Use when when your DDA-mode LC-MS/MS data exhibits chimeric spectra patterns that differ systematically from the reference training set used in DNMS2Purifier, or when you wish to…
Use when you have a set of chemical structures (as SMILES strings or convertible to SMILES) and need to submit them programmatically to the NP Classifier /classify endpoint for…
Use when you have implemented or are evaluating an algorithmic or system optimization (e.g., MASST+) that claims to reduce execution time, and you need to quantify and…
Use when when training a deep neural network on paired MS/MS spectra to predict structural similarity scores, especially when the training dataset is moderate-sized (109,734…
Use when you have paired measurements (e.g., gene expression counts, protein abundance, or sampled flux distributions) from two cell lines or conditions and need to assign a…
Use when after abundance-correlation-based feature group refinement when you observe that larger feature groups (particularly those with 3+ features in the same m/z–retention-time…
Use when after enriching a project JSON document with external metadata (e.g., organism names, genome identifiers) or before writing enriched JSON to disk.
Use when you need to generate a set of candidate chemical formulas for LC-MS/MS simulation—specifically when you want to populate a virtual mass spectrometer with realistic…
Use when when you have a raw mass spectrometry file (e.g., Thermo Orbitrap .raw) and need to establish the measured throughput of a spectral reading function (e.
Use when you have paired MS/MS spectra and molecular structures (SMILES or SDF format) and need to perform compound identification by retrieving the correct structure for an…
Use when after training a GNN-RT model on preprocessed molecular graph data (from Train.py) or after applying transfer learning to an in-house dataset (from Transferlearning.
Use when after applying batch correction (e.g., via pycombat) to a multi-batch feature table, to validate whether the correction has reduced systematic intensity differences…
Use when after constructing a count matrix from transcript quantification files (via tximport, HTSeq, featureCounts, or direct alignment) and before running DESeq() differential…
Use when you have pseudo-MS/MS spectra from LC-MS all-ion fragmentation (AIF) data that have been matched against one or more ion fragment databases (e.g., LipidPos, MassBank),…
Use when after converting proprietary vendor mass spectrometry files (Thermo .raw, Agilent .d, Bruker .d, or mzML) to MZA HDF5 format using the MZA executable.
Use when when annotating matrix-related ions in MSI datasets where two or more ions share identical or near-identical m/z values (isobaric ions) or exhibit overlapping spatial…
Use when you have raw LC-MS/MS spectral data in .mgf format (or vendor-specific raw data that can be converted to .mgf via MZmine or similar tools) and need to prepare it for…
Use when when you have mass spectrometry data (mzML, Bruker .d, or CSV) loaded into a Pandas DataFrame with columns for m/z, retention time, ion mobility, or intensity values, and…
Use when constructing HPLC column feature vectors from raw metadata that includes additive composition flags (e.g., presence/absence or concentration of formic acid, acetic acid,…
Use when you have quantification results from two or more independent implementations, versions, or variants of the same analysis tool (e.g. C++ salmon 1.11.
Use when you have two or more independent, standardised scoring functions that rank the same set of candidate pairs (e.
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 you have raw or processed arrival-time data from a TWIM-MS instrument and need to convert it to CCS values for comparison across experiments or biomolecular classes.
Use when you have raw or processed TWIM-MS data with arrival time and m/z values for multiple features, but lack prior structural identification (e.g., from spectral libraries or…
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 pre-trained MS/MS spectral embedding model and need to validate that it achieves strong and consistent retrieval performance on curated spectral libraries that…
Use when when you have MS/MS fragment spectra (in .mgf format) acquired from unknown metabolite features and need to annotate them against known compounds.
Use when you have multiple MSP (mass spectrum) library files to read and merge in R, and your computational task is time-consuming (e.g., structure extraction, SMILES assignment,…
Use when when you have a set of query chemicals (e.g., ethyl hexanoate, methyl salicylate) and need to find their -matched structural analogues within a reference library — from…
Use when you need to validate that a software project''s release branch is stable and ready for distribution.
