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This module is currently under maintenance

ADAP Aligner (GC)


Feature list methods → Alignment → ADAP aligner (GC)

This alignment algorithm has been developed as part of ADAP-GC v1.0, Automatic Data Analysis Pipeline for processing GC-MS metabolomics data.

ADAP Aligner aligns features based on their mass spectra and retention time similarity. Unlike Join Aligner (which aligns peaks across all samples, using their m/z and retention time similarity), ADAP Aligner uses mass spectra and retention time to detect similar features in each sample and align them together. In fact, this algorithm is similar to Hierarchical Aligner (GC), but it uses a different clustering method.

Similarity between two features \(f1\) and \(f2\) is calculated by the following score:

\[S(f1, f2) = w*S_{time}(f1, f2) + (1 - w)*S_{spec}(f1, f2)\]

where \(S_{time}(f1, f2)\) is the relative retention time difference between two features, \(S_{spec}(f1, f2)\) is the spectrum similarity between two features.

For more details, see reference [1].


ADAP Aligner requires mass spectra to be constructed prior to the alignment (e.g. using Spectral Deconvolution).

A typical workflow can be as following:

  • "Raw data methods → Raw data import" to import raw data files
  • "Raw data methods → Mass detection" to detect masses in the raw data

  • "Feature detection → ADAP Chromatogram builder" to build extracted-ion chromatograms

  • "Feature detection → Chromatogram resolving → ADAP Resolver" to detect peaks (features) in each chromatogram
  • "Feature list methods → Spectral deconvolution (GC) → Multivariate Curve Resolution" to combine the detected peaks (features) into analytes and builds pure fragmentation mass spectra for each analyte
  • "Feature list methods → Alignment → ADAP Aligner (GC)" to align the analytes produced by the previous step
  • "Feature list methods → Export feature list → MSP file (ADAP)" to export fragmentation mass spectra into MSP format


  1. Jiang, W.; Qiu, Y.; Ni, Y.; Su, M.; Jia, W.; Du, X.: An automated data analysis pipeline for GC-TOF-MS metabonomics studies. Journal of proteome research 2010, 9 (11), 5974-81. DOI: 10.1021/pr1007703


Min confidence (between 0 and 1)

A fraction of the total number of samples. An aligned feature must be detected at least in several samples. This parameter determines the minimum number of samples where a feature must be detected. The default value is 0.7, so an aligned feature must be observed at least in 70% of all samples.

Retention time tolerance

The maximum allowed retention time difference between aligned features from different samples.

m/z tolerance

The maximum m/z difference, when two peaks from different mass spectra are considered equal.

Score threshold (between 0 and 1)

The minimum value of the similarity function \(S(f1, f2)\) required for features to be aligned together. The default value is 0.75.

Score weight (between 0 and 1)

The weight \(w\) that is used in the similarity function \(S(f1, f2)\). The default value is 0.1.

Retention time similarity

A method used for calculating the retention time similarity. The retention time difference (fast) is preferred method.

ansgarkorf, omokshyna

Last update: June 18, 2024 11:26:19