Coding & Dev Tools

holobiomicslab Skill

Use when when ingesting raw or vendor-format mass spectrometry data files of unknown or mixed acquisition modality, and you need to automatically determine whether the input originated from liquid chromatography (LC), gas chromatography (GC), ion mobility spectrometry (IMS), or MS imaging (e.

holobiomicslab Skill

Use when when you have a binned MS/MS spectrum vector (e.g., 9948-dimensional input from 10,000 equally-spaced m/z bins in the 10–1000 Da range) and need to compress it into a learned latent representation (e.

holobiomicslab Skill

Use when when you have raw strain correlation scores (or similar overlap-based metrics) computed across genomic cluster family (GCF) and molecular family (MF) pairs of varying sizes, and you need to make those scores comparable across links with different GCF sizes (#G), MF sizes (#m), and.

holobiomicslab Skill

Use when after LC-MS data has been converted to mzML format and processed through peak detection (e.g., MS-DIAL output) to yield a feature table with internal standard identifications, retention times, m/z values, and intensity measurements across multiple samples.

holobiomicslab Skill

Use when after identifying statistically significant features (e.

holobiomicslab Skill

Use when after generating a feature table from mzML data (via Asari) and before performing MS1 or MS2 annotation.

holobiomicslab Skill

Use when you have loaded raw MS intensity tables into QuantyFey and observe or suspect intensity drift artifacts across your measurement sequence. Drift is especially likely in long-running targeted MS experiments where calibration curves or internal standards show systematic variation over time.

holobiomicslab Skill

Use when gNPS has ceased supplying ClassyFire ontology information for spectral library matches, causing downstream ConCISE consensus classification to fail or produce incomplete ontology fields.

holobiomicslab Skill

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 for pathway enrichment. The skill is essential if your detection method has known sensitivity limits (e.

holobiomicslab Skill

Use when when you have PSM files from proteomics search engines (MaxQuant, MSGFPlus, Sage, etc.) that use non-standard modification notation (e.g., 'ox', '+57.02146', or mass-shift labels) and need to resccore peptide identifications with MS²Rescore.

holobiomicslab Skill

Use when when you have just loaded the rawrr R package and need to confirm that the bundled .NET 8.0 assembly (rawrr.exe) is present and functional before performing any mass spectrometry data extraction operations.

holobiomicslab Skill

Use when when raw MS files are uploaded to MSConnect via the Raw File Uploader and must be verified for compatibility with downstream processing tools (e.g., Proteomics_Data_Processor) before routing to the processing queue.

holobiomicslab Skill

Use when your lipidomics experiment includes spiked internal lipid standards with known absolute concentrations, and you have a data matrix of signal intensities (samples × lipids) from LipidSearch or LIQUID output.

holobiomicslab Skill

Use when when designing or optimizing backends that handle large MS datasets (mzML, mzXML, CDF files via MsBackendMzR), to verify that claimed memory advantages of on-disk or chunked approaches actually materialize in practice.

holobiomicslab Skill

Use when when you need to support multiple plotting library backends (static or interactive) for the same data visualization domain (e.

holobiomicslab Skill

Use when after normalizing, smoothing, and baseline-reducing mass spectra via normalize(), smooth(), and reduceBaseline(), when you need to distinguish true peaks from noise-induced artifacts.

holobiomicslab Skill

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 to specify the maximum allowed deviation (in ppm) between observed m/z values and calculated neutral masses.

holobiomicslab Skill

Use when after consolidating aligned LC-MS peaks into a quantitative feature table (with m/z, retention time, and intensity values across all samples), and before proceeding to statistical analysis or functional interpretation.

holobiomicslab Skill

Use when when you need to prototype, test, or benchmark MS1-only acquisition strategies on a defined set of metabolites (e.

holobiomicslab Skill

Use when converting raw MS/MS spectra from library files (e.g., .msp format) into structured library entries, or when annotating experimental LC–MS features against fragment databases.

holobiomicslab Skill

Use when when you have a tandem mass spectrum (MSMS) with known peptide sequence and wish to assess whether enabling neutral loss annotation (e.g., NH3: −17.026549, H2O: −18.010565) increases the proportion of observed m/z peaks that can be matched to predicted fragment ions.

holobiomicslab Skill

Use when after importing raw LC-MS/MS data files into the SIRIUS Java framework, before constructing indexed spectrum objects or submitting data to CSI:FingerID, CANOPUS, or MSNovelist web services.

holobiomicslab Skill

Use when you have metabolomics intensity data with peak annotations, and you want to rank and prioritize metabolite groupings (Molecular Families, Mass2Motifs, or other non-pathway metabolite sets) by their activity levels across experimental contrasts.

holobiomicslab Skill

Use when you have a Spectra object in R and need to apply a specialized Python MS algorithm (e.

holobiomicslab Skill

Use when you have two or more complementary scoring functions (e.g., strain correlation and IOKR scores) that you wish to combine, and you need to determine which combination strategy and parameters maximize enrichment of known true links in a validation set.

holobiomicslab Skill

Use when you have deconvolved GC-MS spectra (from overlapping chromatographic peaks) in MGF or mzTab format and want to group chemically related compounds, visualize their similarity relationships, and identify spectral families without prior library matching.

holobiomicslab Skill

Use when after running MetaMiner's Dereplicator stage to identify some RiPPs via direct database matching against a constructed structure database, apply this skill to enlarge the set of identifications by propagating those matches through spectral clusters and visualizing the connected components.

holobiomicslab Skill

Use when when you have extracted a raw Orbitrap scan from a .raw file and need to verify that the instrument operated within expected parameters and that observed peptide fragment ions rise substantially above noise—i.

holobiomicslab Skill

Use when you have vendor mass spectrometry raw files (e.g., .raw, .d, .

holobiomicslab Skill

Use when you have generated a peak table or feature list output file from an external peak-picking tool (MZmine, XCMS, MS-DIAL, or Compound Discoverer) in its native export format and need to ingest it into LipidMatch for lipid identification without manual column remapping or format conversion.

holobiomicslab Skill

Use when when running a ViMMS Environment simulation with save_eval flag enabled and you need to correlate fragmentation events in the output mzML file back to their originating chemical compounds for downstream evaluation, optimization, or validation of acquisition strategies.

holobiomicslab Skill

Use when apply CLR transformation when you have microbiome or metabolomic count data that sums to a constant across samples (relative abundance or compositional data) and intend to train supervised or unsupervised machine learning models (especially neural networks) that assume unbounded, linear.

holobiomicslab Skill

Use when you have a collection of deconvolved mass spectra (in MGF or mzTab format) from GC-MS analysis and need to group them into a molecular network to identify structural relationships and enable compound annotation.

holobiomicslab Skill

Use when you have a feature quantification table exported from MZmine3 processing of non-targeted LC-MS/MS data that includes both biological samples and blank/control samples, and you need to identify and exclude features whose intensity is driven by contamination in blanks rather than true.

holobiomicslab Skill

Use when when you have raw LC-MS/MS DDA spectral data (positive and/or negative ionization modes) paired with sample metadata (originating taxon), and you need to detect molecular features, build a molecular network from fragmentation spectra, and annotate those features using both spectral.

holobiomicslab Skill

Use when you have high-dimensional TWIM-MS data (arrival time and m/z dimensions) from a multi-omic sample and need to associate experimental features with biomolecular classes *before* running peak detection or feature identification pipelines.