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NITPICK: peak identification for mass spectrometry data.

Renard BY, Kirchner M, Steen H, Steen JA, Hamprecht FA - BMC Bioinformatics (2008)

Bottom Line: NITPICK is based on fractional averaging, a novel extension to Senko's well-known averaging model, and on a modified version of sparse, non-negative least angle regression, for which a suitable, statistically motivated early stopping criterion has been derived.Extensive comparative evaluation has been carried out and results are provided for simulated and real-world data sets.NITPICK outperforms pepex, to date the only alternate, publicly available, non-greedy feature extraction routine.

View Article: PubMed Central - HTML - PubMed

Affiliation: Interdisciplinary Center for Scientific Computing, University of Heidelberg, Heidelberg, Germany. bernhard.renard@iwr.uni-heidelberg.de

ABSTRACT

Background: The reliable extraction of features from mass spectra is a fundamental step in the automated analysis of proteomic mass spectrometry (MS) experiments.

Results: This contribution proposes a sparse template regression approach to peak picking called NITPICK. NITPICK is a Non-greedy, Iterative Template-based peak PICKer that deconvolves complex overlapping isotope distributions in multicomponent mass spectra. NITPICK is based on fractional averaging, a novel extension to Senko's well-known averaging model, and on a modified version of sparse, non-negative least angle regression, for which a suitable, statistically motivated early stopping criterion has been derived. The strength of NITPICK is the deconvolution of overlapping mixture mass spectra.

Conclusion: Extensive comparative evaluation has been carried out and results are provided for simulated and real-world data sets. NITPICK outperforms pepex, to date the only alternate, publicly available, non-greedy feature extraction routine. NITPICK is available as software package for the R programming language and can be downloaded from (http://hci.iwr.uni-heidelberg.de/mip/proteomics/).

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Related in: MedlinePlus

Peak picking results in the m/z  636–646 mass range.  Illustration of observed (top) and reconstructed (bottom) spectra. At m/z 636.64 and m/z 636.96 we observe incomplete unmixing: The isotope distribution (z=3) with monoisotopic mass m0 located at m/z 636.29 heavily overlaps the distribution (z=3) with m0 = 636.64 m/z (left triangle marker). The overlapproves inseparable and the monoisotopic mass of the second distribution is wrongly detected at m/z 636.96.Further, due to conservative noise level/complexity estimation, the isotope distribution located at m/z642.33 (right triangle marker) is not detected. Note that in both of the distributions located at m/z 636.29and m/z 639.65, the monoisotopic mass peak does not correspond to the most intensive peak.
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Figure 7: Peak picking results in the m/z 636–646 mass range. Illustration of observed (top) and reconstructed (bottom) spectra. At m/z 636.64 and m/z 636.96 we observe incomplete unmixing: The isotope distribution (z=3) with monoisotopic mass m0 located at m/z 636.29 heavily overlaps the distribution (z=3) with m0 = 636.64 m/z (left triangle marker). The overlapproves inseparable and the monoisotopic mass of the second distribution is wrongly detected at m/z 636.96.Further, due to conservative noise level/complexity estimation, the isotope distribution located at m/z642.33 (right triangle marker) is not detected. Note that in both of the distributions located at m/z 636.29and m/z 639.65, the monoisotopic mass peak does not correspond to the most intensive peak.

Mentions: In the m/z 636–646 mass range (figure 7) we observe an example of incomplete unmixing: the isotope distribution (z=3) with monoisotopic mass located at m/z 636.29 heavily overlaps the distribution (z=3) located at m/z 636.64 (left triangle marker). The overlap proves inseparable and the monoisotopic mass of the second distribution is wrongly detected at m/z 636.96. Further, due to conservative noise level/complexity estimation, the isotope distribution located at m/z 642.33 (right triangle marker) is not detected. Note that in both of the correctly detected distributions located at m/z 636.29 and m/z 639.65, the monoisotopic mass peak does not correspond to the most prominent peak.


