Use when you need to obtain source code or computational workflows from a published repository, particularly when the article explicitly provides a GitHub URL and documents that…
Use when you have unaligned MS2 spectra from one or more samples (in formats like .mgf, .mzML, or .mzXML) and need to compare them in a retention-time-agnostic manner.
Use when before launching the DaDIA metabolomics pipeline or any multi-package workflow, when you have an R environment with potentially mixed or unknown package versions and need…
Use when when you have a spatial metabolomics or LC-MS dataset with detected m/z features (as a feature matrix or SpaMTP Seurat object) and need to assign metabolite identities.
Use when you have molecular identifiers (SMILES strings or molecular structure files) that need to be converted into node-edge graph tensors for input to message passing neural…
Use when you have retrieved and deduplicated chemical formulae and metadata from multiple heterogeneous sources (HMDB, ChEMBL, PubChem) and extracted both structural relationships…
Use when you have a set of metabolites or chemical formulas to analyze and want to evaluate how different MS/MS fragmentation strategies (e.g., TopN, exclusion lists, dynamic…
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 when you have centroid mzML files from LC-MS metabolomics acquisition and need to construct sample-level mass tracks before cross-sample alignment.
Use when you have collected MS/MS spectra from a microbial sample (pure culture, environmental isolate, or mixed community) and need to assign chemical identities to observed m/z…
Use when you have peak-abundance data (after molecular formula assignment, peak filtering by m/z, isotope, ppm error, and sample presence thresholds) and you need to quantify and…
Use when when processing multiple LC-MS samples in a cohort study and MassGrid construction reveals that anchor mass tracks (13C/12C isotope or Na/H adduct pairs) in non-reference…
Use when after identifying statistically significant LC-MS features (e.g., via MB-VIP with p < 0.01 and permutation testing), when you need to consolidate multiple ionization and…
Use when you have paired mass-spectrometry spectral data (m/z and intensity arrays) with known molecular fingerprints or InChIKeys, and need to train a supervised deep learning…
Use when you have an unknown compound's mass spectrum (m/z peaks and intensities) in positive or negative ion mode and need to identify candidate metabolites from a struc — from…
Use when your XCMS-processed LC-MS dataset exhibits retention-time drift or misalignment artifacts—particularly when analyzing hundreds of samples, data acquisition spans longer…
Use when when implementing or modifying a numerical compression/decompression component (e.g., Numpress for mass-spectrometry m/z and intensity arrays) and you need to verify that…
Use when you have raw RNA-seq read counts and a set of normalization factors (e.g., TMM-computed library size scales from edgeR's calcNormFactors), and you plan to fit a linear…
Use when after initial retention-time-based feature grouping (e.g., ±20 s window) when you need to separate co-eluting features that are chemically distinct.
Use when you have raw or minimally processed tandem MS spectra (in mzML, mgf, or other standard formats) and need to prepare them for spectral matching, library searching, or…
Use when a webservice component (like MAGMa's joblauncher) lacks formal API documentation but the source code is accessible, and downstream consumers (web applications, external…
Use when you have centroided MS2 spectra (ddMS2 data in mzML format) from HRMS analysis and need to identify potential PFAS compounds among thousands of features.
Use when processing heterogeneous mass spectrometry libraries (e.g., from OMSLs) where chemical identifiers are unevenly populated across records.
Use when after feature m/z grouping and pairwise alignment detection have identified candidate feature pairs, and anchor points have been selected to establish retention time…
Use when when you have one or more imzML files containing mass spectrometry imaging data and need to import them into LipidQMap for ion image extraction, isotopic correction, and…
Use when you have trained or loaded a deep learning model that produces high-dimensional spectral embeddings (e.g., 200-dimensional vectors from MS2DeepScore base network) and…
Use when you have preprocessed MS/MS spectral data (normalized peak intensities and m/z values) and need to transform spectra into fixed-dimensional molecular embeddings — from…
Use when you have independent LC-MS assays (e.g., positive and negative ionization modes, different lipid profiling assays, or different chromatographic methods) analyzed on the…
Use when you have raw LC–MS data in vendor-proprietary or uncorrected formats (e.g., .raw, .d) and need to perform targeted peak detection, retention-time correction, or automated…
Use when you have multiple CSV feature tables from independent metabolomic experiments, each with RT and m/z annotations, and you need to produce a single consolidated feature…
Use when you have cloned a scientific repository containing Python code (scripts, Jupyter notebooks, or module imports) and need to execute it locally or on new hardware.
