Use when you are building or extending a multi-module Python library for scientific computation (e.
Use when you have acquired LC-MS/MS data in Data-Dependent Acquisition (DDA) mode for untargeted metabolomics and suspect contamination from chimeric (co-fragmented) MS/MS spectra.
Use when when you need to support multiple plotting library backends (static or interactive) for the same data visualization domain (e.
Use when after executing the Juicer pipeline on raw Hi-C FASTQ files, to confirm that the pipeline has generated the expected .hic output artifact and that the contact matrix…
Use when you have a pre-trained ABCoRT model checkpoint and a new chromatography
Use when you have raw or processed MS spectrum data (m/z and intensity pairs) from DI-MS, ASAP-MS, or other high-throughput mass spectrometry instruments that requires automated…
Use when you have a feature table from LC- or GC-HRMS data (either detected via pyOpenMS or imported as a custom feature list) containing m/z, retention time, and intensity…
Use when after normalizing total UMI counts per cell using normalize_total, and before PCA or feature selection.
Use when when a user uploads a JSON project document to the Pairing Omics Data Platform and you need to determine whether it satisfies the platform's data structure requi — from…
Use when your input is a normalized, centered gene expression matrix with many genes (e.g., 12,000+) and you need to validate whether reducing to a smaller number of principal…
Use when when you have lipidomics quantitation data (lipid abundances across samples) that you need to load into a unified, annotated R object for analysis—either from public…
Use when beginning an LC-MS data analysis pipeline and you need to rapidly inspect instrument metadata, acquisition parameters, or scan statistics from proprietary Thermo .raw…
Use when you have raw lipidomic and metabolomic spectral data files from a Multi-ABLE barocycler-based concurrent multiomics experiment and need to normalize ion intensities,…
Use when when you need to aggregate values from multiple records in a JSON input document into a single concatenated string field (e.
Use when you have raw LC-MS/MS data acquired in Data-Dependent Acquisition (DDA) mode and need to create a labeled training dataset for customized purification model development.
Use when you have (LC-)IM-MS lipidomics data from samples spiked with U13C-labeled yeast extract, measured CCS values for both labeled and unlabeled lipids, and need to quantify…
Use when you have cloned a Python project (e.g., scverse/scanpy) that includes a hatch.toml configuration file and need to set up a consistent development or testing environment.
Use when you have metabolomics data (targeted LC/MS or untargeted GC/MS) with left-censored missing values below the limit of quantification (LOQ) or limit of detection (LOD), and…
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 when beginning mass alignment in a multi-sample LC-MS metabolomics study, before constructing the MassGrid.
Use when you have a GNPS mass spectral molecular network (in .graphml or Cytoscape format) and wish to annotate its nodes and edges with chemical class assignments from the GNPS…
Use when after batch correction of metabolomics QC samples using pooled study quality control (SQC) samples, when you have multiple candidate internal standards and need to…
Use when after applying mspcompiler pipeline transformation steps (e.g., reorganize_mona, assign_smiles, assign_ri, read_multilibs, separate_polarity, complete_mgf) to confirm the…
Use when after completing PCA and k-nearest neighbor graph construction on preprocessed, log-normalized, highly-variable-gene-filtered single-cell RNA-seq data (stored in an…
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 bias-corrected ATAC-seq footprint signals (BigWig files) from two or more distinct conditions (e.
