Limits...
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/).

Show MeSH

Related in: MedlinePlus

Comparison of the impact of averagine and fractional averagine stoichiometry estimationerrors on the estimation of theoretical isotope distributions.   The cumulative histograms of least squares deviations from the true theoretical isotope distribution illustrate the superior overall performance of fractional averagine (solid line) compared to Senko's classical averagine (dashed line):  fractional averagine causes a 17% decrease in mean squared error magnitude.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2655099&req=5

Figure 3: Comparison of the impact of averagine and fractional averagine stoichiometry estimationerrors on the estimation of theoretical isotope distributions. The cumulative histograms of least squares deviations from the true theoretical isotope distribution illustrate the superior overall performance of fractional averagine (solid line) compared to Senko's classical averagine (dashed line): fractional averagine causes a 17% decrease in mean squared error magnitude.

Mentions: Classical and fractional averagine were used to estimate approximate element stoichiometries and , respectively, for all peptides in the data set. Based on and , the corresponding theoretical isotope distribution intensity vectors and were calculated. Figure 3 shows the cumulative distribution of the squared differences between the classical averagine and the true theoretical isotope distribution intensity vectors (, dashed black), and fractional averagine and the true theoretical isotope distribution intensity vectors (, solid red).


NITPICK: peak identification for mass spectrometry data.

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

Comparison of the impact of averagine and fractional averagine stoichiometry estimationerrors on the estimation of theoretical isotope distributions.   The cumulative histograms of least squares deviations from the true theoretical isotope distribution illustrate the superior overall performance of fractional averagine (solid line) compared to Senko's classical averagine (dashed line):  fractional averagine causes a 17% decrease in mean squared error magnitude.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Comparison of the impact of averagine and fractional averagine stoichiometry estimationerrors on the estimation of theoretical isotope distributions. The cumulative histograms of least squares deviations from the true theoretical isotope distribution illustrate the superior overall performance of fractional averagine (solid line) compared to Senko's classical averagine (dashed line): fractional averagine causes a 17% decrease in mean squared error magnitude.
Mentions: Classical and fractional averagine were used to estimate approximate element stoichiometries and , respectively, for all peptides in the data set. Based on and , the corresponding theoretical isotope distribution intensity vectors and were calculated. Figure 3 shows the cumulative distribution of the squared differences between the classical averagine and the true theoretical isotope distribution intensity vectors (, dashed black), and fractional averagine and the true theoretical isotope distribution intensity vectors (, solid red).

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