Use when you have raw LC- or GC-HRMS data from vendor instruments (ESI or APCI ionization) that needs to be converted to a vendor-neutral format for non-target screening, or you…
Use when after batch correction has been applied to metabolomics data using pooled SQC samples, and you need to retrieve the corrected ratios (compound / internal standard) for…
Use when you have generated a peak table or feature list from MZmine, XCMS, MS-DIAL, or Compound Discoverer in its native export format and need to ingest it into LipidMa — from…
Use when after running salmon quant with the --writeMappings/-z flag to produce SAM output, or when investigating discrepancies between the number of mapped reads reported in…
Use when processing a mass spectrometry dataset (in FragHub JSON format or similar) where duplicate spectral records are suspected or known to exist.
Use when after extracting raw MS/MS spectra from mzML files when you observe high fragment counts per spectrum (e.g., 98 fragments) and want to reduce noise from instrument…
Use when you need to validate that Docker image builds for multiple deployment variants (e.g., cli, dev, linux, windows) meet documented compressed size ranges, or when you must…
Use when after applying cluster-based filtering with quasi-molecular adduct constraints and frequency thresholds on LC-MS feature candidates, when some peaks have been entirely…
Use when when processing MGF-format MS2 spectral libraries (e.g., GNPS) that contain SMILES but lack the Molecular Formula field, and you need to prepare the library for MS-DIAL…
Use when you need to verify that a Python package (or similar installable software) passes its declared integration test suite as a prerequisite to trusting its reliability in…
Use when when you need to inventory a collection of related web applications or tools distributed across multiple repositories, discover their live deployment URLs, trace their…
Use when after loading fragment counts into a SummarizedExperiment object (e.g., via getCounts) but before motif matching or deviation computation.
Use when you have executed batch searches across two or more domain-specific MASST tools and obtained separate output files (_microbe.json, _plant.json, _tissue.
Use when processing spectral datasets from open mass spectra libraries (OMSLs) where structural identifiers and ionization metadata are incomplete or inconsistent.
Use when when you have tandem mass spectra from ribosomally synthesized peptides (RiPPs) and suspect the presence of unknown or non-standard post-translational modifications that…
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 when you have a tandem mass spectrometry spectrum with a known or inferred peptide sequence that may contain post-translational modifications (phosphorylation,…
Use when you have raw Hi-C FASTQ files from a high-throughput chromatin conformation capture experiment and need to generate a normalized contact matrix (.hic file) for downstream…
Use when after applying a quantitative analysis function (e.g., cooltools.insulation, contact frequency calculations) to Hi-C cooler files or other genomic datasets, validate that…
Use when after running Enrichment() on a configured EnrichParam object (via KEGG_Enrich_PlotPanel or similar), when you have a full enrichment result table and need to reduce it…
Use when deploying Mass2SMILES on a TensorFlow-CPU build and you need to optimize inference throughput on multi-core systems.
Use when when you have abundance-normalized FT-ICR MS peak data with assigned molecular formulas and need to distinguish between richness (total number of distinct metabolites)…
Use when you are building or extending a multi-module Python library for scientific computation (e.
Use when you have raw MS/MS peak lists and suspect electronic noise contamination—particularly
Use when when applying a pre-trained Word2Vec model to mass spectra at inference time (e.g., library matching or molecular networking), especially when the query spectra may…
Use when when you have an untargeted metabolomics dataset with MS2 fragmentation
Use when after applying the CV_ratio() filtering function to a normalized metabolomic feature matrix (e.g., Urine_RP_NEG_norm.txt) in margheRita, generate retention statistics to…
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 you have a preprocessed feature table from non-targeted LC-MS/MS metabolomics data (after data merging, cleanup, blank removal, and batch correction) and need to test…
Use when when you have extracted intermediate JSON conforming to the Experiment Description Specification and need to restructure, filter, sort, or aggregate records (e.