Use when you have high-resolution LC-MS data processed through both XCMS feature detection and RAMClustR clustering, and you need to verify the reliability of molecular weight…
Use when when you have raw count matrices from paired microbiome (16S rRNA or metagenomic) and metabolomic (LC-MS/MS) profiling data that will be used to train or apply a…
Use when when you need to verify that a software package (such as MassQL) passes its periodic integration test suite as indicated by CI workflow badges in the project…
Use when after constructing a SummarizedExperiment object from raw metabolomics data via buildExperiment, or after batch correction and ratio computation steps, inspect rowData,…
Use when when contributing code changes to a Python project (fork, feature branch, or pull request) that uses a setup.py-based test suite, before pushing changes to the remote…
Use when apply Leiden clustering after constructing a k-nearest neighbor (kNN) graph from single-cell expression data when you need to partition cells into discrete, biologically…
Use when you have run mass detection and chromatogram building independently on each LC-MS/MS sample and produced per-sample feature lists with m/z, retention time, and intensity…
Use when you have an untargeted metabolomics feature table (with m/z, retention time, and statistical significance values) and want to predict which metabolic pathways and…
Use when immediately after extracting ion chromatograms (EICs) by binning mass spectral data across the full m/z range from raw LC/HRMS files (mzML, mzXML, or netCDF format).
Use when you have already assigned samples to batches (inter-batch balance is fixed) and need to shuffle injection order within each batch to decorrelate sample properties from…
Use when after drift correction of LC-MS peak intensity data, when you need to identify metabolic features with excessive internal spread (within-group variability in QC samples)…
Use when when you have generated multiple 3D conformations for a molecule or set of ionized adducts (e.g., via RDKit) and need to retain only the most energetically favorable…
Use when when comparing mapping performance between two mapper implementations (e.g., C++ salmon vs. Rust salmon), validating that a bug fix or algorithmic change did not degrade…
Use when when identifying landmark peaks for retention time alignment in multi-sample LC-MS metabolomics workflows.
Use when after training multiple MLPNN models (via cross-validation) on paired microbiome and metabolome data when you need to extract interpretable feature importance scores from…
Use when you have completed one or more LC-MS gradient runs, extracted separation efficiency metrics from the resulting MS1 and MS2 spectra, and need to incorporate those real…
Use when when linking statistically significant LC-MS features into structural clusters based on adduct signatures and cross-assay references (e.g., [M+H]+/[M-H]−), and you need…
Use when you have mzML or mzXML mass spectrometry data files and need to extract and validate spectral records (m/z and intensity arrays) for lossless compression, lossy…
Use when when performing targeted peak detection on LC-MS data where compounds have been assigned expected ionization polarities (positive or negative mode) in the target list,…
Use when you have a trained NeatMS neural network model and a labelled validation dataset of MS1 peaks (annotated as 'High_quality' or 'Low_quality'), and you need to identify the…
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 you have untargeted MS2 spectra from environmental or clinical samples that will be used for natural product identification (e.g., linking to BGCs via IOKR or other…
Use when after MamsiStructSearch has completed structural clustering of statistically significant LC-MS features (p < 0.
Use when you have performed lipid identification in MS-DIAL and need to pass the results to LipoCLEAN or another downstream quality-filtering tool.
Use when after nontargeted peak detection and segmentation has generated a feature table from raw LC-MS data (mzML or vendor format), apply quality assessment when you need to…
Use when you need to represent, validate, and manipulate molecular compositions in MS analysis—specifically when annotating precursor or product ions with elemental formulas,…
Use when when you have binned mass spectrometry imaging peaks and want to understand which detected mass-to-charge ratios represent the same metabolite in different ionization…
Use when after executing a reproducible simulation pipeline (particularly for Over-representation Analysis in metabolomics), compare the newly generated outputs against reference…
Use when you have a collection of cleaned MS/MS spectra (in formats like mzML, mgf, msp, mzxml, or json) and need to predict molecular structural similarities between spectrum…
Use when you have a new or modified LC-MS data processing tool and need to determine whether it can handle production-scale sample cohorts (50–100+ samples) on modest hardware…
Use when when you have loaded a cooler file containing Hi-C contact matrices and need to quantify how contact probability decays with genomic distance within a single chromosome.
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 MS2 fragmentation spectra from multiple metabolomics samples and need to compare them in a retention time-agnostic manner, especially when samples are chemically…
Use when before executing a complex bioinformatics pipeline (such as Hi-C data processing) that depends on multiple third-party tools with explicit version constraints.