# Peak Boundary Definition And Validation > Use when after nontargeted peak detection has identified candidate peaks in LC-MS chromatograms, when you need to establish exact peak start/end retention times and extract peak-level metadata (intensity, width, shape) for downstream feature quality evaluation and annotation workflows. - Skill: `holobiomicslab/peak-boundary-definition-and-validation-2` (Agent Skill) - Install (CLI): `npx skillmds@latest add holobiomicslab/peak-boundary-definition-and-validation-2` - Raw SKILL.md: https://api.skillmd.com/api/skills/holobiomicslab/peak-boundary-definition-and-validation-2/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - License: CC-BY-4.0 - Author: HolobiomicsLab (https://skillmd.com/u/holobiomicslab) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/holobiomicslab/peak-boundary-definition-and-validation-2 --- # peak-boundary-definition-and-validation > **License: noncommercial** — confirm your use is a permitted (noncommercial) purpose before applying; commercial use requires a separate license (see `metadata.tool_license`). ## Summary Define and validate precise chromatographic peak boundaries in LC-MS data to extract quantitatively accurate peak characteristics (m/z, retention time, intensity, peak width). This skill ensures that detected peaks are correctly segmented and quality-evaluated before downstream feature annotation and metabolite identification. ## When to use Apply this skill after nontargeted peak detection has identified candidate peaks in LC-MS chromatograms, when you need to establish exact peak start/end retention times and extract peak-level metadata (intensity, width, shape) for downstream feature quality evaluation and annotation workflows. Use it when raw peak detection outputs require refinement to eliminate false positives or overlapping signals. ## When NOT to use - Input is already a curated, pre-segmented feature table (e.g., from vendor software or a prior processing run); re-segmentation risks losing validated boundaries. - Peak detection has not yet been performed; apply nontargeted peak detection first. - Data contains only targeted (SRM/MRM) acquisitions rather than full-scan LC-MS; targeted methods do not require nontargeted segmentation. ## Inputs - raw LC-MS data in mzML format - raw LC-MS data in vendor format - peak detection results (retention time coordinates, m/z values, intensity estimates) ## Outputs - feature table in CSV format with detected peaks and metadata - feature table in feather format with detected peaks and metadata - peak characteristics: m/z, retention time, intensity, peak width - quality metrics per peak ## How to apply Load the raw LC-MS data (mzML or vendor format) and peak detection results into MassCube. Apply the peak segmentation algorithm to define precise peak boundaries by analyzing chromatographic intensity profiles and m/z trajectories. For each detected peak, extract and validate the following characteristics: m/z value, retention time at peak apex, peak intensity (height or area), and peak width at baseline or half-height. Generate a feature table (CSV or feather format) containing all detected peaks with their metadata and quality metrics. Validate segmentation accuracy by visual inspection of a subset of peaks (e.g., comparing detected boundaries against raw chromatograms) and by checking that peak widths and intensities fall within biologically plausible ranges for the instrument and method. ## Related tools - **masscube** (performs peak segmentation, extracts peak boundaries and characteristics, generates validated feature table with quality metrics) — https://github.com/huaxuyu/masscube/ - **Python** (runtime environment for masscube package execution and custom validation workflows) ## Evaluation signals - Feature table contains non-null, physically plausible values for m/z (within expected mass range), retention time (within data acquisition window), intensity (positive values), and peak width (consistent with chromatographic resolution). - Peak count and intensity distribution match or exceed those from vendor software or published benchmarks for the same dataset. - Manual spot-check of 5–10 peaks shows that detected boundaries align visually with chromatographic peaks in the raw LC-MS image and do not overlap incorrectly with adjacent peaks. - Quality metrics (e.g., peak shape scores, signal-to-noise ratios if calculated) show expected distributions and correlate inversely with known artifact peaks. - Export schema is valid (all rows have matching column counts, data types match specification) and file format (CSV or feather) is readable by downstream tools. ## Limitations - Highly accurate peak segmentation depends on appropriate tuning of detection algorithm parameters (e.g., smoothing window, intensity threshold) for the specific LC-MS instrument, column, and metabolite class; parameter transfer across methods may degrade accuracy. - Overlapping or co-eluting peaks may not be resolved correctly; manual review or complementary high-resolution MS/MS data may be required. - No changelog is available in the repository, limiting traceability of algorithm changes across software versions. ## Evidence - [other] Apply nontargeted peak detection algorithm to identify chromatographic peaks across the full retention time range. 3. Perform peak segmentation to define precise peak boundaries and extract peak characteristics (m/z, retention time, intensity, peak width).: "Perform peak segmentation to define precise peak boundaries and extract peak characteristics (m/z, retention time, intensity, peak width)." - [other] Generate and export feature table in tabular format (CSV or feather) containing detected peaks with their metadata and quality metrics.: "Generate and export feature table in tabular format (CSV or feather) containing detected peaks with their metadata and quality metrics." - [readme] Highly accurate nontargeted peak detection and segmentation.: "Highly accurate nontargeted peak detection and segmentation." - [readme] Comprehensive feature quality evaluation.: "Comprehensive feature quality evaluation." - [readme] masscube is an integrated Python package for liquid chromatography-mass spectrometry (LC-MS) data processing.: "masscube is an integrated Python package for liquid chromatography-mass spectrometry (LC-MS) data processing."