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Professional software for analysis of HRM-MS™ (SWATH, DIA) data

Now available: Spectronaut 11 (Asimov) with support for Mascot database search engine and spectral-library based Waters SONAR™ analysis.

Requires Biognosys iRT Kit or HRM Calibration Kit to enable automated, fast and accurate signal processing of your data sets.

Please take a look at our General Spectronaut License.


Annual Academic Single Named User License

Product number: Sw-3001-a

Spectronaut™ software is specifically developed for the analysis of HRM-MS™ (DIA or SWATH™ data sets). HRM, Hyper Reaction Monitoring, is Biognosys’ next generation proteomics technology based on data independent acquisition (DIA) that enables reproducible and accurate quantification of 1000s of proteins in a single instrument run.  In combination with the iRT Kit or HRM Calibration Kit Spectronaut™ enables fully automated, fast and accurate signal processing of your DIA or SWATH™ data sets. 

Spectronaut™ features

  • Spectral library generation from Mascot, MaxQuant, Proteome Discoverer™ and ProteinPilot™ search results or from any other search engine using the Biognosys Generic Format
  • Integrated Protein-FDR control
  • Fast data analysis speed 
  • Fully automated in-run calibration (robust against mass shifts of up to 20 ppm)
  • Automatic quality control 
  • Powerful peak picking
  • Automatic interference correction
  • Protein regulation differences annotated with Gene Ontology terms in your samples available immediately after the analysis

Please contact us for support.

Supported instruments

  • Thermo Scientific™ Q Exactive™ Series
  • Thermo Scientific™ Orbitrap Fusion™ Series
  • AB SCIEX TripleTOF® Series (5600, 5600+, 6600)
  • Bruker Q-TOF Series 
  • Waters Xevo T-QS


Supported methods

Spectronaut™ analyzes a large variety of different DIA methods. Minimal requirements are a reversed phase chromatography with a linear or nonlinear gradient that spans at least 10-35% Acetonitrile. Methods acquiring only MS2 scans are supported as well as methods with both, MS1 and MS2 scans. The cycle time of the DIA method should be in the range of 2-3 seconds depending on the peak width of the chromatography used. MS1 as well as MS2 ranges can be segmented. The MS2 scans should cover at least 500-900 m/z. Gas phase fractionation is supported starting with Spectronaut™ 7 (Nimoy). More specifically Spectronaut™ supports HRM-MS™, WiSIM-DIA, AIF, SWATH™ and SWATH™ 2.0. Multiplexed DIA is not supported.

Figure: Schematic representation of different DIA versions supported by Spectronaut™.


System requirements

Minimum: Windows 7 x64, CPU Intel ® Core™ CPU 2.7 GHz (quad core), HDD 200 GB free space, Memory 8 GB, Software .NET 4.5.

Recommended: Windows 7 x64 or higher, CPU Intel Core i7 4770, 3.4 GHz (octa core) or more, HDD 500 GB free space (SSD), Memory 16 GB or more, Software .NET 4.5 or higher.

Expected memory consuption


How to order

Simply Add to Cart and proceed through checkout. Our team will review your inquiry and get back to you within 5 working days. If you don't get contacted in this period, please notify


How to cite

If you refer to Spectronaut™ algorithms in your research please cite:

Reiter L, Rinner O, Picotti P, Hüttenhain R, Beck M, Brusniak MY, Hengartner MO, Aebersold R. mProphet: automated data processing and statistical validation for large-scale SRM experiments. Nat Methods. 2011;8(5):430-5.

If you refer to the software package please cite:

Bruderer R, Bernhardt OM, Gandhi T, Miladinovic SM, Cheng LY, Messner S, et al. Extending the limits of quantitative proteome profiling with data-independent acquisition and application to acetaminophen treated 3D liver microtissues. Mol Cell Proteomics. 2015 Feb 27. pii: mcp.M114.044305.

Please contact us for support.

Spectronaut™ 11 User Manual

Spectronaut™ 10.0 User Manual

FASTA of iRT peptides for shotgun search

Skyline Schema for exporting Spectronaut compatible libraries - simply add this schema to Skyline’s report list


Spectronaut™ Video Tutorials

Settings and Prepare Perspective

Review and QC Perspective

Post Analysis and Report Perspective


Test Files

Spectral library generation using Proteome Discoverer 1.4 (HEK293 cell line sample)

Proteome Discoverer 1.4 Search Files (zip, 1.3 GB) for spectral library generation


Spectral library generation using MaxQuant 1.5 (HEK293 cell line sample)

MaxQuant Search Files (zip, 386 MB) for spectral library generation


HEK293 demo data set

Description Demo Data Set (pdf file)

HTRMS Spectronaut™ Sample 1 Run 1 (htrms file, 2.9 GB)

