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A framework for intelligent data acquisition and real-time database searching for shotgun proteomics.

Graumann J, Scheltema RA, Zhang Y, Cox J, Mann M - Mol. Cell Proteomics (2011)

Bottom Line: Finally, we demonstrate enhanced quantification of SILAC pairs whose ratios were poorly defined in survey spectra.MaxQuant Real-Time is flexible and can be applied to a large number of scenarios that would benefit from intelligent, directed data acquisition.Our framework should be especially useful for new instrument types, such as the quadrupole-Orbitrap, that are currently becoming available.

View Article: PubMed Central - PubMed

Affiliation: Department of Proteomics and Signal Transduction, Max-Planck Institute for Biochemistry, Am Klopferspitz 18, D-82152 Martinsried, Germany.

ABSTRACT
In the analysis of complex peptide mixtures by MS-based proteomics, many more peptides elute at any given time than can be identified and quantified by the mass spectrometer. This makes it desirable to optimally allocate peptide sequencing and narrow mass range quantification events. In computer science, intelligent agents are frequently used to make autonomous decisions in complex environments. Here we develop and describe a framework for intelligent data acquisition and real-time database searching and showcase selected examples. The intelligent agent is implemented in the MaxQuant computational proteomics environment, termed MaxQuant Real-Time. It analyzes data as it is acquired on the mass spectrometer, constructs isotope patterns and SILAC pair information as well as controls MS and tandem MS events based on real-time and prior MS data or external knowledge. Re-implementing a top10 method in the intelligent agent yields similar performance to the data dependent methods running on the mass spectrometer itself. We demonstrate the capabilities of MaxQuant Real-Time by creating a real-time search engine capable of identifying peptides "on-the-fly" within 30 ms, well within the time constraints of a shotgun fragmentation "topN" method. The agent can focus sequencing events onto peptides of specific interest, such as those originating from a specific gene ontology (GO) term, or peptides that are likely modified versions of already identified peptides. Finally, we demonstrate enhanced quantification of SILAC pairs whose ratios were poorly defined in survey spectra. MaxQuant Real-Time is flexible and can be applied to a large number of scenarios that would benefit from intelligent, directed data acquisition. Our framework should be especially useful for new instrument types, such as the quadrupole-Orbitrap, that are currently becoming available.

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Related in: MedlinePlus

Characteristics of the top10 strategy implemented by the intelligent agent.A, Direct comparison with an Xcalibur directed measurement with similar settings. Total number of scans is higher for Xcalibur, but the intelligent agent more often achieves a full top10 cycle. B, The difference in the number of scans can be attributed to communication overhead for each scan event of ∼36 ms, resulting in a longer cycle time for the intelligent agent (difference between the right peaks). C, The total number of identifications is comparable between the two approaches.
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Figure 3: Characteristics of the top10 strategy implemented by the intelligent agent.A, Direct comparison with an Xcalibur directed measurement with similar settings. Total number of scans is higher for Xcalibur, but the intelligent agent more often achieves a full top10 cycle. B, The difference in the number of scans can be attributed to communication overhead for each scan event of ∼36 ms, resulting in a longer cycle time for the intelligent agent (difference between the right peaks). C, The total number of identifications is comparable between the two approaches.

Mentions: Fig. 3A compares the number of collected scans between an Xcalibur run and a MaxQuant real-time directed acquisition. Overall, Xcalibur acquires more scans for the same gradient length because of the communication overhead incurred by the intelligent agent during the upload of the separate scan definitions. This is reflected in a cycle time increase from 1.5 s to 1.9 s for a top10 cycle, indicating an overhead of ∼36 ms per scan event, in the current implementation (Fig. 3B). However, the intelligent agent more often achieves a top10 cycle compared with the Xcalibur run, resulting in similar identification rates between the two approaches despite the 26% cycle time penalty (Fig. 3C). When the communication overhead can be eliminated we expect to see that the intelligent agent will perform better than the Xcalibur method as it will be able to achieve a higher scan frequency (giving better peak definition) and sequencing ability.


