Dinosaur

Dinosaur detects peptide isotopic and chromatographic features in LC-MS datasets from bottom-up mass spectrometry-based proteomics to support label-free quantification, chimeric spectrum identification, and sample complexity characterization.

Key Features:

• Enhanced Feature Detection: Detects peptide isotopic envelopes and chromatographic traces from LC-MS data, optimized for complex and high-resolution datasets.
• Algorithmic Improvements: Incorporates algorithmic enhancements that improve robustness and computational efficiency and achieved a 98% detection rate for MS/MS identifications in benchmark datasets versus ≤96% for other tested algorithms.
• Quality-Control Plots (Plot Trail): Samples algorithm computations via quality-control plots called plot trails to evaluate and refine feature detection performance.
• Improved Chimeric Spectrum Identification: Re-implements a published workflow for peptide identification in chimeric spectra, increasing chimeric identification rates from 26% to 32% in reported comparisons.
• Support for Label-free Quantification and Sample Complexity Characterization: Provides feature-level outputs that facilitate label-free quantification and characterization of sample complexity from LC-MS data.

Scientific Applications:

• Label-free quantification: Provides peptide feature inputs for label-free quantitative proteomics workflows in bottom-up LC-MS experiments.
• Chimeric spectra analysis: Enhances identification of peptides from chimeric MS/MS spectra to improve peptide identification rates.
• High-resolution LC-MS datasets: Processes high-resolution LC-MS data efficiently for feature detection in complex biological samples.
• Sample complexity characterization: Enables assessment of sample complexity through distributions of detected peptide features.

Methodology:

Detects peptide isotopic and chromatographic traces from LC-MS scans, incorporates algorithmic enhancements for robustness and efficiency, re-implements a published peptide identification workflow for chimeric spectra, and samples computations via plot trails.