MOTA allows integrative analysis of multi-omics data for identification of disease-associated metabolites. An integrated differential network is built by merging the rewiring of intra-omics and inter-omics interactions based on differential partial correlation and differential canonical correlation, respectively, in disease vs. control groups. A MOTA activity score is calculated for each metabolite considering the changes in its levels and pairwise rewiring as a result of both intra-omics and inter-omics interactions.
Fan Z, Zhou Y, and Ressom HW (2020). MOTA: Network-based multi-omic data integration for biomarker discovery. Metabolites 10 (4): 10.3390/metabo10040144. PMID: 32276350
MetFID ranks putative metabolite IDs using an artificial neural network (ANN) that is trained to predict molecular fingerprints based on experimental MS/MS data. The method is particularly useful to rank putative IDs for analytes whose reference MS/MS spectra are not present in spectral libraries.
Fan Z, Alley A, Ghaffari, Ressom HW (2020). MetFID: Artificial neural network-based compound fingerprint prediction for metabolite annotation. Metabolomics. 2020 Sep 30;16(10):104. PMID: 32997169
MetID is a Bioconductor R-package for network-based ranking of putative metabolite IDs. It allows users to upload putative IDs and apply Bayesian approach with pathway and network information to rank putative IDs of metabolites. Metabolic pathway and network information are incorporated into the Bayesian framework to assign scores to putative IDs. The assumption is that a putative ID is more likely if other metabolites that are connected or are in close proximity to it are also detected in the same study. The formation of a dense cluster confirms that the analysis is inclined to assign higher probability scores to putative IDs that interact with others.
INDEED is a Bioconductor R-package that identifies biomolecules with significant changes on both individual and pairwise interaction levels. This is accomplished by using differential correlation to construct a differential network whose edges represent significant rewiring interactions between metabolite pairs. Each node represents a biomolecules whose activity score is calculated by combining its node degree with statistical significance of the feature in distinguishing disease from control.
Click here to download the R-package
Zuo Y, Cui Y, Di Poto C, Varghese RS, Yu G, Li R, and Ressom HW (2016). INDEED: Integrated differential expression and differential network analysis of omic data for biomarker discovery. Methods 111:12-20. PMID: 27592383
Li Z, Zuo Y, Xu C, Varghese RS, Ressom HW (2018). INDEED: R package for network based differential expression analysis. Proceedings (IEEE Int Conf Bioinformatics Biomed). 2018 Dec;2018:2709-2712. PMID: 31179159
SIMAT is a tool for analysis of GC-MS data acquired in SIM mode. The tool provides several functions to import raw GC-SIM-MS data and standard format mass spectral libraries. It also provides guidance for fragment selection before running the targeted experiment in SIM mode by using optimization. This is done by considering overlapping peaks from a library provided by the user. Other functionalities include retention index calibration to improve target identification and plotting EICs of individual peaks in specific runs which can be used for visual assessment.
Click here to download the R-package
Nezami Ranjbar MR, Di Poto C, Wang Y, and Ressom HW. "SIMAT: GC-SIM-MS data analysis tool," BMC Bioinformatics, 2015, 16:259. PMID: 26283310
MetaboSearch is a tool for metabolite identification by searching against four databases: Human Metabolome DataBase (HMDB), Madison Metabolomics Consortium Database (MMCD), METLIN Metabolomics Database, and LIPID Metabolites and Pathways Strategy (LIPID MAPS). The search results from these databases are integrated into a uniformly and non-redundant format based on IUPAC International Chemical Identifier (InChI) key.
Zhou B, Wang J, Ressom HW (2012). "MetaboSearch: Tool for mass-based metabolite identification using multiple databases." PLoS One 7(6): e40096. doi:10.1371/journal.pone.0040096. PMID: 22768229
DAta & Scripts
Tsai TH, Tadesse MG, Di Poto C, Pannell LK, Mechref Y, Wang Y, Ressom HW (2013).
"Multi-profile Bayesian alignment model for LC-MS data analysis with integration of internal standards.'' Bioinformatics 29(21):2774-80. PMID: 24013927
Tuli L, Tsai T-H, Varghese R, Xiao JF, Cheema A, and Ressom HW (2012).
"Using a spike-in experiment to evaluate analysis of LC-MS data. Proteome Science." Proteome Science 10(1):13.
Ressom HW, Varghese RS, Goldman L, Loffredo CA, Abdel-Hamid M, Kyselova Z, Mechref Y,
Novotny M, Goldman R (2008).
"Analysis of MALDI-TOF mass spectrometry data for detection of glycan biomarkers.'' Pacific Symposium on Biocomputing 13:216-227.
Ressom HW, Varghese RS, Drake SK, Hortin GL, Abdel-Hamid M, Loffredo CA,
and Goldman R (2007).
"Peak selection from MALDI-TOF mass spectra using ant colony optimization.'' Bioinformatics 23(5):619-626.
Goldman R, Ressom HW, Abdel-Hamid M, Goldman L, Wang A, Varghese RS, An Y,
Loffredo CA, Drake SK, Eissa SA, Gouda I, Ezzat S, Seillier-Moiseiwitsch F (2007). "Candidate markers for the detection of hepatocellular carcinoma in low molecular weight fraction of serum.''
Ressom HW, Varghese RS, Abdel-Hamid M, Abdel-Latif Eissa S, Saha D, Goldman L, Petricoin EF, Conrads TP, Veenstra TD, Loffredo CA, Goldman R (2005). "Analysis of mass spectral serum profiles for biomarker selection.'' Bioinformatics 21(21):4039-4045.