A framework for intelligent data acquisition and real-time database searching for shotgun proteomics.

Graumann J, Scheltema RA, Zhang Y, Cox J, Mann M - Mol. Cell Proteomics (2011)

Characteristics of the top10 strategy implemented by the intelligent agent.A, Direct comparison with an Xcalibur directed measurement with similar settings. Total number of scans is higher for Xcalibur, but the intelligent agent more often achieves a full top10 cycle. B, The difference in the number of scans can be attributed to communication overhead for each scan event of ∼36 ms, resulting in a longer cycle time for the intelligent agent (difference between the right peaks). C, The total number of identifications is comparable between the two approaches.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3316723&req=5

Figure 3: Characteristics of the top10 strategy implemented by the intelligent agent.A, Direct comparison with an Xcalibur directed measurement with similar settings. Total number of scans is higher for Xcalibur, but the intelligent agent more often achieves a full top10 cycle. B, The difference in the number of scans can be attributed to communication overhead for each scan event of ∼36 ms, resulting in a longer cycle time for the intelligent agent (difference between the right peaks). C, The total number of identifications is comparable between the two approaches.
Mentions: Fig. 3A compares the number of collected scans between an Xcalibur run and a MaxQuant real-time directed acquisition. Overall, Xcalibur acquires more scans for the same gradient length because of the communication overhead incurred by the intelligent agent during the upload of the separate scan definitions. This is reflected in a cycle time increase from 1.5 s to 1.9 s for a top10 cycle, indicating an overhead of ∼36 ms per scan event, in the current implementation (Fig. 3B). However, the intelligent agent more often achieves a top10 cycle compared with the Xcalibur run, resulting in similar identification rates between the two approaches despite the 26% cycle time penalty (Fig. 3C). When the communication overhead can be eliminated we expect to see that the intelligent agent will perform better than the Xcalibur method as it will be able to achieve a higher scan frequency (giving better peak definition) and sequencing ability.

Bottom Line: Finally, we demonstrate enhanced quantification of SILAC pairs whose ratios were poorly defined in survey spectra.MaxQuant Real-Time is flexible and can be applied to a large number of scenarios that would benefit from intelligent, directed data acquisition.Our framework should be especially useful for new instrument types, such as the quadrupole-Orbitrap, that are currently becoming available.

View Article: PubMed Central - PubMed

Affiliation: Department of Proteomics and Signal Transduction, Max-Planck Institute for Biochemistry, Am Klopferspitz 18, D-82152 Martinsried, Germany.

ABSTRACT
In the analysis of complex peptide mixtures by MS-based proteomics, many more peptides elute at any given time than can be identified and quantified by the mass spectrometer. This makes it desirable to optimally allocate peptide sequencing and narrow mass range quantification events. In computer science, intelligent agents are frequently used to make autonomous decisions in complex environments. Here we develop and describe a framework for intelligent data acquisition and real-time database searching and showcase selected examples. The intelligent agent is implemented in the MaxQuant computational proteomics environment, termed MaxQuant Real-Time. It analyzes data as it is acquired on the mass spectrometer, constructs isotope patterns and SILAC pair information as well as controls MS and tandem MS events based on real-time and prior MS data or external knowledge. Re-implementing a top10 method in the intelligent agent yields similar performance to the data dependent methods running on the mass spectrometer itself. We demonstrate the capabilities of MaxQuant Real-Time by creating a real-time search engine capable of identifying peptides "on-the-fly" within 30 ms, well within the time constraints of a shotgun fragmentation "topN" method. The agent can focus sequencing events onto peptides of specific interest, such as those originating from a specific gene ontology (GO) term, or peptides that are likely modified versions of already identified peptides. Finally, we demonstrate enhanced quantification of SILAC pairs whose ratios were poorly defined in survey spectra. MaxQuant Real-Time is flexible and can be applied to a large number of scenarios that would benefit from intelligent, directed data acquisition. Our framework should be especially useful for new instrument types, such as the quadrupole-Orbitrap, that are currently becoming available.

Show MeSH
Related in: MedlinePlus