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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

ProbMetab: an R package for Bayesian probabilistic annotation of LC-MS-based metabolomics

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Silva, Ricardo R. [1] ; Jourdan, Fabien [2, 3, 4] ; Salvanha, Diego M. [5, 1] ; Letisse, Fabien [2, 3, 4, 6] ; Jamin, Emilien L. [2, 3, 4] ; Guidetti-Gonzalez, Simone [7] ; Labate, Carlos A. [7, 8] ; Vencio, Ricardo Z. N. [1]
Total Authors: 8
[1] Univ Sao Paulo, Dept Comp & Math FFCLRP USP, LabPIB, BR-14049 Ribeirao Preto - Brazil
[2] INRA, UMR1331, Toxalim, Res Ctr Food Toxicol, F-31931 Toulouse - France
[3] Univ Toulouse, INSA, UPS, INP, Toulouse - France
[4] LISBP, Toulouse - France
[5] Inst Syst Biol, Seattle, WA - USA
[6] CNRS, UMR5504, Toulouse - France
[7] Univ Sao Paulo, Dept Genet ESALQ USP, Piracicaba - Brazil
[8] Lab Nacl Ciencia & Tecnol Bioetanol CTBE, Campinas, SP - Brazil
Total Affiliations: 8
Document type: Journal article
Source: Bioinformatics; v. 30, n. 9, p. 1336-1337, MAY 1 2014.
Web of Science Citations: 25

We present ProbMetab, an R package that promotes substantial improvement in automatic probabilistic liquid chromatography-mass spectrometry-based metabolome annotation. The inference engine core is based on a Bayesian model implemented to (i) allow diverse source of experimental data and metadata to be systematically incorporated into the model with alternative ways to calculate the likelihood function and (ii) allow sensitive selection of biologically meaningful biochemical reaction databases as Dirichletcategorical prior distribution. Additionally, to ensure result interpretation by system biologists, we display the annotation in a network where observed mass peaks are connected if their candidate metabolites are substrate/ product of known biochemical reactions. This graph can be overlaid with other graph-based analysis, such as partial correlation networks, in a visualization scheme exported to Cytoscape, with web and stand-alone versions. (AU)

FAPESP's process: 09/53161-6 - Information technology applied to bioenergy genomics: probabilistic annotation using artificial intelligence
Grantee:Ricardo Zorzetto Nicoliello Vêncio
Support Opportunities: Program for Research on Bioenergy (BIOEN) - Regular Program Grants