Use when analyzing raw 2D MS data (m/z vs. retention time maps) where conventional peak picking introduces unacceptable error rates, particularly in untargeted metabolomics or…
Use when you have a collection of MS/MS spectra (≥2 spectra) and wish to identify fragmentation signatures common to subsets of those spectra.
Use when after XCMS feature detection, grouping, retention time correction, regrouping, and missing value filling on LC-MS or GC-MS data, when you have an aligned feature table…
Use when you have a feature table from nontargeted LC-MS peak detection (containing m/z, retention time, and intensity values) and need to disambiguate whether detected features…
Use when you have loaded raw ATAC-seq fragment counts into a SummarizedExperiment object and are preparing to compute motif deviations.
Use when after drift correction and before imputation, when you have a MetaboSet object with LC-MS peak abundances and need to remove features with insufficient detection…
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 you have mass spectrometry data (chromatograms, spectra, mobilograms, or peak maps) in a Pandas DataFrame and need to generate the same visualization in multiple…
Use when you have loaded MSI data with an extracted peak list and need to annotate matrix-related signals, particularly when the dataset may contain isobaric ions or peaks with…
Use when after executing an MZmine batch processing workflow on raw metabolomics
Use when you have experimental RT measurements from a source chromatographic method and need to predict RTs for the same molecules on a target chromatographic method, but lack a…
Use when you have loaded raw MS intensity tables into QuantyFey and observe or suspect intensity drift artifacts across your measurement sequence.
Use when when XCMS or other DTW-based aligners have produced misaligned LC-MS feature groups across hundreds of samples or long acquisition periods (>1 week), particularly when…
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 raw MRM sample files from a LC-MS/MS instrument and need to systematically recover all precursor m/z and product m/z pairs for each transition.
Use when you have a mass spectrum of an unknown metabolite with a known or inferred precursor m/z, you have run a deep-learning semantic similarity model (e.
Use when you have a feature table from LC- or GC-HRMS data (either detected via pyOpenMS or imported as a custom feature list) containing m/z, retention time, and intensity…
Use when after constructing a background set for ORA in metabolomics: you have loaded an experimental detection list and a metabolomics pathway database, applied background-set…
Use when when ingesting or updating MassBank records in plain-text or structured format, and you need to verify that metadata fields (accession, name, formula, mass, spectrum…
Use when you have extracted retention times from MS1 spectra for top signals in a single LC-MS/MS run and need to evaluate whether that gradient's separation performance is…
Use when when you have a set of molecular structures (N-Me derived unsaturated sterol lipids or structurally similar organic molecules with C=C bonds) represented as SMILES or…
Use when you have a connected subnetwork of feature ions that have been validated as belonging to the same empirical compound (khipu instance), with isotope and adduct edges…
Use when after acquiring a PRM experiment on a Thermo Fisher Orbitrap instrument when you need to verify that the mass spectrometer's data acquisition controller executed the…
Use when you have a mass spectral library in MSP format (e.g., from NIST, SWGDRUG, or other sources) exported alongside a folder of MOL files, and you need to populate the SMILES…
Use when you have a metabolomics dataset (LC/MS or GC/MS) with missing values and need to determine which are below the limit of detection (LOD) or limit of quantification (LOQ).
Use when when you have completed a PALS pathway analysis on a clean metabolomics peak intensity matrix and pathway annotation set, and you need to verify that the ranked pathway…
Use when after running annotateRC on LC–MS AIF data when you need to inspect whether a feature has multiple plausible annotations (e.g., isobaric lipids, isomers with the same…
Use when after ASE-ANI has filtered conformers to remove high-energy geometries, and you need to compute electronic properties required for CCS prediction.
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 a collection of molecular structures (as SMILES or SDF files) and need to generate pre-computed CCS values for fast retrieval in downstream mass spectrometry…
Use when you have NMR metabolomics measurements paired with pre-analytical metadata (processing delay times, centrifugation timing, sample type such as plasma vs.
