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Software

Spectronaut X™

Discovery proteomics software for the targeted analysis of DIA measurements from various MS platforms.

Enables maximal proteome coverage and data completeness by utilizing the power of Hybrid Libraries – combining core proteome libraries (project or resource libraries) with sample-specific libraries (directDIA).

Please take a look at our General Software License.

Spectronaut X™

Product number: Sw-3001

Data independent acquisition (DIA) has become the method of choice for discovery proteomics workflows. Spectronaut™ has been able to achieve the highest protein coverage and data completeness through its cutting-edge data analysis algorithms including integrated search engine Pulsar.
Spectronaut X™ offers novel workflows to significantly extend the number of research applications:

  • Hybrid Library Generation: By combining core proteome libraries (project or resource libraries) with sample-specific libraries (directDIA), the number of quantifiable proteins can be increased
  • Spike-in Workflow: In combination with Biognosys’ new PQ500 reference peptide kit, targeted proteins can be label-free quantified using a comprehensive reference calibration curve spanning five orders of concentration
  • Host-Cell Proteins: Calibration carry-over allows for higher precision in identification and quantification of low abundance host-cell proteins

Spectronaut X™ also includes new features that facilitate the data analysis through improved performance and visualization:

  • Extensive spectral libraries: Increased performance and memory efficiency allows processing of very large experiments
  • Data match plot: Filters to choose which matched ions to visualize
  • Protein-coverage plot: More classes of peptides annotated with streamlined run overview

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 Impact II
  • Bruker timsTOF and timsTOF Pro without Ion Mobility
  • Waters Xevo G2-XS

 

Supported Methods

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

  • DIA
  • WiSIM-DIA
  • SWATH™
  • SWATH™ 2.0
  • SONAR™

Methods should acquire MS1 and MS2 or MS2 only scans. The cycle time of the method should be in the range of 1-3 seconds depending on your average peak width. Fractionation, including gas phase fractionation, is supported.

 

System Requirements

Minimum:
Windows 7 x64
CPU Intel® Core™, 2.7 GHz (quad core)
HDD 200 GB free space
Memory 8 GB, Software .NET 4.7.

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 4.7 or higher.

Expected memory consumption

 

How to Order

Please submit a request form. Our team will review your inquiry and get back to you within 1 working day.

 

 

Please contact us for support.

Spectronaut X™ Manual

Spectronaut X™ Brochure

FASTA of iRT peptides for shotgun search

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

 

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.

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.

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 info@biognosys.com.

Please contact us for support.

Can you explain the concept of Hybrid Libraries?

Hybrid Libraries are a combination of DIA and DDA libraries to maximize the proteome coverage beyond the core proteome. To minimize the mass spectrometer overhead time the DDA libraries are taken from resource data such as the Kim et. al data set.

Combining multiple libraries is well established, what is different with the Hybrid Libraries?

Spectronaut X™ is to our knowledge the only software that combines precursor FDR, protein FDR, and protein inference to control the data quality. Further, Spectronaut™ Pulsar X has implemented source specific iRT calibration to maximize the identifications and quantification quality.

Can you explain what you mean with spike-in workflow?

In this workflow, a set of stable isotope labeled peptides are spiked into the sample and analyzed in DIA. Spectronaut™ Pulsar can process the data multi-channel wise to display the heavy light ratios and the label-free quantities in the same experiment. For human plasma and other body fluids, Biognosys has recently launched PQ500 specifically for this workflow. Yet, any other panel can be used.

What is calibration carry-over for Host Cell Proteins (HCP)?

In this workflow, the retention time calibration of the best run is used for all other runs. In an HCP run the best run is usually the run with the majority of the host cell proteins still in there.

 

If you have any further questions, please visit our Helpdesk at help.biognosys.com.

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