HTRMS Spectronaut™ Sample 1 Run 2 (htrms file, 3.4 GB)

HTRMS Spectronaut™ Sample 1 Run 3 (htrms file, 3.0 GB)

HTRMS Spectronaut™ Sample 5 Run 1 (htrms file, 3.4 GB)

HTRMS Spectronaut™ Sample 5 Run 2 (htrms file, 3.4 GB)

HTRMS Spectronaut™ Sample 5 Run 3 (htrms file, 2.9 GB)

HTRMS Spectronaut™ Sample 8 Run 1 (htrms file, 3.1 GB)

HTRMS Spectronaut™ Sample 8 Run 2 (htrms file, 3.3 GB)

HTRMS Spectronaut™ Sample 8 Run 3 (htrms file, 2.9 GB)

Spectral library HEK (kit file, 73MB)


HEK293 demo data set

SWATH™ Spectronaut HEK293 Demo R01 (wiff-file, 8.8 MB)
SWATH™ Spectronaut HEK293 Demo R01 (wiff-file.scan, 3.8 GB)

SWATH™ Spectronaut HEK293 Demo R02 (wiff-file, 8.8 MB)
SWATH™ Spectronaut HEK293 Demo R02 (wiff-file.scan, 3.8 GB)

SWATH™ Spectronaut HEK293 Demo R03 (wiff-file, 8.8 MB)
SWATH™ Spectronaut HEK293 Demo R03 (wiff-file.scan, 3.8 GB)

HEK293 GSDS TTof.tsv (wiff-file.scan, 34.0 MB)


HEK cell line sample

RAW Test Data Set 1 (raw file, 2.4 GB)

RAW Test Data Set 2 (raw file, 2.8 GB)

RAW Test Data Set 3 (raw file, 2.5 GB)

Spectral library HEK293 (xls file, 4.2 MB)

Spectral library E. Coli (xls file, 5.9 MB)

Window sizes (txt file) - this file is needed only when analyzing the data with software other than Spectronaut™

Please contact us for support.

Visit Science Hub to access all scientific and technical content (publications, videos, application notes, posters and more) related to Biognosys technology, products and services.