NITPICK: peak identification for mass spectrometry data.

Renard BY, Kirchner M, Steen H, Steen JA, Hamprecht FA - BMC Bioinformatics (2008)

Peak picking results in the m/z  636–646 mass range.  Illustration of observed (top) and reconstructed (bottom) spectra. At m/z 636.64 and m/z 636.96 we observe incomplete unmixing: The isotope distribution (z=3) with monoisotopic mass m0 located at m/z 636.29 heavily overlaps the distribution (z=3) with m0 = 636.64 m/z (left triangle marker). The overlapproves inseparable and the monoisotopic mass of the second distribution is wrongly detected at m/z 636.96.Further, due to conservative noise level/complexity estimation, the isotope distribution located at m/z642.33 (right triangle marker) is not detected. Note that in both of the distributions located at m/z 636.29and m/z 639.65, the monoisotopic mass peak does not correspond to the most intensive peak.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2655099&req=5

Figure 7: Peak picking results in the m/z 636–646 mass range. Illustration of observed (top) and reconstructed (bottom) spectra. At m/z 636.64 and m/z 636.96 we observe incomplete unmixing: The isotope distribution (z=3) with monoisotopic mass m0 located at m/z 636.29 heavily overlaps the distribution (z=3) with m0 = 636.64 m/z (left triangle marker). The overlapproves inseparable and the monoisotopic mass of the second distribution is wrongly detected at m/z 636.96.Further, due to conservative noise level/complexity estimation, the isotope distribution located at m/z642.33 (right triangle marker) is not detected. Note that in both of the distributions located at m/z 636.29and m/z 639.65, the monoisotopic mass peak does not correspond to the most intensive peak.
Mentions: In the m/z 636–646 mass range (figure 7) we observe an example of incomplete unmixing: the isotope distribution (z=3) with monoisotopic mass located at m/z 636.29 heavily overlaps the distribution (z=3) located at m/z 636.64 (left triangle marker). The overlap proves inseparable and the monoisotopic mass of the second distribution is wrongly detected at m/z 636.96. Further, due to conservative noise level/complexity estimation, the isotope distribution located at m/z 642.33 (right triangle marker) is not detected. Note that in both of the correctly detected distributions located at m/z 636.29 and m/z 639.65, the monoisotopic mass peak does not correspond to the most prominent peak.

Bottom Line: NITPICK is based on fractional averaging, a novel extension to Senko's well-known averaging model, and on a modified version of sparse, non-negative least angle regression, for which a suitable, statistically motivated early stopping criterion has been derived.Extensive comparative evaluation has been carried out and results are provided for simulated and real-world data sets.NITPICK outperforms pepex, to date the only alternate, publicly available, non-greedy feature extraction routine.

View Article: PubMed Central - HTML - PubMed

Affiliation: Interdisciplinary Center for Scientific Computing, University of Heidelberg, Heidelberg, Germany. bernhard.renard@iwr.uni-heidelberg.de

ABSTRACT

Background: The reliable extraction of features from mass spectra is a fundamental step in the automated analysis of proteomic mass spectrometry (MS) experiments.

Results: This contribution proposes a sparse template regression approach to peak picking called NITPICK. NITPICK is a Non-greedy, Iterative Template-based peak PICKer that deconvolves complex overlapping isotope distributions in multicomponent mass spectra. NITPICK is based on fractional averaging, a novel extension to Senko's well-known averaging model, and on a modified version of sparse, non-negative least angle regression, for which a suitable, statistically motivated early stopping criterion has been derived. The strength of NITPICK is the deconvolution of overlapping mixture mass spectra.

Conclusion: Extensive comparative evaluation has been carried out and results are provided for simulated and real-world data sets. NITPICK outperforms pepex, to date the only alternate, publicly available, non-greedy feature extraction routine. NITPICK is available as software package for the R programming language and can be downloaded from (http://hci.iwr.uni-heidelberg.de/mip/proteomics/).

Show MeSH
Related in: MedlinePlus