Use when you are designing a new tool for FT-ICR MS analysis (or similar high-resolution mass spectrometry domain) and need to understand which analytical and visualization…
Use when you have high-throughput replicate measurements (e.g., mass spectrometry metabolomics) on biological replicates and need to identify which sample pairs exhibit…
Use when when working with raw 1H NMR FID data acquired on instruments like Bruker Avance spectrometers that require baseline correction, phase adjustment, and signal alignment…
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 after preprocessing and filtering mass spectra (peak filtering, metadata cleaning) when you need to compare all spectrum pairs within a dataset or between a query set and…
Use when when you have an untargeted metabolomics dataset from HPLC–MS (e.g., mzML, NetCDF) with detected peaks of unknown identity, and you need to disambiguate or validate…
Use when when beginning mass alignment in a multi-sample LC-MS metabolomics study, before constructing the MassGrid.
Use when you have detected multiple LC-MS features (m/z peaks) across a chromatogram and need to distinguish true chemical relationships (isotopes differing by 1.003 Da, adducts…
Use when after structural cluster assignment and correlation clustering are complete, and you need to represent the full set of structural relationships (isotopologues, adducts,…
Use when you have a trained predictor (like BitterPredict) that accepts structured descriptors and want to understand feature importance without retraining.
Use when after marker identification or feature selection has produced a list of discriminatory m/z features, and before pathway enrichment analysis (e.g., KEGG).
Use when when you have IM-MS lipidomics data spiked with U13C labeled lipid internal standards (e.g., fully labeled yeast extract) and want to assess whether measured CCS values…
Use when setting up a LipidMatch analysis run and you need to select among three mutually-exclusive analysis modes (PFAS, Lipid, or Tween-positive detection).
Use when you have extracted MS/MS spectra for a given metabolomic feature across multiple replicates (e.g., after top-TIC filtering) and need to identify which fragments are…
Use when when you have raw tandem mass spectrometry peak data (m/z and intensity pairs), precursor m/z, charge state, and adduct annotation for one or more compounds, and need to…
Use when when you have per-feature quality metrics (such as CV values from NMR or MS reproducibility analysis) and need to: (1) confirm that a specified proportion of features…
Use when you have raw MS/MS spectra with variable numbers of peaks at continuous m/z values and need to feed them to a neural network (e.g., Siamese network for similarity…
Use when when preparing to run ORA on a metabolomics study: you have a list of detected metabolites from your experiment and need to determine which metabolites from the full…
Use when when you have source code access to a webservice component (such as the MAGMa joblauncher) and need to enumerate all exposed HTTP endpoints, their methods (GET, POST,…
Use when after generating candidate transformed structures from biotransformation rules and when you have MS/MS spectral feature data that you wish to organize into putative…
Use when you have raw LC-MS or GC-MS data files from a mass spectrometer (in mzML, NetCDF, or mzXML format) and need to detect chromatographic peaks, correct m/z bias via mass…
Use when you have millions of MS/MS spectra in mzML, mzXML, or MGF format and need to identify similar spectra for clustering, but exhaustive pairwise cosine-similarity…
Use when you have GC-MS data preprocessed into a structured spread format and need to confirm that a set of known or suspected compounds are correctly identified in your samples.
Use when you have preprocessed metabolite intensity data (log2-transformed, zero-mean unit-variance standardized) mapped to compound annotations, and you need to derive activity…
Use when you have defined a Keras model architecture (convolutional and dense layers) accepting raw mass spectrometry imaging data tensors and need to prepare it for training on…
Use when when you have downloaded a curated structure-organism dataset (such as LOTUS) in TSV or CSV format with separate 2D and 3D structure-organism pair tables, and ne — from…
Use when you have centroided LC-MS/MS spectra (in MGF, mzXML, mzML, or mzData format) and genomically-predicted precursor peptide sequences, and you need to identify whic — from…
Use when you have NMR dataset metadata (cache-file flag, total point count, block layout configuration) and need to select an appropriate storage backend that balances memory…
Use when you have a Docker image published to a registry (e.g., docker://stravsm/msnovelist6),
Use when you have isolated reference peaks from training chromatograms (ground-truth, single compounds per sample) and need to create a diverse, labelled training set large enough…