Use when when importing a tab-delimited or Sciex OS text export metabolomics dataset into mzQuality, before building the SummarizedExperiment object.
Use when your metabolomics experiment includes calibration line samples with known concentrations for spiked compounds, and you have computed batch-corrected…
Use when you have run a metabolomics experiment with incomplete coverage of a reference pathway database (e.g., 10–100% of database metabolites detected), and you plan to use ORA…
Use when you have a genome annotation GTF file and need to identify all transcript-level alternative splicing events (exon skipping, intron retention, alternative splice sites,…
Use when after computing per-bin coverage depth using cooltools.coverage() on a loaded cooler object, when you need to (1) share the coverage track with non-Python tools, (2)…
Use when investigating how a specific pull request (e.g., PR #72 introducing MS2Query''s two-branch workflow split) modified the codebase architecture, control flow, or data…
Use when you have ion-mobility mass spectrometry metabolomics data with putative metabolite identifications (e.g., from database matching) and want to reduce false positives by…
Use when when comparing GNPS chemical annotations across two or more groups of samples (defined by ReDU sample-information categories such as sample type, extraction method, or…
Use when when designing or optimizing S4-based data backends (such as MsBackend subclasses) and you need to decide whether to pre-populate all slots with complete data structures…
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 a mzPeak file (uncompressed ZIP archive containing Parquet tables) and need to access decoded spectral data arrays (m/z, intensity), spectrum metadata (scan…
Use when after loading MS-Dial feature tables (e.g., Urine_RP_NEG_norm.txt or Urine_RP_POS_norm.txt) and before sample-level filtering or imputation, whenever the feature…
Use when after peak detection when you have a table of detected peaks with m/z values and retention times from LC/HRMS data, and you observe systematic m/z drift across a — from…
Use when you have LC-MS normalized intracellular metabolite abundance data from multiple cell lines (or samples) and need to compute reaction-level propensity scores that account…
Use when when you have experimental MS/MS data (peak lists, precursor m/z, charge state, adduct type) paired with a chemical structure (SMILES or structural identifier), and need…
Use when when processing a batch of LC-MS samples in mzML or mzXML format where at least one file has been designated as a quality control (QC) file, extract its TIC or BPC before…
Use when you have a GNPS mass spectral molecular network and wish to annotate its nodes with both chemical class assignments (from GNPS public library matches) and MS2LDA-derived…
Use when after loading and preprocessing a Cardinal MSImagingExperiment object (with normalized peaks and optional spatial segmentation results), and before conducting spatial…
Use when you have a set of chemical compounds (with known retention times and intensities) loaded into a ViMMS IndependentMassSpectrometer and need to simulate a specific MS/MS…
Use when you have raw mass-spectrometry files (MGF, BIOM, mzXML, mzML) or feature abundance tables from external tools (MZmine2, peak detection software) and need to convert them…
Use when after running macs3 callpeak with the -f BEDPE flag on paired-end ChIP-Seq data (e.g., CTCF_PE_ChIP_chr22_50k.bedpe.
Use when after database search algorithms have scored unknown MS samples against reference species, and you need to visually inspect and confirm species assignments or identify…
Use when when you have a trained molecular classifier (like BitterPredict) that accepts structured descriptor input, and you need to understand which chemical descriptor subgroups…
Use when you have aligned ChIP-Seq reads (in BED or BEDPE format) and need to convert them into quantitative genome-wide signal tracks (coverage, p-value, or q-value scores) for…
Use when after submitting a POST request to the /api/smart3/search endpoint with peak data as a JSON payload, you receive an HTTP response and need to extract classification…
Use when you have raw tabular experimental data (CSV or Excel) with column headers annotated using MESSES tag syntax (#<table_name>.id for record identifiers, #.
Use when you have discovered Mass2Motifs via LDA and need to (1) load a pre-computed motifset JSON file (e.g., motifset_optimized.
