Metabolite signal identification in accurate mass metabolomics data with MZedDB, an interactive m/z annotation tool utilising predicted ionisation behaviour ‘rules’.

John Draper; David Enot; David  Parker; Manfred Beckmann; Stuart Snowden; Wanchang Lin; Hassan Zubair

Metabolite signal identification in accurate mass metabolomics data with MZedDB, an interactive m/z annotation tool utilising predicted ionisation behaviour ‘rules’.

John Draper, David Enot, David Parker, Manfred Beckmann, Stuart Snowden, Wanchang Lin, Hassan Zubair

Department of Biological Sciences

Research output: Contribution to journal › Article › peer-review

Original language	English
Article number	227
Journal	BMC Bioinformatics
Volume	10
Publication status	Published - 2009

Cite this

@article{762b622fdab04e14adfd8fdffc4c192e,

title = "Metabolite signal identification in accurate mass metabolomics data with MZedDB, an interactive m/z annotation tool utilising predicted ionisation behaviour {\textquoteleft}rules{\textquoteright}.",

author = "John Draper and David Enot and David Parker and Manfred Beckmann and Stuart Snowden and Wanchang Lin and Hassan Zubair",

year = "2009",

language = "English",

volume = "10",

journal = "BMC Bioinformatics",

issn = "1471-2105",

publisher = "BioMed Central",

}

TY - JOUR

T1 - Metabolite signal identification in accurate mass metabolomics data with MZedDB, an interactive m/z annotation tool utilising predicted ionisation behaviour ‘rules’.

AU - Draper, John

AU - Enot, David

AU - Parker, David

AU - Beckmann, Manfred

AU - Snowden, Stuart

AU - Lin, Wanchang

AU - Zubair, Hassan

PY - 2009

Y1 - 2009

M3 - Article

SN - 1471-2105

VL - 10

JO - BMC Bioinformatics

JF - BMC Bioinformatics

M1 - 227

ER -