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

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