Use when you have generated PSI matrices for alternative splicing events or transcripts across two or more biological conditions using SUPPA's psiPerEvent or psiPerIsoform…
Use when you have generated or assembled a lipid spectral library with precursor m/z values, adduct information, and fragmentation patterns, and you need to import those spectra…
Use when you have a published predictive model with known coefficients and feature requirements (e.g., MetaboAge from a peer-reviewed study), a target R package with an…
Use when when you have coordinate-sorted BAM files from single-cell ATAC-seq experiments (e.g., 10X Genomics scATAC-seq) and need to generate a compressed fragment file for…
Use when after drift correction in non-targeted LC-MS metabolomics workflows, when you need to decide which molecular features are sufficiently reproducible (low instrument/QC…
Use when when you need to verify that a specific data transformation (e.g., precursor m/z zeroing, feature scaling, or field masking) is applied consistently across multiple…
Use when you have completed Hi-C map generation (producing .hic files from aligned reads) and need to detect and annotate topological features such as chromatin loops,…
Use when when you have obtained a raw Orbitrap mass spectrometry file and need to verify that the instrument was configured as claimed in the methods section or dataset…
Use when you have preprocessed mass spectra (peak-filtered, metadata-cleaned) in supported formats (mzML, mzXML, msp, MGF, JSON) and need to compare all-pairs or many-to-many…
Use when you have raw mzML files and feature tables (CSV format from mzMine or XCMS) from untargeted LCMS experiments and need to distinguish true metabolite peaks from false…
Use when you have raw MS intensity data paired with known-concentration calibration standard measurements, and you need to convert intensities to absolute or relative…
Use when you have configured a GitHub Actions workflow that executes build, test, and quality checks, and you want to embed a machine-readable, auto-updating badge in your…
Use when when training a fresh CNN model from scratch on labeled LCMS data (e.g., MS1 peak classification in NeatMS), particularly when aiming for a specific performance target…
Use when after componentization of parent and TP features with generateComponents(algorithm='tp'),
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 you have a pretrained RT-Transformer model checkpoint from a large, well-characterized chromatographic dataset (e.g., SMRT) and need to predict retention times for a…
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 after computing activity scores for a collection of metabolite sets (pathways, GNPS Molecular Families, or MS2LDA Mass2Motifs) from intensity and annotation data.
Use when when you have two or more implementations of a spectral search tool (e.g., MASST vs. MASST+) and need to quantify whether claimed performance improvements (e.g.,…
Use when when preparing to read Thermo Fisher Scientific .raw files using rawrr functions (readFileHeader, readSpectrum, readChromatogram, readIndex), or when retrieving cached…
Use when when you have raw MS/MS spectra in MGF or mzML/mzXML formats and need to feed them into Casanovo or similar transformer-based de novo sequencing models.
Use when after feature extraction (Asari) has produced a full feature table from mzML data, but before normalization and annotation.
Use when starting from raw LC-MS spectral files (mzML or mzXML format) in a global metabolomics study and you need to produce a complete, validated feature table with m/z,…
Use when you have centroided MS2 spectra from data-dependent LC- or GC-HRMS measurements and need to rapidly prioritize potential PFAS features within a larger feature set.
Use when when you have extracted mass tracks (EICs) from individual LC-MS samples and need to establish reliable landmarks for subsequent pairwise or global alignment across a…
Use when when exporting quantified ion images and pixel metadata from LipidQMap to HDF5 format for use in downstream Cardinal or other MSI analysis workflows.
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 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 trained a multitask NMR-to-structure model and need to quantify its predictive accuracy on held-out test molecules.
Use when before running any R function that wraps compiled C# methods or system executables (e.g., rawrr::readSpectrum), especially when the package depends on language runtimes…
Use when you have completed XCMS grouping on LC-MS data and suspect misaligned features due to long acquisition periods (>1 week) or large sample cohorts (hundreds of samples).
Use when you have raw Hi-C FASTQ files from a public repository (NCBI SRA, GEO, or ENCODE-deposited accession) and need to reproduce or validate Hi-C map generation following the…
Use when when you have IM-MS lipidomics data with measured CCS values from samples spiked with U13C labeled internal standards, and you need to assess systematic CCS bias or…