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LC-MSsim--a simulation software for liquid chromatography mass spectrometry data.

Schulz-Trieglaff O, Pfeifer N, Gröpl C, Kohlbacher O, Reinert K - BMC Bioinformatics (2008)

Bottom Line: The data resulting from an LC-MS experiment is huge, highly complex and noisy.Algorithm developers can match the results of feature detection and alignment algorithms against the simulated ion lists and meaningful error rates can be computed.We anticipate that LC-MSsim will be useful to the wider community to perform benchmark studies and comparisons between computational tools.

View Article: PubMed Central - HTML - PubMed

Affiliation: International Max Planck Research School for Computational Biology and Scientific Computing, Berlin, Germany. trieglaf@inf.fu-berlin.de

ABSTRACT

Background: Mass Spectrometry coupled to Liquid Chromatography (LC-MS) is commonly used to analyze the protein content of biological samples in large scale studies. The data resulting from an LC-MS experiment is huge, highly complex and noisy. Accordingly, it has sparked new developments in Bioinformatics, especially in the fields of algorithm development, statistics and software engineering. In a quantitative label-free mass spectrometry experiment, crucial steps are the detection of peptide features in the mass spectra and the alignment of samples by correcting for shifts in retention time. At the moment, it is difficult to compare the plethora of algorithms for these tasks. So far, curated benchmark data exists only for peptide identification algorithms but no data that represents a ground truth for the evaluation of feature detection, alignment and filtering algorithms.

Results: We present LC-MSsim, a simulation software for LC-ESI-MS experiments. It simulates ESI spectra on the MS level. It reads a list of proteins from a FASTA file and digests the protein mixture using a user-defined enzyme. The software creates an LC-MS data set using a predictor for the retention time of the peptides and a model for peak shapes and elution profiles of the mass spectral peaks. Our software also offers the possibility to add contaminants, to change the background noise level and includes a model for the detectability of peptides in mass spectra. After the simulation, LC-MSsim writes the simulated data to mzData, a public XML format. The software also stores the positions (monoisotopic m/z and retention time) and ion counts of the simulated ions in separate files.

Conclusion: LC-MSsim generates simulated LC-MS data sets and incorporates models for peak shapes and contaminations. Algorithm developers can match the results of feature detection and alignment algorithms against the simulated ion lists and meaningful error rates can be computed. We anticipate that LC-MSsim will be useful to the wider community to perform benchmark studies and comparisons between computational tools.

Show MeSH
Intensity distribution of a real mass spectrum. The intensity distribution (histogram and density plot) of raw data point intensities in a real mass spectrum. The plot shows that the true intensities match closely our Poisson-distributed intensity model.
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Figure 5: Intensity distribution of a real mass spectrum. The intensity distribution (histogram and density plot) of raw data point intensities in a real mass spectrum. The plot shows that the true intensities match closely our Poisson-distributed intensity model.

Mentions: To our knowledge, the notion of shot noise in mass spectrometry is much less well defined than in physics but usually loosely refers to high-frequency noise of low intensity in a mass spectrum. Noise models for mass spectra have been the topic of several publications, but no consensus on the most suitable model exists so far [52-54]. However, recent publications suggests that noise in both Q-TOF and Ion Trap spectra can be modeled using a Poisson distribution [53] and therefore we decided to do the same. We split each spectrum in our simulated LC-MS map into segments of uniform size. We determine the number of shot noise signals by sampling from a Poisson distribution, though m/z and intensity of these particles are given by a Gaussian and Exponential distribution, respectively. Fig. 5 shows the peak intensity distribution of a real MS scan. The distribution is approximately exponential with some signals (true peptide peaks) having a high intensity. This shows that our model with exponentially distributed noise intensities well approximates real signals.


LC-MSsim--a simulation software for liquid chromatography mass spectrometry data.

Schulz-Trieglaff O, Pfeifer N, Gröpl C, Kohlbacher O, Reinert K - BMC Bioinformatics (2008)

Intensity distribution of a real mass spectrum. The intensity distribution (histogram and density plot) of raw data point intensities in a real mass spectrum. The plot shows that the true intensities match closely our Poisson-distributed intensity model.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2577660&req=5

Figure 5: Intensity distribution of a real mass spectrum. The intensity distribution (histogram and density plot) of raw data point intensities in a real mass spectrum. The plot shows that the true intensities match closely our Poisson-distributed intensity model.
Mentions: To our knowledge, the notion of shot noise in mass spectrometry is much less well defined than in physics but usually loosely refers to high-frequency noise of low intensity in a mass spectrum. Noise models for mass spectra have been the topic of several publications, but no consensus on the most suitable model exists so far [52-54]. However, recent publications suggests that noise in both Q-TOF and Ion Trap spectra can be modeled using a Poisson distribution [53] and therefore we decided to do the same. We split each spectrum in our simulated LC-MS map into segments of uniform size. We determine the number of shot noise signals by sampling from a Poisson distribution, though m/z and intensity of these particles are given by a Gaussian and Exponential distribution, respectively. Fig. 5 shows the peak intensity distribution of a real MS scan. The distribution is approximately exponential with some signals (true peptide peaks) having a high intensity. This shows that our model with exponentially distributed noise intensities well approximates real signals.

Bottom Line: The data resulting from an LC-MS experiment is huge, highly complex and noisy.Algorithm developers can match the results of feature detection and alignment algorithms against the simulated ion lists and meaningful error rates can be computed.We anticipate that LC-MSsim will be useful to the wider community to perform benchmark studies and comparisons between computational tools.

View Article: PubMed Central - HTML - PubMed

Affiliation: International Max Planck Research School for Computational Biology and Scientific Computing, Berlin, Germany. trieglaf@inf.fu-berlin.de

ABSTRACT

Background: Mass Spectrometry coupled to Liquid Chromatography (LC-MS) is commonly used to analyze the protein content of biological samples in large scale studies. The data resulting from an LC-MS experiment is huge, highly complex and noisy. Accordingly, it has sparked new developments in Bioinformatics, especially in the fields of algorithm development, statistics and software engineering. In a quantitative label-free mass spectrometry experiment, crucial steps are the detection of peptide features in the mass spectra and the alignment of samples by correcting for shifts in retention time. At the moment, it is difficult to compare the plethora of algorithms for these tasks. So far, curated benchmark data exists only for peptide identification algorithms but no data that represents a ground truth for the evaluation of feature detection, alignment and filtering algorithms.

Results: We present LC-MSsim, a simulation software for LC-ESI-MS experiments. It simulates ESI spectra on the MS level. It reads a list of proteins from a FASTA file and digests the protein mixture using a user-defined enzyme. The software creates an LC-MS data set using a predictor for the retention time of the peptides and a model for peak shapes and elution profiles of the mass spectral peaks. Our software also offers the possibility to add contaminants, to change the background noise level and includes a model for the detectability of peptides in mass spectra. After the simulation, LC-MSsim writes the simulated data to mzData, a public XML format. The software also stores the positions (monoisotopic m/z and retention time) and ion counts of the simulated ions in separate files.

Conclusion: LC-MSsim generates simulated LC-MS data sets and incorporates models for peak shapes and contaminations. Algorithm developers can match the results of feature detection and alignment algorithms against the simulated ion lists and meaningful error rates can be computed. We anticipate that LC-MSsim will be useful to the wider community to perform benchmark studies and comparisons between computational tools.

Show MeSH