Use when you have transcript-level quantification files (quant.sf.gz or quant.gz) from salmon, sailfish, kallisto, or oarfish and need to aggregate them into gene-level or…
Use when you have received chemical annotations from GNPS spectral library matching and need to (1) assess annotation confidence and validity for downstream analysis, (2)…
Use when when you have raw tandem mass spectra in mz/intensity format with precursor m/z values, and need to extract all fragmentation features (observed peaks and neutral losses)…
Use when you observe jagged or noisy peak profiles in low-abundance ions after loading raw IM-MS data (Agilent MassHunter .
Use when after XCMS feature detection and retention time correction, when you have a feature table (CSV or XCMS object) with m/z and retention time values aligned across samples.
Use when when you have processed metabolomics LC-MS/MS data organized by batch and sample type (including pooled QC replicates), and you need to quantify whether batch-to-batch…
Use when you have a large gzip-compressed file (e.g., mzML.gz) with an embedded index structure in the gzip header comment field, and you need to retrieve specific blocks (e.g.,…
Use when when you have a peptide sequence and need to predict which fragment ions (B and Y series) should appear in an MS2 spectrum at a known isotopic abundance (e.g., natural…
Use when processing raw MS intensity tables from long measurement sequences where you observe systematic, time-dependent changes in signal magnitude (e.g., progressive increase or…
Use when you have a normalized gene expression matrix (log2-quantile normalized, filtered to high-variance genes) and a collection of annotated gene sets (e.g., Reactome, MSigDB…
Use when when working with Bruker .d/.baf mass spectrometry imaging data and needing to feed it into MSIGen or other open-source MSI processing pipelines.
Use when when reading a binary file format with a magic integer or fixed checksum field at a known offset, and endianness is not explicitly declared in file metadata or header…
Use when when you have raw or processed TWIM-MS data (arrival time and m/z values) from a mass spectrometry instrument and need to organize it into a feature table before…
Use when after importing fragment files into AnnData using pp.import_fragments and before performing spectral embedding (tl.spectral) or other dimension reduction.
Use when you have a Chemical Feature Tree artifact (phylogeny) output from q2-qemistree's make-hierarchy method and need to verify its structural validity, count nodes (leaves and…
Use FIRST when working with the ASB Metabolomics skill collection. The meta-skill: it explains good practice (search -> apply -> ground), enforces the license-tier acknowledgment…
Use when releasing a new version of a Python package, validating packaging infrastructure changes, or confirming that distribution channels (PyPI, Bioconda) remain functional…
Use when when you have cloned or accessed the Reverse_metabolomics_library_generation
Use when you have LC-IM-MS/MS raw data from multiple tissue samples and need to identify and quantify unsaturated sterol lipids at the isomer level (distinguishing double-bond…
Use when when annotating .msp files with metadata from multiple external web services and you need to monitor which services are slow or unreliable.
Use when you need to automate testing and quality checks on code changes—specifically when pull requests or commits are made to a repository and you want to verify that builds…
Use when you are parsing mass spectrometry spectral library files in MSP format and need to guarantee that all spectrum records are either successfully integrated into the final…
Use when you have a feature-by-sample matrix (rows = annotated chemical features such as m/z, retention time, GNPS spectral library matches;
Use when you have an untargeted metabolomics feature table with m/z values, retention times, intensity measurements, and p-values from statistical testing, but lack or wi — from…
Use when you have a published scientific article describing a computational method (e.g., natural products annotation, MS/MS data processing) and need to assess reproducibility…
Use when after drift correction and before missing value imputation when your LC-MS peak table contains features with variable detection rates across samples.
Use when you have a processed single-cell expression matrix (AnnData object) with pre-computed cluster assignments (e.g., leiden or louvain clusters in adata.obs) and want to…
Use when when you have detected peaks in a direct injection FTICR-MS mzML file (or similar high-resolution MS format) and need to assess whether m/z measurements are accurate and…
Use when when running Mass2SMILES inference on a TensorFlow-CPU build (e.g., delser292/mass2smiles:final container) and you need to optimize inference speed by controlling CPU…
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 after instantiating a specXplore dashboard session layer with a loaded session data object from disk, before conducting visual exploration of LC-MS/MS spectral data.
