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

Autor(es)

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]

Afiliação do(s) autor(es)

^[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

• Tipo de documento: Artigo Científico
• Fonte: Bioinformatics; v. 30, n. 9, p. 1336-1337, MAY 1 2014.
• Citações Web of Science: 25

Resumo

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)

• Processo FAPESP: 09/53161-6 - Tecnologia da Informação aplicada a genômica para bioenergia: anotação probabilística usando inteligência artificial
• Beneficiário: Ricardo Zorzetto Nicoliello Vêncio
• Modalidade de apoio: Auxílio à Pesquisa - Programa BIOEN - Regular