Use when you have spatial metabolomics data with semi-colon-delimited multi-isomer annotations (e.g., 'all_IsomerNames' column in SpaMTP Seurat objects) and you want to quantify…
Use when you have raw or preprocessed MS imaging data archived as an RDS file or from a Zenodo deposit that includes the full m/z feature set (e.g., 10,200 m/z values spanning…
Use when after installing R or modifying an R environment via conda, package managers, or container images; before running any pipeline step that depends on R packages for…
Use when when executing peak integration on preprocessed GC-IMS data (after alignment and baseline correction) and you need to decide whether to include or exclude peaks that…
Use when when you have a .msp mass spectra file with incomplete or missing chemical metadata fields (SMILES, InChI, CAS number, formula, InChIKey, IUPAC name) and need to populate…
Use when you have generated gene-level count matrices via two methodologically distinct routes—e.
Use when you need to verify claims about algorithm performance, data processing correctness, or workflow outcomes in a scientific article or software repository.
Use when when preparing to read Thermo Fisher Scientific .raw files using rawrr functions (readFileHeader, readSpectrum, readChromatogram, readIndex), or when retrieving cached…
Use when you have corrected ATAC-seq footprint scores (from ATACorrect and ScoreBigwig) at open chromatin regions and a motif database (e.g., JASPAR PWMs), and you need to…
Use when after completing peak picking, sample alignment, and before final MS2 spectrum extraction, when you have identified individual ion peaks across samples and need to link…
Use when you have Thermo Fisher Scientific .raw files from an LC-MS experiment and need to extract spectral features (base-peak m/z, intensity, scan-level properties) indexed by…
Use when when processing mass spectrometry imaging data with multiple adduct forms of the same lipid species, and you need to correct one adduct form (e.g.
Use when you have observed compounds (from LC-MS/MS, GC-MS, NMR, or other analytical techniques) with unknown identity and you want to assign candidate metabolite structures by…
Use when when you have separate LC-MS peak tables for unlabeled (C12) and labeled (C13) isotope tracer experiments and need to identify which features correspond to the same…
Use when you have trained a new machine learning model for chemical formula or adduct assignment from MS/MS spectra and need to assess whether it offers genuine performance gains…
Use when after applying any sequence of spectrum preprocessing operations (set_mz_range, remove_precursor_peak, filter_intensity, scale_intensity) to an MsmsSpectrum object, to…
Use when when you have matched transcriptomics (RNA-seq read counts), intracellular metabolomics (LC-MS abundance data), and extracellular flux measurements (YSI bioanalyzer or…
Use when after marker identification or metabolite annotation has produced a curated list of compound IDs (e.g., KEGG IDs or CAS numbers) and you need to determine which metabolic…
Use when when you have a set of chemical structures (SMILES strings or SDF files) that need to be processed for training a graph neural network model on molecular property…
Use when after generating theoretical spin multiplets for individual metabolites via first-order or density-matrix NMR simulation, but before combining spectra or applying Fourier…
Use when after feature detection and alignment (XCMS or equivalent), when you have a CSV feature table with m/z and retention time columns and need to group features derived from…
Use when when preparing a software release, testing contribution workflows, or auditing package availability: verify that matchms can be installed and imported successfully from…
Use when you have paired-end Hi-C FASTQ files from a public repository (NCBI SRA, GEO, or ENCODE-deposited) and need to produce standardized .hic binary contact maps that conform…
Use when after implementing or modifying a cross-language integration layer that wraps Python mass spectrometry functions (e.g., spectral matching, peak detection, normalization…
Use when analyzing complex GC-MS mixtures where two or more chemical compounds elute at similar or identical retention times, producing overlapping or merged peaks in the raw…
Use when you have 1D NMR spectra (¹H or ¹³C or both) for an unknown organic compound with ≤19 heavy atoms and need to rapidly predict its molecular formula and connectivity graph…
Use when when evaluating whether an MS data processing platform (such as mzmine) supports the full range of separation/ionization techniques your laboratory uses, or when…
Use when after training multiple MLPNN models (via cross-validation) on paired microbiome and metabolome data when you need to extract interpretable feature importance scores from…
Use when you have a vendor mass spectrometry raw file (e.g., .raw format) that requires conversion to Aird format using AirdPro CLI, and you are running on macOS or Linux.