MassIVE MSV000085357

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GNPS - Molecular Signatures of Preeclampsia and Gestational Diabetes Mellitus Utilizing Multi-Omics Analyses, Part 1

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Description

The application of multi-omic evaluations, multi-dimensional analysis methods, and new cheminformatics-based visualization tools to provide an in depth understanding of the molecular changes taking place in preeclampsia (PRE) and gestational diabetes mellitus (GDM) patients. Since PRE and GDM are two prevalent pregnancy complications that result in adverse health effects for both the mother and fetus during pregnancy and later in life, a better understanding of each is essential. The multi-omic evaluations performed here provide new insight into the end-stage molecular profiles of each disease, thereby supplying crucial information for earlier diagnosis and potential treatments. For the Proteomics_PooledFractions datasets, peptide samples were pooled by condition and fractionated offline into 24 samples; each fraction was run via Q Exactive and Ion Mobility Mass Spectrometry for creation of an AMT tag database. Proteomics_Samples contains datasets from peptide samples that were run via Ion Mobility Mass Spectrometry. Lipidomics_Samples contains datasets from lipid samples that were run via LTQ Orbitrap Velos Mass Spectrometry. [doi:10.25345/C5K706] [dataset license: CC0 1.0 Universal (CC0 1.0)]

Keywords: preeclampsia ; gestational diabetes ; pregnancy

Contact

Principal Investigators:
(in alphabetical order) 		Erin S. Baker, North Carolina State University, United States
Kristin E. Burnum-Johnson, Pacific Northwest National Laboratory, USA
Submitting User: alchemistmatt

Publications

Odenkirk MT, Stratton KG, Gritsenko MA, Bramer LM, Webb-Robertson BM, Bloodsworth KJ, Weitz KK, Lipton AK, Monroe ME, Ash JR, Fourches D, Taylor BD, Burnum-Johnson KE, Baker ES.
Unveiling molecular signatures of preeclampsia and gestational diabetes mellitus with multi-omics and innovative cheminformatics visualization tools.
Mol Omics. Epub 2020 Sep 23.

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GNPS content goes here (MSV000085357 [task=b81a94c377cd4378931983a5f71a5f99])
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Number of distinct conditions across all analyses (original submission and reanalyses) associated with this dataset.

Distinct condition labels are counted across all files submitted in the "Metadata" category having a "Condition" column in this dataset.

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Number of distinct biological replicates across all analyses (original submission and reanalyses) associated with this dataset.

Distinct replicate labels are counted across all files submitted in the "Metadata" category having a "BioReplicate" or "Replicate" column in this dataset.

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Number of distinct technical replicates across all analyses (original submission and reanalyses) associated with this dataset.

The technical replicate count is defined as the maximum number of times any one distinct combination of condition and biological replicate was analyzed across all files submitted in the "Metadata" category. In the case of fractionated experiments, only the first fraction is considered.

"N/A" means no results of this type were submitted.
Originally identified proteins that were automatically remapped by MassIVE to proteins in the SwissProt human reference database.

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Number of distinct protein accessions reported across all analyses (original submission and reanalyses) associated with this dataset.

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Number of distinct unmodified peptide sequences reported across all analyses (original submission and reanalyses) associated with this dataset.

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Number of distinct peptide sequences (including modified variants or peptidoforms) reported across all analyses (original submission and reanalyses) associated with this dataset.

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Total number of peptide-spectrum matches (i.e. spectrum identifications) reported across all analyses (original submission and reanalyses) associated with this dataset.

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Number of distinct proteins quantified across all analyses (original submission and reanalyses) associated with this dataset.

Distinct protein accessions are counted across all files submitted in the "Statistical Analysis of Quantified Analytes" category having a "Protein" column in this dataset.

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Number of distinct proteins found to be differentially abundant in at least one comparison across all analyses (original submission and reanalyses) associated with this dataset.

A protein is differentially abundant if its change in abundance across conditions is found to be statistically significant with an adjusted p-value <= 0.05 and lists no issues associated with statistical tests for differential abundance.

Distinct protein accessions are counted across all files submitted in the "Statistical Analysis of Quantified Analytes" category having a "Protein" column in this dataset.

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