Peer-Reviewed Papers

  1. Li S, Cao Q, Xiao W, Guo Y, Yang Y, Duan X, Shui W. Optimization of Acquisition and Data-Processing Parameters for Improved Proteomic Quantification by Sequential Window Acquisition of All Theoretical Fragment Ion Mass Spectrometry. J Proteome Res. 2017; 16(2):738-747.
  2. Bruderer R, Sondermann J, Tsou CC, Barrantes-Freer A, Stadelmann C, et al. New targeted approaches for the quantification of data-independent acquisition mass spectrometry. Proteomics. 2017; 17(9).
  3. Meyer JG, Schilling B. Clinical applications of quantitative proteomics using targeted and untargeted data-independent acquisition techniques. Expert Rev Proteomics. 2017; 14(5):419-429.
  4. Li H, Han J, Pan J, Liu T, Parker CE, Borchers CH. Current trends in quantitative proteomics - an update. J Mass Spectrom. 2017; 52(5):319-341.
  5. Crowgey EL, Matlock A, Venkatraman V, Fert-Bober J, Van Eyk JE. Mapping Biological Networks from Quantitative Data-Independent Acquisition Mass Spectrometry: Data to Knowledge Pipelines. Methods Mol Biol. 2017; 1558:395-413.
  6. Anjo SI, Santa C, Manadas B. SWATH-MS as a tool for biomarker discovery: From basic research to clinical applications. Proteomics. 2017; 17(3-4).
  7. Fabre B, Korona D, Nightingale DJ, Russell S, Lilley KS. SWATH-MS data of Drosophila melanogaster proteome dynamics during embryogenesis. Data Brief. 2016; 9:771-775.
  8. Fabre B, Korona D, Nightingale DJ, Russell S, Lilley KS. SWATH-MS dataset of heat-shock treated Drosophila melanogaster embryos. Data Brief. 2016; 9:991-995.
  9. Parker SJ, Holewinski RJ, Tchernyshyov I, Venkatraman V, Parker L, Van Eyk JE. Label-Free Quantification by Data Independent Acquisition Mass Spectrometry to Map Cardiovascular Proteomes. Manual of Cardiovascular Proteomics 2016. pp 227-245.
  10. Vowinckel J, Zelezniak A, Kibler A, Bruderer R, Muelleder M, Reiter L, Ralser M. Precise label-free quantitative proteomes in high-throughput by microLC and data-independent SWATH acquisition. bioRxiv Sep 5.
  11. Bruderer R, Bernhardt OM, Gandhi T, Reiter L. High-precision iRT prediction in the targeted analysis of data-independent acquisition and its impact on identification and quantitation. Proteomics. 2016; 16(15-16): 2246-56.
  12. Sabbagh B, Mindt S, Neumaier M, Findeisen P. Clinical applications of MS-based protein quantification. Proteomics Clin Appl. 2016; 10(4): 323-345.
  13. Fabre B, Korona D, Groen A, Vowinckel J, Gatto L, Deery MJ, Ralser M, Russell S, Lilley KS. Analysis of the Drosophila melanogaster proteome dynamics during the embryo early development by a combination of label-free proteomics approaches. Proteomics. 2016 Mar 31.
  14. Campbell K, Vowinckel J, Keller MA, Ralser M. Methionine metabolism alters oxidative stress resistance via the pentose phosphate pathway. Antioxid Redox Signal. 2015 Nov 23.
  15. Parker SJ, Rost H, Rosenberger G, Collins BC, Malmström L, et al. Identification of a Set of Conserved Eukaryotic Internal Retention Time Standards for Data-independent Acquisition Mass Spectrometry. Mol Cell Proteomics. 2015; 14(10): 2800-2813.
  16. Pernikarova V, Bouchal P. Targeted proteomics of solid cancers: from quantification of known biomarkers towards reading the digital proteome maps. Expert Rev Proteomics. 2015 Oct 10:1-17. 
  17. Muntel J, Xuan Y, Berger ST, Reiter L, Bachur R, Kentsis A, Steen H. Advancing Urinary Protein Biomarker Discovery by Data-Independent Acquisition on a Quadrupole-Orbitrap Mass Spectrometer. J Proteome Res. 2015 Oct 22. 
  18. Bilbao A, Zhang Y, Varesio E, Luban J, Strambio-De-Castillia C, Lisacek F, Hopfgartner G.Ranking Fragment Ions Based on Outlier Detection for Improved Label-Free Quantification in Data-Independent Acquisition LC-MS/MS. J Proteome Res. 2015 Oct 14. 
  19. Krautkramer KA, Reiter L, Denu JM, Dowell JA. Quantification of SAHA-Dependent Changes in Histone Modifications Using Data-Independent Acquisition Mass Spectrometry. J Proteome Res. 2015; 14(8):3252-62. 
  20. Bilbao A, Varesio E, Luban J, Strambio-De-Castillia C, Hopfgartner G, Müller M, Lisacek F. Processing strategies and software solutions for data-independent acquisition in mass spectrometry. Proteomics. 2015; 15(5-6):964-80.
  21. Bruderer R, Bernhardt OM, Gandhi T, Miladinovic SM, Cheng LY, Messner S, et al. Extending the limits of quantitative proteome profiling with data-independent acquisition and application to acetaminophen treated 3D liver microtissues. Mol Cell Proteomics. 2015 May;14(5):1400-10.
  22. Schubert OT, Gillet LC, Collins BC, Navarro P, Rosenberger G, Wolski WE, et al. Building high-quality assay libraries for targeted analysis of SWATH MS data. Nat Protoc. 2015; 10(3):426-41.
  23. Selevsek N, Chang CY, Gillet LC, Navarro P, Bernhardt OM, Reiter L, et al. Reproducible and consistent quantification of the Saccharomyces cerevisiae proteome by SWATH-MS. Mol Cell Proteomics. 2015.
  24. Kuharev J, Navarro P, Distler U, Jahn O, Tenzer S. In-depth evaluation of software tools for data-independent acquisition-based label-free quantification. Proteomics. 2014.
  25. Sajic T, Liu Y, Aebersold R. Using data-independent, high resolution mass spectrometry in protein biomarker research: Perspectives and clinical applications. Proteomics Clin Appl. 2014. doi: 10.1002/prca.201400117.
  26. Orellana CA, Marcellin E, Schulz BL, Nouwens AS, Gray PP, Nielsen LK. High-Antibody-Producing Chinese Hamster Ovary Cells Up-Regulate Intracellular Protein Transport and Glutathione Synthesis. J Proteome Res. 2015.
  27. Bilbao A, Varesio E, Luban J, Strambio-De-Castillia C, Hopfgartner G, Müller M, Lisacek F. Processing Strategies and Software Solutions for Data-Independent Acquisition in Mass Spectrometry. Proteomics. 2014. doi: 10.1002/pmic.201400323.
  28. Rosenberger G, Koh CC, Guo T, Röst HL, Kouvonen P, Collins BC, Heusel M, et al. A repository of assays to quantify 10,000 human proteins by SWATH-MS. Scientific Data. 2014; 1: 140031.
  29. Westman J, Smeds E, Johansson L, Mörgelin M, Olin AI, Malmström E, Linder A, Herwald H. Treatment with p33 Curtails Morbidity and Mortality in a Histone-Induced Murine Shock Model. J Innate Immun. 2014.
  30. Liu Y, Hüttenhain R, Collins B, Aebersold R. Mass spectrometric protein maps for biomarker discovery and clinical research. Expert Rev Mol Diagn. 2013;13(8):811-25.
  31. Krüger A, Vowinckel J, Mülleder M, Grote P, Capuano F, Bluemlein K, Ralser M. Tpo1-mediated spermine and spermidine export controls cell cycle delay and times antioxidant protein expression during the oxidative stress response. EMBO Rep. 2013; 14(12):1113-9.
  32. Vowinckel J, Capuano F, Campbell K, Deery MJ, Lilley KS, Ralser M. The beauty of being (label)-free: sample preparation methods for SWATH-MS and next-generation targeted proteomics. F1000Research 2013, 2:272.
  33. Reiter L, Rinner O, Picotti P, Hüttenhain R, Beck M, Brusniak MY, Hengartner MO, Aebersold R. mProphet: automated data processing and statistical validation for large-scale SRM experiments. Nat Methods. 2011;8(5):430-5.
If we forgot to include your publication that uses our technology, please don’t hesitate to let us know at

