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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.

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LC-MS data analysis. A generic workflow for LC-MS data analysis: This figure illustrates key steps in a typical workflow, which comprise feature detection, spectra alignment and statistical evaluation.
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Figure 1: LC-MS data analysis. A generic workflow for LC-MS data analysis: This figure illustrates key steps in a typical workflow, which comprise feature detection, spectra alignment and statistical evaluation.

Mentions: Modern mass spectrometers can easily generate thousands of mass spectra in a short time. This wealth of information has sparked off the development of new, fully automated methods to analyze and process it. Fig. 1 gives an example of a generic data analysis workflow in a study using LC-MS. Stages of mass spectrometry-based proteomics in which algorithms can be applied are, among many others, low-level preprocessing such as the abovementioned feature detection and quantification [4-6], alignment and registration of LC-MS data sets [7-9] as well as the statistical evaluation [10] of the experiments. There also exist some software tools that offer all (or most) of these steps in one program [11-14]. For a recent review, we refer the interested reader to Müller et al. [15].


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)

LC-MS data analysis. A generic workflow for LC-MS data analysis: This figure illustrates key steps in a typical workflow, which comprise feature detection, spectra alignment and statistical evaluation.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: LC-MS data analysis. A generic workflow for LC-MS data analysis: This figure illustrates key steps in a typical workflow, which comprise feature detection, spectra alignment and statistical evaluation.
Mentions: Modern mass spectrometers can easily generate thousands of mass spectra in a short time. This wealth of information has sparked off the development of new, fully automated methods to analyze and process it. Fig. 1 gives an example of a generic data analysis workflow in a study using LC-MS. Stages of mass spectrometry-based proteomics in which algorithms can be applied are, among many others, low-level preprocessing such as the abovementioned feature detection and quantification [4-6], alignment and registration of LC-MS data sets [7-9] as well as the statistical evaluation [10] of the experiments. There also exist some software tools that offer all (or most) of these steps in one program [11-14]. For a recent review, we refer the interested reader to Müller et al. [15].

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