Proteomics

Dataset Information

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Isobaric matching between runs and novel PSM-level normalization in MaxQuant strongly improve reporter ion-based quantification - Peli1 knock-out mice dataset


ABSTRACT: Isobaric labeling has the promise of combining high sample multiplexing with precise quantification. However, normalization issues and the missing value problem of complete n-plexes hamper quantification across more than one n-plex. Here we introduce two novel algorithms implemented in MaxQuant that substantially improve the data analysis with multiple n-plexes. First, isobaric matching between runs (IMBR) makes use of the three-dimensional MS1 features to transfer identifications from identified to unidentified MS/MS spectra between LC-MS runs in order to utilize reporter ion intensities in unidentified spectra for quantification. On typical datasets, we observe a significant gain in quantifiable n-plexesMS/MS spectra that can be used for quantification. Second, we introduce a novel PSM-level normalization, applicable to data with and without common reference channel. It is a weighted median-based method, in which the weights reflect the number of ions that were used for fragmentation. On a typical dataset, we observe complete removal of batch effects and dominance of the biological sample grouping after normalization. This dataset is one of the datasets used for the study. It is TMT 10-plex with a reference channel.

INSTRUMENT(S): LTQ Orbitrap Elite

ORGANISM(S): Mus Musculus (mouse)

TISSUE(S): Brain

SUBMITTER: Sung-Huan Yu  

LAB HEAD: Jürgen Cox

PROVIDER: PXD019881 | Pride | 2020-06-22

REPOSITORIES: Pride

Dataset's files

Source:
Action DRS
OxidationMSites.txt Txt
TMT10_B1-3.raw Raw
TMT10_B10.raw Raw
TMT10_B11.raw Raw
TMT10_B12.raw Raw
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Publications

Isobaric Matching between Runs and Novel PSM-Level Normalization in MaxQuant Strongly Improve Reporter Ion-Based Quantification.

Yu Sung-Huan SH   Kyriakidou Pelagia P   Cox Jürgen J  

Journal of proteome research 20200916 10


Isobaric labeling has the promise of combining high sample multiplexing with precise quantification. However, normalization issues and the missing value problem of complete <i>n</i>-plexes hamper quantification across more than one <i>n</i>-plex. Here, we introduce two novel algorithms implemented in MaxQuant that substantially improve the data analysis with multiple <i>n</i>-plexes. First, isobaric matching between runs makes use of the three-dimensional MS1 features to transfer identifications  ...[more]

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