MassIVE MSV000092654

Partial Public PXD044576

EndoGenius: Optimized neuropeptide identification from mass spectrometry datasets

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Description

Neuropeptides represent a unique class of signaling molecules that have garnered much attention but require special consideration when gleaning identifications from mass spectra. With highly variable sequence lengths, neuropeptides must by analyzed in their endogenous state. Further, neuropeptides share great homology within families, differing by as little as a single residue, complicating even routine analyses and necessitating optimized computational strategies for identifications. We present EndoGenius, a database searching strategy designed specifically for elucidating neuropeptide identifications from mass spectra by leveraging optimized peptide-spectrum matching approaches, an expansive motif database, and a novel scoring algorithm to achieve broader representation of the neuropeptidome and minimize re-identification. This work describes an algorithm capable of reporting more neuropeptide identifications at 1% false discovery rate than PEAKS DB in five Callinectes sapidus neuronal tissue types. [doi:10.25345/C5Z31P00W] [dataset license: CC0 1.0 Universal (CC0 1.0)]

Keywords: neuropeptide ; endogenius ; homology ; peptide ; mass spectrometry ; database searching ; FDR ; endogenous ; peptidomics ; digest-free

Contact

Principal Investigators:
(in alphabetical order) 		Lingjun Li, University of Wisconsin-Madison, United States
Submitting User: lfields
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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 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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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.
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