Spectronaut Software
Equipment Introduction
Spectronaut® is a commercial software package aimed at analyzing data independent acquisition (DIA) proteomics experiments. Spectronaut can quantitatively profile hundreds to several thousands of proteins in one experiment. Large experiments with several conditions and replicates consisting of up to tens of thousands of LC-MS runs can be analyzed.
Specification
| Spectral library construction | • Comes with the Pulsar search engine, supporting DDA, DIA, and PRM (including MS1 information) for building high-quality libraries; • Supports external search engines, including Proteome Discoverer, MaxQuant, ProteinPilot, and Mascot search results for library construction; • Deep learning-assisted iRT retention time correction, decoy prediction, and Pulsar scoring further enhance peptide identification quantity. |
| DIA data analysis | • Supports DIA analysis based on spectral libraries; • Supports non-library-dependent directDIA analysis; • Computer deep learning enhances spectral-centered DIA data analysis performance, with directDIA+ identification quantity significantly improved (Version 17.0). Compared to SN16, identification depth can increase by 50% to 100% for different acquisition types, and offers FAST and DEEP modes for user selection. |
| Support for ion mobility | • Fully compatible with PASEF, FAIMS Pro, and HDMS2: • Improved algorithms for predicting retention time and ion mobility (Version 17.0); • Supports 1F Slice dia-PASEF data analysis (Version 17.0); • Supports PTM DIA data analysis; • Supports label-free quantification analysis (up to 3 channels). |
| Data quality control | Data quality control module reliant on iRT kit, performing quality control on DIA data analysis regarding mass-to-charge ratio, retention time, signal intensity, etc. |
| SNE integration | Addresses large dataset analysis. Allows splitting large datasets into multiple sub-datasets for individual analysis and generating SNE files, which can then be merged in the Pipeline view to output a comprehensive report. |
| Statistical and bioinformatics analysis | • Identification depth statistics; • Identification score statistics; • Sample coefficient of variation analysis; • Sample correlation analysis; • Sample and protein clustering analysis; • Differential protein analysis; • Differential protein volcano plots; • Protein GO functional annotation and enrichment analysis. |
| Report output | • Comprehensive report content; • Supports personalized report customization. |