Use when when designing injection plate layouts in InjectionDesign and needing to display sample positions with clear, domain-appropriate labels on the y-axis (e.g., row…
Use when after frequency-based denoising has been applied to individual replicate spectra within each feature (via generate_denoised_spectra), you have a collection of denoised…
Use when you have ion mobility-mass spectrometry lipidomics data from samples spiked with U¹³C-labeled lipid internal standards (fully labeled yeast extract) and want to assess…
Use when when you have aligned paired scATAC-seq and scRNA-seq data from the same cells (multiome data) and need to create a single reduced-dimension coordinate space that…
Use when you have centroided LC-MS/MS spectra (in MGF, mzXML, mzML, or mzData format) and wish to identify peptidic natural products or ribosomally synthesized and post-t — from…
Use when when you have a set of query chemicals (e.g., ethyl hexanoate, methyl salicylate, octanal, undecane) and need to evaluate them against reference compound categor — from…
Use when when implementing a new ComputeConverter subclass for MSMetaEnhancer that performs local chemical structure conversions using RDKit (e.g., SMILES to InChI, canonical…
Use when preparing training batches for a neural network classifier on LCMS peak data where class counts are unequal (e.g., more high-quality peaks than low-quality peaks).
Use when when you have raw mass-spectrometry data (precursor m/z, ionization mode, and fragment m/z–intensity pairs) that must be fed into a CNN model for metabolite annotation…
Use when you have predicted retention times from one or more machine learning models (DNN, Gaussian Process, or ensemble) applied to small-molecule chromatography data, along with…
Use when your input is a raw two-dimensional MS map (m/z vs retention time) derived from chromatography–mass spectrometry data with poor signal-to-noise characteristics, and you…
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 xcms-processed LC-MS data with detected feature groups (from xcms grouping), suspect retention time misalignment across samples due to long acquisition periods…
Use when when you have an LC-MS feature table with m/z and retention time columns and need to identify which observed ions correspond to the same neutral compound under different…
Use when you have molecular structures (SMILES or SDF format) for which you need to predict retention time in liquid chromatography, especially when your target dataset contains…
Use when you need to create realistic, diverse chemical populations for simulating LC-MS/MS acquisition strategies in a virtual environment.
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 converted spectral data (jcamp, RAW, or mzML format) from NMR, IR, or MS instruments and need to identify individual peaks, extract their properties…
Use when you have paired cdf files (raw mass spectrometry imaging data) and Matlab workspace (.mat) files for the same root sample, and you need to reproduce published linear-axis…
Use when when you have downloaded a released version of a structured dataset (e.g., LOTUS from Zenodo) and need to confirm it matches the documented headline statistics before…
Use when you have executed batch searches of MS/MS spectra against multiple domain-specific MASST indices and need to integrate the resulting match outputs into a single — from…
Use when you have raw LC-MS fractional abundances (FAM) data from isotope labeling experiments and need to obtain true mass distribution vectors (MDV) that represent only the…
Use when you have loaded individual methylation call files as methylRawList objects from bisulfite sequencing experiments (via methRead()) and need to perform base-level…
Use when after peak picking, sample alignment, and isotopologue/adduct grouping are complete, and you have DDA-MS2 scans associated with grouped feature ions.
Use when when you have generated separate MemoMatrix objects from independent sample sets (e.g., sample set A and sample set B) and need to align and combine their MS2 fingerprint…
Use when you have translated or raw SMILES strings from a chemical structure curation pipeline and need to remove invalid chemical structures, resolve sanitization errors (e.
Use when when you have generated a feature abundance matrix from mzrtsim() peak list simulation with known sample-level attributes (condition assignments, batch labels, sample…
Use when you have raw or processed MSI data (in imzML or rMSIproc formats) and need to identify and annotate matrix-related peaks before statistical analysis or metabolite…
Use when after peak detection and feature alignment in a metabolomic LC–MS/MS or GC–MS workflow, when you have a feature table (rows=metabolic features, columns=samples) split…