Baitmet, a computational approach for GC-MS library-driven metabolite profiling

Domingo-Almenara Xavier; Brezmes Jesus; Venturini GabrielaVivo-Truyols GabrielPerera AlexandreVinaixa Maria

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Baitmet, a computational approach for GC-MS library-driven metabolite profiling

机译：Baitmet, a computational approach for GC-MS library-driven metabolite profiling

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Introduction Current computational tools for gas chromatography-mass spectrometry (GC-MS) metabolomics profiling do not focus on metabolite identification, that still remains as the entire workflow bottleneck and it relies on manual data reviewing. Metabolomics advent has fostered the development of public metabolite repositories containing mass spectra and retention indices, two orthogonal properties needed for metabolite identification. Such libraries can be used for library-driven compound profiling of large datasets produced in metabolomics, a complementary approach to current GC-MS non-targeted data analysis solutions that can eventually help to assess metabolite identities more efficiently.

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《Metabolomics :》 |2017年第8期|Article:93-Article:93|共5页
作者
Domingo-Almenara Xavier; Brezmes Jesus; Venturini GabrielaVivo-Truyols GabrielPerera AlexandreVinaixa Maria;
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作者单位

Univ Rovira & Virgili, Dept Elect Engn DEEEA, Metabol Platform, Tarragona, Catalonia, Spain;

Univ Sao Paulo, Heart Inst InCor, Lab Genet & Mol Cardiol, Sao Paulo, Brazil;

Univ Amsterdam, Vant Hoff Inst Mol Sci, Analyt Chem Grp, Amsterdam, NetherlandsUniv Politecn Cataluna, Dept ESAII, CIBER Bioengn Biomat & Nanomed CIBERBBN, B2SLab, Barcelona;

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正文语种英语
中图分类生理学;
关键词
Compound profiling; Gas chromatography; Mass spectrometry; Metabolomics;

Baitmet, a computational approach for GC-MS library-driven metabolite profiling

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