SuperHirn

SuperHirn performs quantitative analysis of high mass-resolution liquid chromatography–mass spectrometry (LC-MS) data to extract, quantify, align, normalize, and cluster peptide features for label-free proteome profiling.

Key Features:

• Feature Extraction and Quantification: Extracts peptide and LC-MS feature signals and quantifies their intensities from high-resolution LC-MS data.
• LC-MS Similarity Analysis: Computes similarity between LC-MS datasets to support comparative proteomic analyses.
• Alignment of Multiple Datasets: Aligns multiple LC-MS datasets using algorithms for matching features across runs.
• Intensity Normalization: Normalizes intensity values within and between datasets to enable reliable quantitative comparisons.
• Clustering and Classification Analysis: Performs clustering and classification of feature profiles to identify patterns in proteomic data.
• Unsupervised Trend Detection: Detects profiling trends in an unsupervised manner from extracted feature profiles.
• Association with Theoretical Dilution Profiles: Associates observed feature profiles with theoretical dilution profiles to interpret concentration changes in complex mixtures.
• High-Resolution MS Support: Processes LC-MS data acquired from high-resolution mass spectrometers.

Scientific Applications:

• MS1-based Proteome Profiling: MS1-based label-free profiling and quantification of proteomes from LC-MS experiments.
• Comparative Proteomics: Comparative analysis of multiple LC-MS datasets to detect differences across experimental conditions or sample sets.
• Benchmarking and Dilution-Series Analysis: Analysis of benchmark LC-MS datasets and detection of proteins matching theoretical dilution trends.
• High-throughput Label-free Quantification: High-throughput quantification of protein concentration changes to study protein dynamics and interactions across biological conditions.

Methodology:

Processes LC-MS data acquired from high-resolution mass spectrometers using a suite of integrated modules that extract detailed feature profiles, enable unsupervised detection of profiling trends, and associate proteins with theoretical dilution profiles.