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 you need to validate batch correction or normalization algorithms, require ground-truth condition/batch effect annotations for method benchmarking, or want to…
Use when when you have raw Agilent MassHunter (.d) or UIMF IM-MS data files from drift tube (DT) or structure for lossless ion manipulations (SLIM) instruments and need to ingest…
Use when you have metabolomic data (e.g., from LC-MS or GC-MS comparing patient to controls) showing differential abundant metabolites (DAMs), candidate genes from exome…
Use when after molecular formula assignment has been performed on FT-ICR MS peaks and you need to remove assignments with unacceptable mass error before proceeding to…
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 you have downloaded the LC-MS spectral peak dataset (DOI 10.25345/C5FD2F)
Use when you have a small training dataset for molecular property prediction (e.g., <500 samples from PredRet or MoNA databases) and a pre-trained GNN model is available that was…
Use when you have mass spectrometry imaging (MSI) data with ion images that need low-dimensional representation learning for downstream tasks like co-localized ion searching or…
Use when you have obtained or need to prepare a DTCCS_N2 reference library for U13C-labeled lipids (typically provided as part of a lipidomics tool distribution) and need to…
Use when evaluating alternative implementations of data storage or retrieval strategies in R objects—specifically when deciding whether to eagerly populate all columns in a data…
Use when when you have an existing real mzML file from a metabolomics LC-MS/MS acquisition (e.g., beer or urine samples) and need to populate a virtual mass spectrometer with the…
Use when after selecting statistically significant features from multi-assay LC-MS metabolomics datasets (e.g., via MB-VIP and permutation testing with p < 0.01).
Use when you have loaded raw mass spectrometry data (mzML, mzXML, or CDF format) into AutoTuner and need to identify peak regions in the TIC trace prior to extracted ion…
Use when when you need to assess whether a given ATAC-seq clustering method (or variant) is competitive on your data or when evaluating which published method to adopt.
Use when you have multidimensional MS data converted to MZA HDF5 format (from Agilent .d, Bruker .d with ion mobility, Thermo .
Use when you have a curated dataset of small molecules with SMILES, optional 3D coordinates, adduct information, and experimentally measured CCS values (in Ångströms or similar…
Use when when you have peak-abundance .csv files with assigned molecular formulas (elemental composition: C, H, O, N, P, S) from FT-ICR MS or high-resolution MS and need to…
Use when when you have a feature list from HRMS with tentatively assigned molecular formulas (from in silico tools or databases) and need to assess formula plausibility before…
Use when you have LC/MS feature data (m/z, retention time, intensity) and need to assign metabolite annotations with confidence scores rather than binary peak-to-compound matches.
Use when when evaluating or designing a mass spectrometry data analysis platform, and you need to verify that every supported separation/ionisation technique (LC, GC, IMS, MS…
Use when after peak detection in GCIMS when you need to group detected peaks across multiple samples into reproducible clusters.
Use when you have NMR peak data (1H and 13C chemical shift values) and need to obtain SMART 3 classification predictions from the DeepSAT service.
Use when after converting mass-spectrometry data from an existing format (mzML, mzXML, or vendor-specific formats) into mzPeak using command-line tools or API calls.
Use when when you have an untargeted metabolomics feature table (m/z values, retention times, intensity measurements, and p-values from statistical testing) and want to p — from…
Use when when processing raw FT-ICR transient data (e.g., ESI_NEG_SRFA.d) that requires assignment of molecular formulas to experimental m/z peaks.
Use when you have real LC-MS/MS data (mzML) from a complex sample (e.g., beer, metabolomics extract) and need to prototype or validate a new data-dependent acquisition (DDA)…
Use when when you need to evaluate how a specific algorithm parameter (such as SearchMolecularFormulas first_hit mode) affects the quantity and quality of molecular formula…
Use when you have LC-MS/MS DDA metabolomics data (positive and/or negative ionization modes) and sample metadata (originating taxon) for one or more samples, and you need to…
Use when you have raw SMILES strings from multiple external database sources (e.g., PubChem, ChEMBL, vendor databases) that need to be integrated into a unified structure…
Use when you have XCMS-aligned feature tables with retention time values and need to compute pairwise feature similarity.
