# Ms Ms Spectrum Annotation Preprocessing > 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. - Skill: `holobiomicslab/ms-ms-spectrum-annotation-preprocessing-2` (Agent Skill) - Install (CLI): `npx skillmds@latest add holobiomicslab/ms-ms-spectrum-annotation-preprocessing-2` - Raw SKILL.md: https://api.skillmd.com/api/skills/holobiomicslab/ms-ms-spectrum-annotation-preprocessing-2/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - License: CC-BY-4.0 - Author: HolobiomicsLab (https://skillmd.com/u/holobiomicslab) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/holobiomicslab/ms-ms-spectrum-annotation-preprocessing-2 --- # MS/MS Spectrum Annotation Preprocessing > **License: restricted** — no clear open-source license detected for the underlying tool; verify licensing before commercial use or redistribution. ## Summary Prepare and annotate MS/MS spectra from LC-MS/MS acquisitions in DDA mode by curating training datasets with purity labels to enable downstream chimeric spectrum purification and customized model training. This skill bridges raw spectral acquisition and machine-learning-ready training sets. ## When to use 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. Specifically, when you want to train DNMS2Purifier or similar spectrum-quality classifiers on your own instrument, sample matrix, or experimental conditions rather than relying on pre-trained models. ## When NOT to use - Your input spectra are already pre-processed, quality-filtered, or de-chimeric from another tool — this skill assumes raw or minimally processed spectra requiring annotation. - You are applying an existing pre-trained DNMS2Purifier model to new data without retraining — use the main DNMS2Purifier.r purification script directly instead. - Your MS/MS data was acquired in targeted or Selected Reaction Monitoring (SRM) mode rather than Data-Dependent Acquisition (DDA) mode. ## Inputs - Raw MS/MS spectra from LC-MS/MS DDA acquisitions - Annotated purity labels or quality scores for each spectrum - Structured training data (data frame or matrix) containing spectral features and annotations ## Outputs - Serialized trained purification model (.rds file) - Customized model object compatible with DNMS2Purifier.r workflow ## How to apply Collect MS/MS spectra from LC-MS/MS acquisitions performed in DDA mode, ensuring representative coverage of your experimental conditions and sample matrices. Manually or semi-automatically annotate each spectrum with a purity label (typically binary: pure or chimeric, or continuous quality scores) based on visual inspection, spectral quality metrics, or ground-truth tandem MS interpretation. Organize the annotated spectra and their purity labels into a structured dataset (e.g., a data frame with spectral features and labels). Load this training dataset into the DNMS2Purifier_model_generation.r script running under R 4.2.1, which will train a customized purification model on the annotated data. The trained model is serialized and saved as an .rds file for integration into the main DNMS2Purifier.r workflow. ## Related tools - **DNMS2Purifier_model_generation.r** (R script that trains the customized purification model on annotated training data and serializes the model to .rds format) — https://github.com/HuanLab/DNMS2Purifier - **R** (Runtime environment (version 4.2.1 required) for executing model training and serialization) - **DNMS2Purifier.r** (Main purification script that consumes the trained model .rds file for MS/MS spectrum purification) — https://github.com/HuanLab/DNMS2Purifier ## Examples ``` # Load training data with annotated MS/MS spectra and purity labels, then execute: Rscript DNMS2Purifier_model_generation.R --input training_data.csv --output customized_model.rds ``` ## Evaluation signals - Training dataset contains annotated MS/MS spectra with corresponding purity labels; no missing or mismatched entries between spectra and labels. - Trained model .rds file is successfully serialized and can be loaded into R 4.2.1 without errors. - Model object contains expected structure and parameters (e.g., feature weights, classification thresholds) compatible with DNMS2Purifier.r intake. - When the trained model is applied to held-out test spectra, classification accuracy, sensitivity, and specificity meet domain expectations for chimeric vs. pure spectrum discrimination. - Training data distribution reflects the instrument, sample matrix, and ionization conditions of the target application (e.g., representative sample diversity, ion abundance ranges). ## Limitations - Annotation quality and completeness directly affect model performance; subjective or incomplete purity labeling will propagate as classification bias. - Training dataset must be sufficiently large and representative to avoid overfitting; no minimum sample size is specified in the documentation. - Model trained on one LC-MS/MS platform or sample matrix may not generalize well to different instruments, ion sources, or chemical spaces without retraining. - No changelog or version history is publicly available, limiting reproducibility and tracking of model training updates. - The skill is specific to the DNMS2Purifier framework; compatibility with other spectrum purification tools or formats is not addressed. ## Evidence - [other] Prepare training data containing annotated MS/MS spectra and purity labels from LC-MS/MS acquisitions in DDA mode.: "Prepare training data containing annotated MS/MS spectra and purity labels from LC-MS/MS acquisitions in DDA mode." - [other] Execute the model generation script to train the customized purification model on the prepared training dataset.: "Execute the model generation script to train the customized purification model on the prepared training dataset." - [other] Serialize and save the trained model object to an .rds file for downstream use in the DNMS2Purifier.r purification workflow.: "Serialize and save the trained model object to an .rds file for downstream use in the DNMS2Purifier.r purification workflow." - [readme] The program is written in R (ver 4.2.1).: "The program is written in R (ver 4.2.1)." - [readme] DNMS2Purifier is a bioinformatic solution that purifies chimeric MS/MS spectra from LC-MS/MS-based untargeted metabolomics in Data-Dependent Acquisition (DDA) mode.: "DNMS2Purifier is a bioinformatic solution that purifies chimeric MS/MS spectra from LC-MS/MS-based untargeted metabolomics in Data-Dependent Acquisition (DDA) mode."