MassIVE MSV000096533

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GNPS - Investigating/Evaluating sample normalization methods for mass spectrometry-based multi-omics and the application to a neurodegenerative mouse model

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Description

Omics research targets biomolecules such as proteins, lipids, and metabolites, providing a comprehensive view of biological systems. The field has seen significant advancements with the development of mass spectrometry (MS). However, the accuracy of quantitative analyses is crucial due to the expansion of omics applications. Accurate comparisons depend on sample preparation and normalization methods. Some biological samples, like tissues and feces, present inherent variations that challenge accuracy. Normalization aims to reduce these variations through pre-acquisition methods that equalize biomolecule content and post-acquisition methods that adjust instrument signals. Most research has focused on single-omics data and post-acquisition normalization. However, multi-omics research, essential for understanding complex biological systems, has not thoroughly evaluated normalization methods. This study evaluates three pre-acquisition normalization methods using methanol-chloroform-water extraction to enhance multi-omics data analysis. Our multi-omics data shows that the data is significantly different depending on the normalization methods and our optimized normalization method showed that there were significant multi-omics profile changes even with young mice tissue samples. Based on these findings, we suggest sample normalization based on total protein amount to minimize the sample variation and get an accurate comparison for multi-omics. [doi:10.25345/C5QR4P317] [dataset license: CC0 1.0 Universal (CC0 1.0)]

Keywords: Proteomics, Lipidomics, Metabolomics, Normalization, Sample normalization, Extraction method ; DatasetType:Proteomics ; DatasetType:Metabolomics ; DatasetType:Other (Lipidomics)

Contact

Principal Investigators:
(in alphabetical order) 		Ling Hao, University of Maryland, United States
Submitting User: haolab
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Quantification Results
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GNPS content goes here (MSV000096533 [task=72e34a75eeb8489890dfbfe31e3244a5])
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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.

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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.

"N/A" means no results of this type were submitted.
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.

"N/A" means no results of this type were submitted.
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.

"N/A" means no results of this type were submitted.
This dataset may not contain all raw spectra data as originally deposited in PRIDE. It has been imported to MassIVE for reanalysis purposes, so its spectra data here may consist solely of processed peak lists suitable for reanalysis with most software.