LC-MS metabolomics data (with m/z and retention time annotations) and need to group features that may…
Use when you have acquired raw SIMS (secondary ion mass spectrometry) metabolite images aligned with tissue regions and segmented single-cell masks, and you need to extract…
Use when after drift correction and quality flagging, when you have a feature abundance matrix with associated metadata (Feature_ID, m/z, retention time) and need to identify…
Use when you have raw tandem mass spectra data (mz/intensity pairs and precursor m/z values) and need to train interpretable machine learning models (regression or tree-based)…
Use when you have raw LC-MS data in mzML or equivalent binary format from a public repository (MetaboLights, MassIVE) or instrument vendor output, and need to ingest it into…
Use when you have extracted fragmentation patterns from a collection of MS/MS spectra (using mineMS2) and have partitioned spectra into components via GNPS molecular networking…
Use when after frequency-based denoising and sample-level aggregation of replicate MS/MS spectra, when you need to export denoised spectra to a standardized, vendor-independent…
Use when you have a heterogeneous feature matrix combining molecular descriptors (from RDKit/mordred) and chromatographic metadata (column length, temperature, pH, flow rate,…
Use when after computing PLAGE-derived activity scores for pathways or metabolite sets (Molecular Families, Mass2Motifs) from log2-standardized metabolomics intensity data.
Use when you have implemented a new RDKit-based ComputeConverter for SMILES↔InChI conversions and need to verify that the conversion methods preserve molecular structure integrity…
Use when when you have an unknown mass spectrum (query spectrum) and need to search it against a reference database of billions of spectra to find matching or structurally related…
Use when you need to confirm that omitting an optional input parameter (such as secondaryAssay in buildExperiment) produces the expected mathematical result—specifically, when a…
Use when after drift correction has been applied to your LC-MS peak table and you need to identify low-quality metabolic features that exhibit high internal spread (RSD, RSD*) or…
Use when after extending ChIP sample reads to their predicted fragment length and constructing local lambda bias tracks (incorporating d-scaled, 1 kb, 10 kb, and genome-wide…
Use when when you have GC-MS output with Match.Factor values or structural similarity scores from categorate() and need to decide which identified compounds are reliable enough to…
Use when when you have preprocessed MS/MS spectral data (normalized peak intensities and m/z values) and need to convert each spectrum into a learned molecular embedding — from…
Use when you have centroided MS2 spectra (in mzML format from data-dependent acquisition) and a list of known or suspect PFAS diagnostic fragment masses, and you need to…
Use when after loading centroided .mzML LC-MS data and defining a target list (compound ID, name, m/z, RT, polarity) when you need to: (1) automatically locate and integrate peaks…
Use when when you have CE-MS data with migration times that vary between runs due to electroosmotic flow drift, but you possess a reliable internal standard with a known effective…
Use when you have trained MLPNN models on paired microbiome-metabolome data (via 10-fold cross-validation repeated across multiple iterations) and need to derive interpretable…
Use when you have computed RAS (Reaction Activity Scores) from transcriptomics and GPR rules, RPS (Reaction Propensity Scores) from intracellular metabolomics via mass-action…
Use when when preparing to execute the Nextflow4MS-DIAL workflow on raw LC-HRMS metabolomics data (.mzML or .
Use when after RAMClustR clustering and do.findmain molecular weight inference have been completed on XCMS-detected metabolomics features.
Use when you have loaded raw MSI spectral data (imzML format) in profile or centroid mode and need to remove background noise and baseline artifacts before intensity normalization…
Use when when comparing transcript quantification outputs (NumReads counts, abundance estimates) from two mapper implementations (e.
Use when you have paired scATAC-seq and scRNA-seq data from the same cells (multiome experiment) and want to perform integrated analysis that leverages both chromatin…
Use when when you have a target compound (modified or unmodified) and need to obtain its experimental MS/MS spectrum and metadata to serve as a known reference for ModiFinder…
Use when xCMS grouping has been performed on LC-MS data from studies with hundreds of samples or data acquisition periods longer than a week, where retention time drift structures…
Use when you have raw, high-resolution MS/MS spectra in mzML, mzXML, or MGF format that need to be prepared for fast similarity searching or clustering.
Use when when you have loaded a chemical database (e.g., HMDB pickle file) and need to understand how many distinct molecular formulas remain after filtering for a specific m/z…
Use when your metabolomics dataset contains missing values below a known detection limit (left-censored MNAR data), and you need to recover these values while respecting the…
Use when after training a NeatMS CNN model on labeled MS1 peaks and generating predictions on a held-out test set, compute ROC-AUC to assess whether the model achieves the target…
Use when you have obtained a raw reference library file (such as the DTCCS_N2 library for U13C labeled lipids) and need to validate its structure, verify that all expected lipid…
Use when you have obtained or need to prepare a DTCCS_N2 reference library for U13C-labeled lipids (typically provided as part of a lipidomics tool distribution) and need to…
Use when when working with large single-cell ATAC-seq or multi-omics datasets where in-memory storage is infeasible (>1M cells), and you need to iteratively add or modify count…
Use when you have multiple independent implementations of the same data format reader (e.g., Rust, Python, R versions) and need to verify they produce identical or equivalent…
Use when after collecting observed separation efficiency scores at sampled gradient conditions and you need to propose the next gradient to evaluate.
Use when your raw TOF-MS data (Agilent MassHunter .d format) exhibits jagged, artifact-prone peaks in low-abundance ions that compromise peak quality assessment or when you need…
Use when you have loaded raw MS intensity tables into QuantyFey and observe or suspect intensity drift artifacts across your measurement sequence.
Use when you have a collection of chemical structures (SMILES, InChI, SDF, or mol formats) and need to train or apply a machine learning model for retention time prediction or…
Use when when you have centroided .mzML LC–MS runs and a target list (compound ID, theoretical m/z, expected RT, polarity) but are uncertain whether your m/z and RT windows are…
Use when you have two independent LC-MS untargeted metabolomic feature datasets (each with retention time and m/z values) and need to identify which features in one dataset…
Use when you have a query mass spectrum (or representative metabolite spectrum from public data) and need to identify it by searching against large spectral reference databases…
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 a resolved mzML or mzXML spectrum file and need to visualize or analyze the temporal intensity profile of a specific analyte (defined by its m/z value).
Use when when searching an unknown MS/MS spectrum against a spectral library and you need to rapidly identify the most relevant candidate library spectra before applying cascade…
Use when you need to compile and test a .NET-based metabolomics or bioinformatics