Abstract
Mouse knockouts facilitate the study ofgene functions. Often, multiple abnormal phenotypes are induced when a gene is inactivated. The International Mouse Phenotyping Consortium (IMPC) has generated thousands of mouse knockouts and catalogued their phenotype data. We have acquired metabolomics data from 220 plasma samples from 30 unique mouse gene knockouts and corresponding wildtype mice from the IMPC. To acquire comprehensive metabolomics data, we have used liquid chromatography (LC) combined with mass spectrometry (MS) for detecting polar and lipophilic compounds in an untargeted approach. We have also used targeted methods to measure bile acids, steroids and oxylipins. In addition, we have used gas chromatography GC-TOFMS for measuring primary metabolites. The metabolomics dataset reports 832 unique structurally identified metabolites from 124 chemical classes as determined by ChemRICH software. The GCMS and LCMS raw data files, intermediate and finalized data matrices, R-Scripts, annotation databases, and extracted ion chromatograms are provided in this data descriptor. The dataset can be used for subsequent studies to link genetic variants with molecular mechanisms and phenotypes.
| Original language | English (US) |
|---|---|
| Article number | 101 |
| Journal | Metabolites |
| Volume | 9 |
| Issue number | 5 |
| DOIs | |
| State | Published - May 1 2019 |
Fingerprint
Keywords
- Functional genomics
- GC-MS
- IMPC
- LC-MS
- Lipidomics
- Metabolic phenotyping
- Metabolomics
- Mouse knockouts
ASJC Scopus subject areas
- Endocrinology, Diabetes and Metabolism
- Biochemistry
- Molecular Biology
Cite this
A comprehensive plasma metabolomics dataset for a cohort of mouse knockouts within the international mouse phenotyping consortium. / Barupal, Dinesh K.; Zhang, Ying; Shen, Tong; Fan, Sili; Roberts, Bryan S.; Fitzgerald, Patrick; Wancewicz, Benjamin; Valdiviez, Luis; Wohlgemuth, Gert; Byram, Gregory; Choy, Ying Yng; Haffner, Bennett; Showalter, Megan R.; Vaniya, Arpana; Bloszies, Clayton S.; Folz, Jacob S.; Kind, Tobias; Flenniken, Ann M.; McKerlie, Colin; Nutter, Lauryl M.J.; Lloyd, Kent C.; Fiehn, Oliver.
In: Metabolites, Vol. 9, No. 5, 101, 01.05.2019.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - A comprehensive plasma metabolomics dataset for a cohort of mouse knockouts within the international mouse phenotyping consortium
AU - Barupal, Dinesh K.
AU - Zhang, Ying
AU - Shen, Tong
AU - Fan, Sili
AU - Roberts, Bryan S.
AU - Fitzgerald, Patrick
AU - Wancewicz, Benjamin
AU - Valdiviez, Luis
AU - Wohlgemuth, Gert
AU - Byram, Gregory
AU - Choy, Ying Yng
AU - Haffner, Bennett
AU - Showalter, Megan R.
AU - Vaniya, Arpana
AU - Bloszies, Clayton S.
AU - Folz, Jacob S.
AU - Kind, Tobias
AU - Flenniken, Ann M.
AU - McKerlie, Colin
AU - Nutter, Lauryl M.J.
AU - Lloyd, Kent C.
AU - Fiehn, Oliver
PY - 2019/5/1
Y1 - 2019/5/1
N2 - Mouse knockouts facilitate the study ofgene functions. Often, multiple abnormal phenotypes are induced when a gene is inactivated. The International Mouse Phenotyping Consortium (IMPC) has generated thousands of mouse knockouts and catalogued their phenotype data. We have acquired metabolomics data from 220 plasma samples from 30 unique mouse gene knockouts and corresponding wildtype mice from the IMPC. To acquire comprehensive metabolomics data, we have used liquid chromatography (LC) combined with mass spectrometry (MS) for detecting polar and lipophilic compounds in an untargeted approach. We have also used targeted methods to measure bile acids, steroids and oxylipins. In addition, we have used gas chromatography GC-TOFMS for measuring primary metabolites. The metabolomics dataset reports 832 unique structurally identified metabolites from 124 chemical classes as determined by ChemRICH software. The GCMS and LCMS raw data files, intermediate and finalized data matrices, R-Scripts, annotation databases, and extracted ion chromatograms are provided in this data descriptor. The dataset can be used for subsequent studies to link genetic variants with molecular mechanisms and phenotypes.
AB - Mouse knockouts facilitate the study ofgene functions. Often, multiple abnormal phenotypes are induced when a gene is inactivated. The International Mouse Phenotyping Consortium (IMPC) has generated thousands of mouse knockouts and catalogued their phenotype data. We have acquired metabolomics data from 220 plasma samples from 30 unique mouse gene knockouts and corresponding wildtype mice from the IMPC. To acquire comprehensive metabolomics data, we have used liquid chromatography (LC) combined with mass spectrometry (MS) for detecting polar and lipophilic compounds in an untargeted approach. We have also used targeted methods to measure bile acids, steroids and oxylipins. In addition, we have used gas chromatography GC-TOFMS for measuring primary metabolites. The metabolomics dataset reports 832 unique structurally identified metabolites from 124 chemical classes as determined by ChemRICH software. The GCMS and LCMS raw data files, intermediate and finalized data matrices, R-Scripts, annotation databases, and extracted ion chromatograms are provided in this data descriptor. The dataset can be used for subsequent studies to link genetic variants with molecular mechanisms and phenotypes.
KW - Functional genomics
KW - GC-MS
KW - IMPC
KW - LC-MS
KW - Lipidomics
KW - Metabolic phenotyping
KW - Metabolomics
KW - Mouse knockouts
UR - http://www.scopus.com/inward/record.url?scp=85070453875&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85070453875&partnerID=8YFLogxK
U2 - 10.3390/metabo9050101
DO - 10.3390/metabo9050101
M3 - Article
AN - SCOPUS:85070453875
VL - 9
JO - Metabolites
JF - Metabolites
SN - 2218-1989
IS - 5
M1 - 101
ER -