Table 1.

Summary of software for analyzing plant metabolic data.

Software	Description	Compatibility	Language	Reference
XCMS	data preprocessing, alignment, and quantitation; but it is time-consuming for it to process large-scale datasets	LC-MS, GC-MS	R	Smith et al. (2006); Tautenhahn et al. (2012)
MetAlign	data preprocessing, alignment, and quantitation; but it is time-consuming for it to process large-scale datasets	LC-MS, GC-MS	C	Lommen (2009)
Mzmine	distributed computing algorithm-based peak alignment and multiple visualization modules are available for data visualization	LC-MS, GC-MS	Java	Katajamaa et al. (2006); Pluskal et al. (2010)
AMDORAP	accurate m/z detection with the m/z errors within ±3 ppm	LC-MS	R	Takahashi et al. (2011)
MAIT	comprehensive statistical analysis tool for LC-MS metabolic data, but the data normalization is not included	LC-MS	R	Fernández-Albert et al. (2014)
OpenMS	hundreds of workflows are available for data processing, and a highly flexible and professional software environment is provided for users	LC-MS	C++	Röst et al. (2016)
metaX	a comprehensive workflow for untargeted metabolomics data, including data preprocessing, metabolites identification, pathway annotation, and biomarker selection	LC-MS, GC-MS	R	Wen et al. (2017)
ROIMCR	ROI-based peak detection and integration, and an MCR-ALS method is used to resolve peaks from mixture	LC-MS	MATLAB	Gorrochategui et al. (2019)
MetaboAnalyst	a powerful platform for metabolomics data analysis, including enrichment analysis, pathway analysis, and statistical analysis; however, the original data need to be converted and aligned by other software	LC-MS, GC-MS, NMR	Java, R	Xia et al. (2009)
MAVEN	machine learning-based peak quality assessment, pathway, and isotope-labeling visualization	LC-MS	–	Melamud et al. (2010)
apLCMS	a hybrid feature detection approach is used to reduce false-positive and false-negative peaks, but a known-feature database is needed	LC-MS	R	Yu et al. (2009); Yu et al. (2013)
MS-FLO	retention time alignment, accurate mass tolerances, peak height similarity, and Pearson’s correlation analysis-based methods to minimize false-positive peaks	LC-MS	Python	DeFelice et al. (2017)
rFPF	an EIC profile-based method to remove false-positive features	LC-MS	MATLAB	Ju et al. (2019)
Peakonly	precise peak detection using a convolutional neural network-based deep learning method	LC-MS	Python	Melnikov et al. (2020)
AMDIS	data deconvolution; without the function of peak alignment	GC-MS	–	Halket et al. (1999); Stein (1999)
ChromaTOF	GC-TOF-MS data deconvolution; without published algorithm descriptions	GC-MS	–	–
MetaQuant	target metabolome analysis, but an established library is required	GC-MS	Java	Bunk et al. (2006)
MET-IDEA	target metabolome analysis, but a list containing m/z and retention time pairs is required	GC-MS	–	Broeckling et al. (2006); Lei et al. (2012)
TagFinder	peak alignment; without the function of baseline correction and peak smooth	GC-MS	Java	Luedemann et al. (2008)
MetaboliteDetector	data deconvolution and peak alignment based on a QT4 graphical user interface	GC-MS	C++	Hiller et al. (2009)
ADAP	data deconvolution and peak alignment using a two-phase approach	GC-MS	C++, R	Jiang et al. (2010); Ni et al. (2012); Ni et al. (2016); Smirnov et al. (2019)
MS-DIAL	data deconvolution, peak alignment, and annotation	GC-MS	C	Tsugawa et al. (2015);
eRah	peak deconvolution and alignment	GC-MS	R	Domingo-Almenara et al. (2016)
IP4M	62 independent functions for data preprocessing, peak annotation, and pathway enrichment analysis	LC-MS, GC-MS	Java, Perl, R	Liang et al. (2020)
autoGCMSDataAnal	TIC peak detection and resolution using raw data; dynamic programming algorithm-based retention time-shift correction	GC-MS	MATLAB	Zhang et al. (2020)
QPMASS	large-scale metabolic data analysis (alignment, backfill, and quantitation)	GC-MS	C++	Duan et al. (2020)