Use when after feature clustering has been applied to co-eluting LC-MS features and mass-to-charge ratio matching to KEGG has produced an annotated table with adduct assignments.
Use when when you have extracted multiple spectral attributes (e.g., base-peak m/z, intensity, retention time, scan index) from individual MS scans via accessor functions and need…
Use when when you have a labeled peak quality matrix (with known pass/fail labels), need to objectively compare performance across multiple classification algorithms (e.
Use when you have executed a binary classifier (such as BitterPredict.m) on a set of molecules with chemical structure descriptors and need to translate the raw predictions into a…
Use when you have a GNPS-generated molecular network (graphml or JSON format) and corresponding MS2LDA experiment results or chemical class assignments, and you want to annotate…
Use when you have a GNPS mass spectral molecular network (classical or feature-based) and MS2LDA-derived Mass2Motif data, and you need to annotate network nodes with both chemical…
Use when you have two related compounds (a known reference and its structural analog with unknown modification site), baseline peak annotations from cosine alignment, and newly…
Use when when you need to make OpenMS C++ classes, functions, or data structures callable from Python code, or when verifying that a newly bound C++ component can be imported and…
Use when after peak detection when you have a table of detected peaks with m/z values and need to improve mass accuracy for downstream annotation.
Use when you have a trained NeatMS neural network model (.h5 format) and need to assess its classification performance at a specific decision threshold (e.g., 0.01) to de — from…
Use when you have loaded raw mass spectrometry spectral data (in MGF, MSP, mzML, or mzXML format) and need to decide which intensity threshold(s) to use for filtering out noise…
Use when after bias-correcting ATAC-seq cutsite signal (using ATACorrect or equivalent) when you have a set of genomic regions of interest (e.
Use when you have raw CE-MS data (mzML or netCDF format) with migration time measurements and need to establish a reproducible compound-specific axis that is independent of…
Use when your input is a raw metabolomics intensity matrix (compounds × samples) with known batch assignment and QC sample labels, and you observe signal drift across the…
Use when you have built multiple Docker image variants (e.g., cli, dev, linux, windows) from a multi-stage Dockerfile and need to verify that their uncompressed and compressed…
Use when when you have run a pathway ranking method (such as PALS/PLAGE) on clean metabolomics data and wish to assess how sensitive the resulting pathway activity rankings are to…
Use when you have raw GC-MS data in netCDF or vendor-specific binary format and need to separate co-eluting compounds and extract clean mass spectra for each individual chemical…
Use when after batch correction of a metabolomics dataset using pooled study quality control (SQC) samples, when you have multiple candidate internal standards and need to…
Use when augmenting mass spectrometry ion images for contrastive learning, specifically when you need to simulate the natural Poisson noise that arises from photon-counting…
Use when you have executed a complex multi-step processing pipeline (e.g., ENCODE Hi-C uniform processing pipeline) and need to confirm that the generated output files match a…
Use when you have trained a neural network or regression model on one paired microbiome-metabolome dataset and wish to test whether it can predict metabolite abundances in an…
Use when you have detected monoisotopic features (m/z, drift_time, retention_time,
Use when you need to obtain all project JSON documents currently deposited in a data platform (such as the Paired Omics Data Platform) to validate their structure against a JSON…
Use when after mass track extraction and alignment across samples, when preparing to detect elution peaks on composite mass tracks.
Use when when designing a library that needs to support multiple plotting backends (e.g., matplotlib, bokeh, plotly) and you want to avoid reimplementing parameter validation,…
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 when you have Thermo Fisher Scientific .raw files from Orbitrap instruments and need to build a quantitative summary of MS1 acquisition intensity dynamics across a…
Use when you have paired tandem MS/MS spectra with known molecular fingerprints,
Use when when you have a loaded metabolite database (e.g., hmdb_compounds.p pickle file) and need to constrain the chemical space to a specific instrumental m/z range (e.
Use when after peak alignment with peakAlign(), when you have an MSImagingExperiment