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Spectronaut™ Pulsar

Proteomics software for the analysis of data independent acquisition (DIA) measurements.

Features an integrated database search engine called Pulsar enabling spectral-library free DIA workflow - directDIA™

Supports spectral-library-free workflow (directDIA™) and targeted analysis of the data using spectral libraries (Hyper Reaction Monitoring – HRM™). More information available in the Spectronaut™ Pulsar Brochure (download here).

Please take a look at our General Software License.

Spectronaut™ Pulsar

Product number: Sw-3001

Spectronaut™ Pulsar is specifically developed for the analysis of data independent acquisition (DIA) measurements. Spectronaut™ Pulsar analyzes the acquired signals with a spectral-library-free workflow, directDIA™, or with a targeted analysis of the data using spectral libraries (Hyper Reaction Monitoring – HRM™). It features Pulsar, Biognosys’ proprietary database search engine, which supports both workflows and eliminates the need for external search engines. Contact us for a FREE TRIAL!

Spectronaut™ Pulsar features:

  • Inegrated database search engine called Pulsar enabling spectral-library free DIA workflow - directDIA™
  • Integrated Protein-FDR control
  • Spectral library generation from Pulsar, Mascot, MaxQuant, Proteome Discoverer™ and ProteinPilot™ search results or from any other search engine using the Biognosys Generic Format
  • 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

HRM™ workflow is supported for all instruments listed above. DirectDIA™ worklow is supported for Thermo Scientific™ instruments.


Supported methods

Spectronaut™ Pulsar analyzes raw data from a large variety of
different methods:

  • DIA
  • SWATH™
  • SWATH™ 2.0
  • SONAR™

Methods should acquire MS1 and MS2 or MS2 only scans. Cycle time of the method should be in the range of 1-3 seconds depending on your average peak width. Gas phase fractionation is supported.


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 1 working day.


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™ Pulsar Manual

Spectronaut™ Pulsar Brochure

directDIA Brochure

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)

Spectral library HEK293 (tsv file, 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™ Pulsar

