ADAP: Automated Data Analysis Pipeline for untargeted metabolomics

  1. ADAP-GC 3.0: workflow for preprocessing untargeted GC-MS metabolomics data. Released in June 2016
  2. ADAP-GC is an automated computational pipeline for untargeted, GC-MS-based metabolomics studies. It takes raw mass spectrometry data as input and carries out a sequence of data processing steps including construction of extracted ion chromatograms, detection of chromatographic peak features, deconvolution of co-eluting compounds, and alignment of compounds across samples. Despite the increased accuracy from the original version to version 2.0 in terms of extracting metabolite information for identification and quantitation, ADAP-GC 2.0 requires appropriate specification of a number of parameters and has difficulty in extracting information of compounds that are in low concentration. To overcome these two limitations, ADAP-GC 3.0 was developed to improve both the robustness and sensitivity of compound detection.

    The algorithms in ADAP-GC 3.0 was developed primarily in R. The code is open source and can be found HERE on Github.

    Relevant publications can be found here: ADAP-GC 1.0, ADAP-GC 2.0, and ADAP-GC 3.0.

  3. ADAP 3.1: new algorithms for construction of EICs and detection of EIC peaks. Released on March 31, 2017
  4. False positive and false negative peaks detected from extracted ion chromatograms (EIC) are an urgent problem with existing software packages that preprocess untargeted liquid or gas chromatography-mass spectrometry metabolomics data because they can translate downstream into spurious or missing compound identifications. We have developed new algorithms that carry out the sequential construction of EICs and detection of EIC peaks. Initial comparison of the new algorithms to two popular software packages XCMS and MZmine 2 showed that these new algorithms detect significantly fewer false positives. Regarding the detection of compounds known to be present in the data, the new algorithms perform at least as well as XCMS and MZmine 2.

    The algorithms were written in Java and have been incorporated into MZmine 2.24. MZmine 2.24 can be downloaded on Github. The static ADAP 3.1 can be found HERE on Github as well.

  5. ADAP 3.2: improved algorithms for spectral deconvolution of GC-MS metabolomics data. Released on May 6, 2017
  6. ADAP 3.2 features an improved spectral deconvolution, compared to the original ADAP-GC 3.0 spectral deconvolution algorithm written in R. In addition, ADAP 3.2 is coded in Java and has been incorporated into MZmine 2.25. ADAP 3.2 can be downloaded from Github. The static ADAP 3.2 can be found HERE on Github as well.

  7. ADAP User Manual
  8. ADAP User Manual can be downloaded by clicking THIS link. It can also be downloaded from Github.


Xlink-Identifier is a C++ program for identifying chemical crosslinks from tandem mass spectra. The source code can be accessed HERE on Github. Relevant publications can be found here: paper 1, paper 2.