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.
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.
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.