Specter
Specter deconvolutes data-independent acquisition (DIA) mass spectrometry proteomics mixture spectra by directly comparing them to a spectral library to enable sensitive and reproducible peptide identification and quantification.
Key Features:
- Deconvolution via linear algebra: Employs linear algebra to deconvolute complex mixture spectra directly.
- Direct spectral library comparison: Directly compares DIA mixture spectra against a spectral library, bypassing fragment-correlation-based approaches.
- Systematic precursor measurement: Systematically measures all peptide precursors in a biological sample.
- Resolution of highly similar peptides: Resolves convoluted spectra to discriminate peptides with highly similar sequences, including SNP-derived variants and closely related phosphopeptides.
- Validated sensitivity and performance: Comparative studies demonstrate sensitivity and performance in analyses involving highly similar peptides.
Scientific Applications:
- Discrimination of highly similar peptides: Differentiates peptides with closely related sequences that produce overlapping DIA spectra.
- Detection of single-nucleotide polymorphisms (SNPs): Enables detection of peptide sequence variants arising from SNPs.
- Phosphoproteomics site localization: Identifies alternative phosphorylation site localizations within phosphopeptides.
Methodology:
Specter measures all peptide precursors and deconvolutes DIA mixture spectra by direct comparison to a spectral library using linear algebra.
Topics
Collections
Details
- License:
- Freeware
- Maturity:
- Emerging
- Cost:
- Free of charge
- Tool Type:
- command-line tool, desktop application
- Programming Languages:
- R, Python
- Added:
- 5/18/2018
- Last Updated:
- 11/25/2024
Operations
Data Inputs & Outputs
Spectral analysis
Inputs
Outputs
Publications
Peckner R, Myers SA, Jacome ASV, Egertson JD, Abelin JG, MacCoss MJ, Carr SA, Jaffe JD. Specter: linear deconvolution for targeted analysis of data-independent acquisition mass spectrometry proteomics. Nature Methods. 2018;15(5):371-378. doi:10.1038/nmeth.4643. PMID:29608554. PMCID:PMC5924490.
Documentation
Downloads
- Software packagehttps://github.com/rpeckner-broad/Specter