Use when you have an annotated or raw tandem mass spectrometry spectrum and need to identify which observed peaks correspond to expected peptide fragment ions from a known or…
Use when you have a measured m/z value from spatially-resolved metabolomics or mass spectrometry imaging and need to assign a molecular formula with high confidence.
Use when after running DESeq() and extracting raw results with results(), when you have log fold change estimates with high variance and wish to improve their precision.
Use when when a deep learning pipeline processes mass spectrometry spectra through multiple independent scripts (e.g., train_rescore.py, run_fiddle.py, test_caffeine.py) and a…
Use when you have raw MS data files from one or more instrument vendors (Agilent, Bruker, Thermo Fisher, or mzML-formatted) and need to convert them to a vendor-agnostic…
Use when you have a normalized single-cell expression matrix (e.g., after SCTransform) and need to compute gene-level covariance structure for pathway enrichment analysis (e.g.,…
Use when you have computed eigenvector values from a prior eigs_cis calculation on a cooler Hi-C matrix and need to classify genomic regions into discrete A/B compartment…
Use when you have aligned ATAC-seq BAM files and want to discriminate between transcription factor binding sites that are actually occupied by protein versus sites with matching…
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 after you have identified statistically significant LC-MS features and run MamsiStructSearch to generate structural clusters (isotopologue groups, adduct groups,…
Use when you have a peak-picked feature table (HDF5 format) from high-dimensional MS data (m/z, drift_time, retention_time, intensity) and need to identify and label isotopic…
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 a Shiny application or R package is confirmed to work on one OS (e.g., Windows only) but fails to initialize or run on others due to unresolved file path conventions,…
Use when you have high-dimensional replicate experiment data (metabolomics, proteomics, or genomics) with multiple biological or technical replicates per sample, and you need to…
Use when you have a labeled peak quality dataset (development set with ground-truth pass/fail labels), a defined set of peak-quality metrics (e.
Use when you have raw Hi-C FASTQ files from a kilobase-resolution Hi-C experiment and need to produce a processed Hi-C contact map (.hic file) for visualization, loop calling, or…
Use when apply CLR transformation when working with microbiome or metabolomic relative abundance tables that will be input to multivariate regression or neural network models.
Use when you have an observed m/z value from mass spectrometry imaging and need to assign a chemical formula with high confidence.
Use when you have imported raw mass spectrometry data in formats such as MGF, MSP, mzML, or mzXML and need to clean the spectral data prior to similarity comparisons, metadata…
Use when you have raw or unprocessed MS/MS spectral data in standard metabolomics formats (MGF, mzML, mzXML, msp, or JSON) and need to import them into a Python-based workflow for…
Use when you have raw methylation call files from Bismark, MethylDackel, or similar bisulfite alignment tools (bedGraph, cytosine report, or tabix-indexed formats) and need to…
Use when when generating a virtual chemical mixture for LC-MS/MS simulation, or when sampling molecular formulas from a metabolite database (such as HMDB), you need to restrict…
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 when you have genomic sequences (assembled contigs or antiSMASH/BOA mining results) and want to match experimental tandem mass spectra against predicted RiPP structures.
Use when you are evaluating or selecting FT-ICR MS software for a specific metabolomics workflow and need to assess which tools support your required analytical dimensions (e.g.,…
Use when you have a collection of MS/MS spectra (stored as Spectrum2 objects in an ms2Lib class) and need to identify which spectra share identical fragmentation…
Use when you are in the Bayesian optimization loop after fitting a Gaussian Process model to observed LC gradient runs, and you need to propose the next gradient to evaluate.
Use when you have LC-MS/MS spectra (MGF, mzXML, mzML, or mzData format) and either raw genome nucleotide sequences or antiSMASH/BOA genome mining tool output, and you need to…
Use when after loading an MsmsSpectrum object but before intensity filtering or spectral annotation.