MassIVE MSV000088598

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GLEAMS clustering dark proteome

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Description

GLEAMS is a deep neural network to embed spectra into a low-dimensional space in which spectra generated by the same peptide are close to one another. We have used GLEAMS as the basis for a large-scale spectrum clustering, detecting groups of unidentified, proximal spectra representing the same peptide. GLEAMS was used to embed 669 million spectra from the MassIVE-KB dataset, after which hierarchical clustering with average linkage was used to cluster the embeddings. Medoid spectra were extracted from clusters consisting of only unidentified spectra, resulting in 45 million medoid spectra representing 257 million clustered spectra. The medoid spectra were split into two groups based on cluster size (size two and size greater than two) and exported to two MGF files. ANN-SoLo was used for open modification searching, identifying 5.3 million peptide-spectrum matches. We here present the originally unidentified cluster medoid spectra and the ANN-SoLo identification results as a community resource. This is a valuable dataset to further explore the dark proteome, by investigating spectra that are observed repeatedly across many experiments but consistently remain unidentified. [doi:10.25345/C52K34] [dataset license: CC0 1.0 Universal (CC0 1.0)]

Keywords: deep learning ; clustering ; dark proteome ; open modification searching

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Principal Investigators:
(in alphabetical order) 		William Stafford Noble, University of Washington, USA
Submitting User: woutb
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Number of distinct conditions across all analyses (original submission and reanalyses) associated with 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.

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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.

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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.

"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.