Please contact us for support.

What are the minimal systems requirement to run Spectronaut?

Recommended system requirements are: Windows 7 x64 or higher, CPU Intel Core i7 4770, 3.4 GHz (octa core) or more, HDD 500 GB free space (SSD), Memory 16 GB or more, Software .NET 4.5 or higher. 

Is there a way to calculate the required RAM to analyze and experiment with a spectral library with the size of n precursors and r numbers of DIA runs?

Yes, the required RAM can be calculated using the following formula: 

Which mass spectrometric vendors are supported by Spectronaut?

Spectronaut is a vendor independent software. As of Spectronaut 9 the following vendors are supported: ThermoFisher, Sciex and Bruker. 

My vendor (e.g. Waters) is not supported why is this the case?

Most likely we do not have the vendors API to implement it into the software. If you wish to have your vendors data supported please contact the vendor as well as us at

Which search engines for spectral library generation are supported?

As of Spectronaut 10 search files from MaxQuant, Protein Pilot and Proteome Discoverer are supported to generate spectral libraries directly in the Prepare Perspective of Spectronaut. Further, any search output correctly formatted into the Biognosys Generic Format can be used.

My favorite search engine is not supported. What can I do?

Please contact the search engine as well as us at and together we can try to get the API of your favorite search engine to support it in future software releases. 

How does Spectronaut use the iRT peptides?

The iRT peptides are used for retention time and mass calibration in the three pass analysis applied by the software. In the first pass the peptides are used as anchor points to initiate the analysis. Further, the iRT peptides are used in the software for quality control features in the QC Perspective. 

Is Spectronaut applying normalization and if so what strategy is used?

Per default Spectronaut normalizes the data. The two underlying assumptions are that there are on average no proteins that change in abundance (1) and if there are proteins that are changing then there is the same number of proteins upregulated vs downregulated (2). 

What does the Q-value stand for and how is it different from the p-Value?

The Q-value is the minimum false discovery rate at which the test may be called significant. As the definition implies it gives a direct measure of the FDR of the identification and quantification. 

What is the default Q-value cut off used by Spectronaut?

For peptide identification the cut off value is set at Q-value<0.01 for precursor identification. This means that among all true identified precursors there is 1% false positive identifications.

For the statistical testing of differentially abundant proteins the Q-value is set at Q-value<0.05. 

How is the Q-value for peptide identification calculated?

In total more than 12 different measurements are used to calculate the Q-value for a given peptide. Among the values factoring into the equation are iRT accuracy, relative fragment ion intensities many more. 

How are the differential abundant candidates in the Post Analysis Perspective calculated?

To determine the proteins differentially abundant between different conditions Spectronaut is performing a one sample t-test on the log ratios of all features between all conditions. For the protein quantities the average precursor intensities are used per default. 

I do not like the default settings where can I change them?

The Biognosys factory settings or also called default settings have proven to be suitable for the majority of the experimental set ups. Yet, sometimes individual settings are necessary. To perform data analysis using user defined settings there are two options available. Option 1: the settings are modified in the Review Perspective when setting up the experiment. Option 2: In the Settings Perspective the settings are modified and stored in a separate settings schema. 

I want to subject the data generated in Spectronaut to third party analysis tools how can I do this?

The Report Perspective is design in a way that all accessible information can be exported. For certain downstream analysis tools a reporting schema can be created based on the information needed. If you need help establishing a schema do not hesitate to contact us at 

Can I use other peptides than the iRT peptides for QC monitoring?

Yes, in the Prepare Perspective the library can be used to generate a pool of peptides suitable for QC. This additional panel can be used in the QC Perspective.

Please contact us for support.


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