# Chemical Shift Annotation > Use when when you have preprocessed 1H NMR spectral data with unidentified peaks and need to determine metabolite identity by exploiting the correlation structure of NMR signals. - Skill: `holobiomicslab/chemical-shift-annotation-2` (Agent Skill) - Install (CLI): `npx skillmds@latest add holobiomicslab/chemical-shift-annotation-2` - Raw SKILL.md: https://api.skillmd.com/api/skills/holobiomicslab/chemical-shift-annotation-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/chemical-shift-annotation-2 --- # chemical-shift-annotation > **License: restricted** — no clear open-source license detected for the underlying tool; verify licensing before commercial use or redistribution. ## Summary Use Statistical Total Correlation Spectroscopy (STOCSY) to assign chemical identity to unknown NMR signals by analyzing their covariance and correlation patterns across a reference spectrum. This workflow converts ambiguous δ shifts into metabolite assignments by identifying characteristic multiplet patterns. ## When to use When you have preprocessed 1H NMR spectral data with unidentified peaks and need to determine metabolite identity by exploiting the correlation structure of NMR signals. Specifically, when a driver signal (known or suspected metabolite at a specific ppm value) shows strong covariance with other peaks in the spectrum, STOCSY can reveal whether those peaks belong to the same metabolite based on their J-coupling and chemical shift relationships. ## When NOT to use - Raw, unphased NMR spectra or data not baseline-corrected (preprocessing in TopSpin or equivalent is required before STOCSY) - Metabolites lacking strong multiplet structure or J-coupling patterns (STOCSY relies on covariance across related spins) - Highly overlapped spectral regions where multiple metabolites share the same chemical shift range and cannot be resolved by correlation alone ## Inputs - Preprocessed 1H NMR spectral data (Bioconductor metabo_SE object or compatible matrix/data frame with ppm chemical shift columns and intensity rows) - Driver signal chemical shift value (ppm, e.g., 1.04) - Known or suspected metabolite reference library with expected multiplet patterns and chemical shifts ## Outputs - STOCSY correlation profile (numeric vector of correlation coefficients indexed by ppm) - Annotated STOCSY plot with highlighted covariant peaks and metabolite assignments - Correlation data matrix linking driver signal to all other chemical shifts - Metabolite identity assignment with confidence support from J-coupling pattern matching ## How to apply Load preprocessed NMR spectral data (in the format accepted by Bioconductor's metabo_SE object) into R with Bioconductor installed. Apply the STOCSY function from MWASTools with the query ppm parameter set to the chemical shift of your driver signal (e.g., δ 1.04 for the BMI-associated valine signal). The function generates a correlation profile across all chemical shifts; extract and visualize correlation coefficients to identify peaks showing significant covariance. Cross-reference highlighted multiplets against known metabolite chemical shift signatures from NMR literature (e.g., valine doublets at δ 1.04 and δ 0.99). A successful assignment occurs when the STOCSY correlation pattern matches the expected J-coupling fine structure and chemical shift spacing of a known metabolite. ## Related tools - **Statistical Total Correlation Spectroscopy (STOCSY)** (Core algorithm that computes correlation coefficients between driver signal intensity and all other chemical shifts to reveal covariant metabolite signatures) - **MWASTools** (R/Bioconductor package providing STOCSY implementation, preprocessing infrastructure, and visualization for metabolite-phenotype association workflows) — https://github.com/AndreaRMICL/MWASTools - **R** (Programming environment for loading, manipulating, and running STOCSY analysis on NMR spectral objects) - **Bioconductor** (Data structure (metabo_SE) and computational framework for managing high-dimensional NMR spectral matrices and annotation metadata) - **TopSpin 3.2** (Upstream NMR data processing software for phasing and baseline correction required before STOCSY analysis) ## Evaluation signals - Highlighted doublets or multiplets in the STOCSY output appear at expected chemical shifts for the assigned metabolite (e.g., valine at δ 1.04 and δ 0.99) - Correlation coefficients at covariant peaks are statistically significant (high magnitude, consistent across independent samples or spectral replicates) - J-coupling splitting patterns and chemical shift spacing match literature values for the proposed metabolite identity - Cross-validation: driver signal and assigned peaks show consistent covariance when STOCSY is rerun with one of the highlighted peaks as an alternative driver - No conflicting high-correlation peaks from unrelated metabolites appear in the STOCSY profile ## Limitations - STOCSY requires strong J-coupling and covariance structure; highly isolated singlets or closely overlapped multiplets may not yield unambiguous assignments - Assignment confidence depends on the quality and completeness of the reference chemical shift library; novel or rare metabolites may not be reliably identified - Baseline distortions or residual phase errors in the preprocessed NMR data can reduce correlation signal and lead to missed or false assignments - STOCSY identifies only correlations; it does not distinguish between structurally similar metabolites with nearly identical chemical shifts and J-coupling patterns without additional MS or 2D NMR data ## Evidence - [other] STOCSY analysis using δ 1.04 as the driver signal identified two highlighted doublets at δ 1.04 and δ 0.99, indicating the unknown signal corresponds to valine.: "STOCSY analysis using δ 1.04 as the driver signal identified two highlighted doublets at δ 1.04 and δ 0.99, indicating the unknown signal corresponds to valine" - [other] Can STOCSY analysis of NMR spectral data identify the chemical identity of unknown metabolic features by their covariance and correlation patterns with a driver signal?: "STOCSY analysis of NMR spectral data identify the chemical identity of unknown metabolic features by their covariance and correlation patterns with a driver signal" - [other] Apply STOCSY (Statistical Total Correlation Spectroscopy) function with query ppm parameter set to 1.04 to generate a correlation profile across all chemical shifts.: "Apply STOCSY (Statistical Total Correlation Spectroscopy) function with query ppm parameter set to 1.04 to generate a correlation profile across all chemical shifts" - [intro] metabolite assignment using Statistical Total Correlation Spectroscopy (STOCSY): "metabolite assignment using Statistical Total Correlation Spectroscopy (STOCSY)" - [intro] Following phasing and baseline correction in TopSpin 3.2 software: "Following phasing and baseline correction in TopSpin 3.2 software"