Use when you have raw IM-MS data in Agilent MassHunter (.d) or UIMF format from drift tube (DT) or SLIM instruments, and you intend to perform HRdm demultiplexing or peak — from…
Use when you have raw floating-point m/z and intensity arrays extracted from mass-spectrometry experiments (e.g., from mzML or mzXML files) and need to compress them for storage…
Use when you have constraint-based metabolic models with integrated multi-omics constraints (transcriptomics via Reaction Activity Scores, extracellular flux ratios via YSI…
Use when you need to understand how a complex MS/MS spectral search system routes query spectra through multiple parallel processing pipelines with different objectives (e.g.,…
Use when after installing a Python package (especially one with optional dependencies) to confirm that: (1) core modules are accessible and importable;
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 after chromatographic peak detection on preprocessed LC-MS data, when you have hundreds or thousands of individual m/z × retention-time peaks and need to associa — from…
Use when when you have mass spectrometry data organized in a Pandas DataFrame with m/z values, retention time (RT), and intensity measurements, and you want to visualize the joint…
Use when you have a preprocessed bag-of-fragments corpus derived from tandem mass spectrometry spectra and need to discover recurring fragmentation motifs without prior compound…
Use when after serializing empirical compound collections to JSON format via khipu's build_empCpds command, or before ingesting empCpd.
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 you have m/z values from spatially-resolved mass spectrometry imaging (e.g., MALDI-MSI, DESI-MSI) and need to assign molecular formulae to thousands of features with…
Use when you have simulated or experimental mzML data from two or more fragmentation controllers (e.
Use when you are evaluating a new or existing data analysis pipeline (e.g., MetaboDirect) and need to produce a transparent, evidence-based feature matrix showing which analyses…
Use when you have computed a histogram of pairwise mass differences from MS peaks and want to determine which observed mass differences correspond to known chemical species such…
Use when salmon quant is run with the --writeMappings/-z flag and you need to verify that all mapped reads appear in the SAM output file.
Use when when you have an Excel file uploaded by a user following the InjectionDesign template schema and need to convert it into a modifiable, structured sample list that…
Use when you have raw molecular datasets (e.g., METLIN-CCS, CCSBase) with SMILES strings, 3D coordinates, adduct information, and ground-truth collision cross section labels, and…
Use when after peak picking (e.g., via MS-DIAL) and quality control filtering, when you have a raw feature abundance matrix with intensity values across multiple samples and need…
Use when you have completed cluster-based filtering of KEGG candidate assignments in untargeted LC-MS metabolomics and need to rank those candidates by biological plausibility…
Use when you have raw MS2 spectral data (MGF, mzML, or msp format) and need to generate a sample-level fingerprint for comparison across metabolomics samples, especially when…
Use when you have a SMILES string or batch of SMILES strings representing chemical structures and need to obtain NP Classifier predictions programmatically.
Use when you have raw LC-MS data (mzML or vendor format) and need to discover and characterize all chromatographic features present, without prior knowledge of target analytes.
Use when you have an untargeted metabolomics feature table with m/z values, retention times, and intensity measurements, a metabolic network representation with compound — from…
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 converting raw metabolomics data (tab-delimited text files, Sciex OS exports) into a structured object for batch processing, or when you need to organize…
Use when you have CDF-format mass spectrometry imaging files from plant roots with accompanying MATLAB workspace files (.mat), and your research goal is to reproduce linear-axis…
Use when you have mass spectrometry spectral data (m/z and intensity pairs, precursor m/z, MS level, and metadata) in R memory or in a file format (mzML, mzXML, CDF, MGF, MSP),…
Use when you have multiple tandem MS/MS libraries in different formats (msp, mgf) from different providers (NIST, RIKEN, MoNA, GNPS) with incomplete or inconsistent structural…