Coding & Dev Tools

holobiomicslab Skill

Use when analyzing MALDI-mass spectrometry imaging data in which sodium or other alkali metal contamination is suspected, or when peak lists show unexplained mass differences in the range of ~20–25 Da (characteristic of Na adducts).

holobiomicslab Skill

Use when after running RAMClustR clustering on XCMS-processed metabolomics data, export spectral data when you need to share clustered spectra with external annotation software (MSFinder, Sirius), perform spectrum matching against reference databases, or prepare results for collaborative analysis.

holobiomicslab Skill

Use when after identifying statistically significant LC-MS features (e.

holobiomicslab Skill

Use when you have multi-sample MS1 data (from Agilent, Thermo, Bruker, or mzML formats) and need to quantify ion-level quality attributes—such as signal consistency, noise characteristics, or chromatographic stability—to either flag outlier samples or validate data fitness for downstream.

holobiomicslab Skill

Use when you have TWIM-MS experimental data with arrival times and m/z values, and access to calibrant reference standards with known CCS values (typically loaded from a calibration template).

holobiomicslab Skill

Use when you need to enable optional modules in Pyteomics that depend on external libraries not bundled with the core package—such as h5py and hdf5plugin for mzMLb format access, sqlalchemy for Unimod database queries, or psims for ProForma parsing.

holobiomicslab Skill

Use when you have raw LC-MS/MS spectra from vendor instruments (mzML, mzXML, MGF, or MSP format) with variable peak quality and intensity distributions, and you plan to perform library matching, molecular networking, or spectral similarity comparison.

holobiomicslab Skill

Use when after sample alignment and grouping of isotopologues and adducts have been completed, when the aligned feature table contains NA or zero entries (missing intensities) for features that are detected in some samples but fall below the detection threshold in others.

holobiomicslab Skill

Use when after feature detection but before statistical analysis, when your study includes blank samples (e.g., solvent or extraction blanks) and you want to remove features that fail to show meaningful enrichment in actual study samples relative to blank contamination.

holobiomicslab Skill

Use when when you have a set of small molecule structures (as SMILES or molecular graphs) and need to predict their elution order in RPLC systems with eluent pH around 2.

holobiomicslab Skill

Use when you have a high-resolution LC-MS/MS spectrum or pre-computed molecular fingerprint from a small-molecule sample and need to retrieve a systematic structural classification (compound class and subclass) with confidence estimates.

holobiomicslab Skill

Use when you have GC-MS data with multiple replicate injections or samples, need to identify a predefined set of query chemicals by name, and want to consolidate all instances of those chemicals (which may appear with varying match factors or peak areas across different samples or injection.

holobiomicslab Skill

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 biomolecular class assignment or CCS calculations.

holobiomicslab Skill

Use when after matching MRM transitions against a lipid reference database, when you have candidate lipid identities for each transition and need to rank them by quality and select a single match per transition for export to the labelled lipid-identity table.

holobiomicslab Skill

Use when when you have loaded a raw mass spectrum (e.g. ESI_NEG_SRFA.

holobiomicslab Skill

Use when you have two augmented versions of the same ion image (from mass spectrometry imaging data) and need to extract learnable 512-dimensional feature representations using a shared-weight encoder for contrastive loss optimization.

holobiomicslab Skill

Use when you have vendor-independent centroided mzML files from LC- or GC-HRMS data acquired in data-dependent acquisition (ddMS2) mode and need to extract detected features with m/z, retention time, and intensity attributes as input for non-target screening or PFAS prioritization workflows.

holobiomicslab Skill

Use when you have MS2 spectra data (MGF/mzML format) and aligned feature tables, and your analysis goal is to compare samples that may have poor MS1 feature overlap, strong retention-time shifts across runs, or were acquired on different LC-MS platforms.

holobiomicslab Skill

Use when when you have trained predictive models (e.g., neural networks) on paired microbiome-metabolome data and need to identify which metabolites are genuinely well-predicted above chance.

holobiomicslab Skill

Use when when you have CE-MS data with migration times that vary between runs due to electroosmotic flow drift, but you possess a reliable internal standard with a known effective mobility value.

holobiomicslab Skill

Use when after running MS1 extraction and prescreening on mzML files with assigned adducts and tags, when you need to inspect detected compounds visually to verify peak shape, confirm retention time consistency across samples (e.

holobiomicslab Skill

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 HDF5-based storage format for downstream software development, machine learning, or cross-platform data access.

holobiomicslab Skill

Use when implementing replacement methods ($<-, [<-, spectraData<-, mz<-, intensity<-, peaksData<-) for a writable MsBackend subclass, or when modifying peak data in an existing backend.

holobiomicslab Skill

Use when when you have raw mzML files from LC-MS/MS metabolomics experiments and need to convert them into a structured feature table with accurate mass, retention time, and MS2 spectral data linked to a reference compound list.

holobiomicslab Skill

Use when compiling or harmonizing MS/MS spectral libraries from multiple source repositories and you need to identify and remove spectra that fail quality thresholds (low resolution, precursor-fragment mass inconsistency, duplicate fragment patterns, noise-dominated, or missing critical metadata.

holobiomicslab Skill

Use when you have generated a peak table or feature list from MZmine, XCMS, MS-DIAL, or Compound Discoverer and need to ingest it into LipidMatch for lipid identification.

holobiomicslab Skill

Use when you have a pre-computed hierarchical dendrogram from correlation-based clustering of LC-MS features (with fixed linkage criterion and distance metric) and need to decide whether a single constant-threshold cut or data-driven silhouette optimization better resolves the underlying cluster.

holobiomicslab Skill

Use when you have a new fragmentation acquisition strategy (e.g., a weighted exclusion variant, alternative TopN ranking, or dynamic isolation window rule) that you wish to evaluate without access to real mass spectrometry hardware.

holobiomicslab Skill

Use when when you have raw LC-MS or LC-IMS-MS data in instrument format (Agilent .d, Thermo .raw, Bruker .

holobiomicslab Skill

Use when you have annotated metabolite structures (with SMILES strings) from a reference library (e.

holobiomicslab Skill

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 (single-core CPU, ≤16 GB RAM).

holobiomicslab Skill

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 which predicted RiPPs are actually expressed and modified in the sample.

holobiomicslab Skill

Use when you have normalized peak intensities or abundance matrices from mass spectrometry (e.

holobiomicslab Skill

Use when you have acquired raw MS/MS spectra (in MGF or mzML format) from a mass spectrometry instrument or public repository (e.g., MassIVE, MetaboLights, GNPS) that will be used for de novo chemical formula ranking or adduct assignment.

holobiomicslab Skill

Use when when you have centroided .mzML LC–MS runs and a target list (compound ID, theoretical m/z, expected RT, polarity) but are uncertain whether your m/z and RT windows are wide enough to capture all targets without false positives.

holobiomicslab Skill

Use when you have a conceptual MS/MS fragmentation strategy (e.