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. von Ziegler LM, Selevsek N, Tweedie-Cullen RY, Kremer E, Mansuy IM. Subregion-Specific Proteomic Signature in the Hippocampus for Recognition Processes in Adult Mice. Cell Rep. 2018; 22(12):3362-3374.
  2. Krasny L, Bland P, Kogata N, Wai P, Howard BA, Natrajan RC, Huang PH. SWATH mass spectrometry as a tool for quantitative profiling of the matrisome. J Proteomics. 2018 Mar 1.
  3. Buczak K, Ori A, Kirkpatrick JM, Holzer K, Dauch D, et al. Spatial Tissue Proteomics Quantifies Inter- and Intratumor Heterogeneity in Hepatocellular Carcinoma (HCC). Mol Cell Proteomics. 2018; 17(4):810-825.
  4. Mattugini N, Merl-Pham J, Petrozziello E, Schindler L, Bernhagen J, Hauck SM, Götz M. Influence of white matter injury on gray matter reactive gliosis upon stab wound in the adult murine cerebral cortex. Glia. 2018 Mar 24.
  5. Keam SP, Gulati T, Gamell C, Caramia F, Huang C, Schittenhelm RB, Kleifeld O, Neeson PJ, Haupt Y, Williams SG. Exploring the oncoproteomic response of human prostate cancer to therapeutic radiation using data-independent acquisition (DIA) mass spectrometry. Prostate. 2018 Mar 9.
  6. Guri Y, Colombi M, Dazert E, Hindupur SK, Roszik J, et al. mTORC2 Promotes Tumorigenesis via Lipid Synthesis. Cancer Cell. 2017; 32(6):807-823.e12.
  7. Thygesen C, Boll I, Finsen B, Modzel M, Larsen MR. Characterizing disease-associated changes in post-translational modifications by mass spectrometry. Expert Rev Proteomics. 2018; 15(3):245-258.
  8. Lin L, Zheng J, Yu Q, Chen W, Xing J, Chen C, Tian R. High throughput and accurate serum proteome profiling by integrated sample preparation technology and single-run data independent mass spectrometry analysis. J Proteomics. 2018; 174:9-16.
  9. Piazza I, Kochanowski K, Cappelletti V, Fuhrer T, Noor E, Sauer U, Picotti P. A Map of Protein-Metabolite Interactions Reveals Principles of Chemical Communication. Cell. 2018; 172(1-2):358-372.e23.
  10. Musa YR, Boller S, Puchalska M, Grosschedl R, Mittler G. Comprehensive Proteomic Investigation of Ebf1 Heterozygosity in Pro-B Lymphocytes Utilizing Data Independent Acquisition. J Proteome Res. 2018; 17(1):76-85.
  11. Huang J, Wang J, Li Q, Zhang Y, Zhang X. Enzyme and Chemical Assisted N-Terminal Blocked Peptides Analysis, ENCHANT, as a Selective Proteomics Approach Complementary to Conventional Shotgun Approach. J Proteome Res. 2018; 17(1):212-221.
  12. Lepper MF, Ohmayer U, von Toerne C, Maison N, Ziegler AG, Hauck SM. Proteomic Landscape of Patient-Derived CD4+ T Cells in Recent-Onset Type 1 Diabetes. J Proteome Res. 2018; 17(1):618-634.
  13. Messner S, Fredriksson L , Lauschke VM, Roessger K , Escher C, Bober M, et al. Transcriptomic, Proteomic, and Functional Long-Term Characterization of Multicellular Three-Dimensional Human Liver Microtissues. Applied In Vitro Toxicology.Mar 2018.ahead of print
  14. Zhang W, Chen X, Yan Z, Chen Y, Cui Y, Chen B, et al. Detergent-Insoluble Proteome Analysis Revealed Aberrantly Aggregated Proteins in Human Preeclampsia Placentas. J Proteome Res. 2017; 16(12):4468-4480.
  15. Bruderer R, Bernhardt OM, Gandhi T, Xuan Y, Sondermann J, Schmidt M, Gomez-Varela D, Reiter L. Optimization of Experimental Parameters in Data-Independent Mass Spectrometry Significantly Increases Depth and Reproducibility of Results. Mol Cell Proteomics. 2017; 16(12):2296-2309.
  16. Michalik S, Depke M, Murr A, Gesell Salazar M, Kusebauch U, Sun Z, et al. A global Staphylococcus aureus proteome resource applied to the in vivo characterization of host-pathogen interactions. Sci Rep. 2017; 7(1):9718.
  17. Rosenberger G, Bludau I, Schmitt U, Heusel M, Hunter CL, et al. Statistical control of peptide and protein error rates in large-scale targeted data-independent acquisition analyses. Nat Methods. 2017; 14(9):921-927.
  18. 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.
  19. 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).
  20. Suomi T, Elo LL. Enhanced differential expression statistics for data-independent acquisition proteomics. Sci Rep. 2017; 7(1):5869.
  21. 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.
  22. 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.
  23. 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.
  24. 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).
  25. 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.
  26. 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.
  27. 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.
  28. 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.
  29. 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.
  30. Sabbagh B, Mindt S, Neumaier M, Findeisen P. Clinical applications of MS-based protein quantification. Proteomics Clin Appl. 2016; 10(4): 323-345.
  31. 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.
  32. 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.
  33. 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.
  34. 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. 
  35. 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. 
  36. 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. 
  37. 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. 
  38. 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.
  39. 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.
  40. 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.
  41. 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.
  42. 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.
  43. 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.
  44. 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.
  45. 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.
  46. 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.
  47. 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.
  48. 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.
  49. 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.
  50. 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.
  51. 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 is the difference between directDIA™ and HRM workflows?

DirectDIA™ and HRM are data independent acquisition (DIA) based workflows that provide comprehensive proteome coverage by multiplexing thousands of proteins. DirectDIA™ is a simple workflow that doesn’t require additional runs for spectral library generation. In HRM additional DDA or DIA runs are needed for spectral library generation to produce the highest possible number of identified and quantified peptides and proteins.

What is Pulsar?

Pulsar is Biognosys’ proprietary database search engine that is integrated into Spectronaut™. This search engine implements a dynamic PSM stratification strategy to maximize identifications in large data sets and to control FDR on PSM subsets such as modified peptides.

Can I use external database search engines with Spectronaut™ Pulsar?

Yes, Spectronaut™ Pulsar supports external search engines MaxQuant, Mascot, Proteome Discoverer and ProteinPilot in addition to Pulsar for HRM experiments. However, directDIA™ workflow is only possible with the use of integrated Pulsar search engine.

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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