Leveraging non-targeted metabolite profiling via statistical genomics.

One of the challenges of systems biology is to integrate multiple sources of data in order to build a cohesive view of the system of study. Here we describe the mass spectrometry based profiling of maize kernels, a model system for genomic studies and a cornerstone of the agroeconomy. Using a networ...

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Autores principales:	Miaoqing Shen, Corey D Broeckling, Elly Yiyi Chu, Gregory Ziegler, Ivan R Baxter, Jessica E Prenni, Owen A Hoekenga
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/81e7000c6bb4414f97d7b02a29e553d3
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id	oai:doaj.org-article:81e7000c6bb4414f97d7b02a29e553d3
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spelling	oai:doaj.org-article:81e7000c6bb4414f97d7b02a29e553d32021-11-18T07:55:26ZLeveraging non-targeted metabolite profiling via statistical genomics.1932-620310.1371/journal.pone.0057667https://doaj.org/article/81e7000c6bb4414f97d7b02a29e553d32013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23469044/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203One of the challenges of systems biology is to integrate multiple sources of data in order to build a cohesive view of the system of study. Here we describe the mass spectrometry based profiling of maize kernels, a model system for genomic studies and a cornerstone of the agroeconomy. Using a network analysis, we can include 97.5% of the 8,710 features detected from 210 varieties into a single framework. More conservatively, 47.1% of compounds detected can be organized into a network with 48 distinct modules. Eigenvalues were calculated for each module and then used as inputs for genome-wide association studies. Nineteen modules returned significant results, illustrating the genetic control of biochemical networks within the maize kernel. Our approach leverages the correlations between the genome and metabolome to mutually enhance their annotation and thus enable biological interpretation. This method is applicable to any organism with sufficient bioinformatic resources.Miaoqing ShenCorey D BroecklingElly Yiyi ChuGregory ZieglerIvan R BaxterJessica E PrenniOwen A HoekengaPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 2, p e57667 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Miaoqing Shen Corey D Broeckling Elly Yiyi Chu Gregory Ziegler Ivan R Baxter Jessica E Prenni Owen A Hoekenga Leveraging non-targeted metabolite profiling via statistical genomics.
description	One of the challenges of systems biology is to integrate multiple sources of data in order to build a cohesive view of the system of study. Here we describe the mass spectrometry based profiling of maize kernels, a model system for genomic studies and a cornerstone of the agroeconomy. Using a network analysis, we can include 97.5% of the 8,710 features detected from 210 varieties into a single framework. More conservatively, 47.1% of compounds detected can be organized into a network with 48 distinct modules. Eigenvalues were calculated for each module and then used as inputs for genome-wide association studies. Nineteen modules returned significant results, illustrating the genetic control of biochemical networks within the maize kernel. Our approach leverages the correlations between the genome and metabolome to mutually enhance their annotation and thus enable biological interpretation. This method is applicable to any organism with sufficient bioinformatic resources.
format	article
author	Miaoqing Shen Corey D Broeckling Elly Yiyi Chu Gregory Ziegler Ivan R Baxter Jessica E Prenni Owen A Hoekenga
author_facet	Miaoqing Shen Corey D Broeckling Elly Yiyi Chu Gregory Ziegler Ivan R Baxter Jessica E Prenni Owen A Hoekenga
author_sort	Miaoqing Shen
title	Leveraging non-targeted metabolite profiling via statistical genomics.
title_short	Leveraging non-targeted metabolite profiling via statistical genomics.
title_full	Leveraging non-targeted metabolite profiling via statistical genomics.
title_fullStr	Leveraging non-targeted metabolite profiling via statistical genomics.
title_full_unstemmed	Leveraging non-targeted metabolite profiling via statistical genomics.
title_sort	leveraging non-targeted metabolite profiling via statistical genomics.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/81e7000c6bb4414f97d7b02a29e553d3
work_keys_str_mv	AT miaoqingshen leveragingnontargetedmetaboliteprofilingviastatisticalgenomics AT coreydbroeckling leveragingnontargetedmetaboliteprofilingviastatisticalgenomics AT ellyyiyichu leveragingnontargetedmetaboliteprofilingviastatisticalgenomics AT gregoryziegler leveragingnontargetedmetaboliteprofilingviastatisticalgenomics AT ivanrbaxter leveragingnontargetedmetaboliteprofilingviastatisticalgenomics AT jessicaeprenni leveragingnontargetedmetaboliteprofilingviastatisticalgenomics AT owenahoekenga leveragingnontargetedmetaboliteprofilingviastatisticalgenomics
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Leveraging non-targeted metabolite profiling via statistical genomics.

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