Ms2 Spectrum Feature Extraction

MS2-spectrum-feature-extraction

Summary

Extract metabolic features directly from MS2 (tandem mass spectrometry) spectra using the JPA MS2 recognition module, generating a feature table with m/z, retention time, and intensity—an alternative to MS1 peak picking when MS2 spectral data is available and full-scan or DIA datasets are not being processed.

When to use

You have DDA (data-dependent acquisition) LC-MS/MS data with MS2 spectra and want to discover metabolic features that may be missed by MS1-only peak picking, or when you need an alternative feature extraction workflow that leverages fragmentation patterns to identify true metabolite signals.

When NOT to use

• Input is full-scan or DIA (data-independent acquisition) data — the README explicitly states 'Please do not use this function when processing full-scan or DIA data set'
• Only MS1 peak-picked features are available and no MS2 spectral data is present
• Feature extraction goal is to reproduce results from a published MS1-only pipeline without MS2 validation

Inputs

• mzXML files from DDA LC-MS/MS analysis (one or multiple files in a single folder)
• Existing MS1 peak-picked feature table (optional; CSV format with columns: m/z, retention time, rtmin, rtmax, intensity)
• Raw LC-MS/MS data in vendor format convertible via MS-Convert

Outputs

• Feature table (dataframe) with columns: mz, rt, rtmin, rtmax, maxo (intensity), sample, level (labeled 'MR')
• Combined feature table integrating both MS1 PP and MS2 MR features
• Structured output file for downstream alignment and annotation workflows

How to apply

Load raw mzXML files or an existing MS1 peak-picked feature table into JPA using R. Execute the MS2 recognition module to identify features from MS2 spectra by comparing spectral patterns and intensity thresholds. A threshold for determining true positive MR (MS2 recognition) features should be estimated using the provided thresholdEstimate.R code on your dataset to tune sensitivity. The module outputs a feature table with m/z, retention time (rtmin, rtmax), and intensity columns, labeled with 'MR' level designation. These MR features are then combined with any existing PP (peak picking) features for downstream alignment and annotation.

Related tools

• JPA (Comprehensive metabolomics data processing software providing MS2 recognition module for feature extraction from tandem MS spectra) — https://github.com/HuanLab/JPA.git
• R (Programming language in which JPA is written and executed; R ≥ 4.0.0 required)
• XCMS (Embedded algorithm used by JPA for peak picking on MS1 or custom feature tables; referenced for parameter details) — https://rdrr.io/bioc/xcms/man/
• thresholdEstimate.R (Provided R code to estimate the threshold for determining whether MS2 recognition features are true positives on your specific dataset) — https://github.com/HuanLab/JPA

Examples

# After installing JPA and loading demo data:
library(JPA)
dir <- "X:/Users/JPAtest_20210330/multiDDA"
featureTable_PP <- XCMS.featureTable(dir = dir, mz.tol = 10, ppm=10, peakwidth=c(5,20), mzdiff = 0.01, snthresh = 6, integrate = 1, prefilter = c(3,100), noise = 100)
featureTable_combined <- MS2recognition(featureTable = featureTable_PP, dir = dir)

Evaluation signals

• Output feature table contains all required columns (mz, rt, rtmin, rtmax, maxo, sample, level) with no missing values in retention time or m/z fields
• All extracted features are labeled with 'MR' level designation in the 'level' column
• MS2 MR feature m/z values fall within expected range (typically 50–1500 m/z for small-molecule metabolomics)
• MS2 MR features with intensity (maxo) above the user-estimated threshold show agreement with manual MS2 spectral inspection or orthogonal validation
• Combined PP + MR feature table shows a superset of features compared to MS1 peak picking alone, validating that MS2 recognition identified additional true metabolites

Limitations

• MS2 recognition should not be applied to full-scan or DIA datasets, as MS2 spectral topology differs fundamentally from DDA fragmentation patterns
• True positive threshold must be empirically estimated per dataset using thresholdEstimate.R; a universal cutoff does not exist and suboptimal thresholds lead to false positives or false negatives
• MS2 recognition produces feature-level annotations only; subsequent adduct and metabolite identification require separate CAMERA and MS2 annotation workflows
• Performance depends on MS2 spectral quality and coverage; samples with poor fragmentation or low MS2 scan density may yield sparse MR feature tables

Evidence

• [readme] After PP features have been extracted, extracting features using MS2 recognition (labeled as "MR") can be performed. This step is optional. Please do not use this function when processing full-scan or DIA data set!: "Please do not use this function when processing full-scan or DIA data set!"
• [other] JPA provides MS2 recognition as a distinct feature extraction workflow component, positioned separately from MS1 peak picking, to generate feature tables through alternative analytical pathways.: "JPA provides MS2 recognition as a distinct feature extraction workflow component, positioned separately from MS1 peak picking"
• [readme] The threshold used for determining whether the MR features are true positive can be estimated by using the R code "thresholdEstimate.R" provided on the GitHub.: "The threshold used for determining whether the MR features are true positive can be estimated by using the R code "thresholdEstimate.R""
• [intro] JPA extracts both Gaussian and non-Gaussian shaped metabolic features. It also performs sample alignment, adduct and metabolite annotations.: "JPA extracts both Gaussian and non-Gaussian shaped metabolic features"
• [readme] One or multiple mzXML files from LC-MS analyses can be read in and processed to extract MS1 PP features. 'JPA' accepts raw data in any vendor format that is compatible with MS-Convert.: "'JPA' accepts raw data in any vendor format that is compatible with MS-Convert"