Data pre-processing in liquid chromatography-mass spectrometry-based proteomics

Xiang Zhang; John M. Asara; Jiri Adamec; Mourad Ouzzani; Ahmed K. Elmagarmid

doi:10.1093/bioinformatics/bti660

Data pre-processing in liquid chromatography-mass spectrometry-based proteomics

Xiang Zhang^*, John M. Asara, Jiri Adamec, Mourad Ouzzani, Ahmed K. Elmagarmid

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

69 Citations (Scopus)

Abstract

Motivation: In a liquid chromatography-mass spectrometry (LC-MS)-based expressional proteomics, multiple samples from different groups are analyzed in parallel. It is necessary to develop a data mining system to perform peak quantification, peak alignment and data quality assurance. Results: We have developed an algorithm for spectrum deconvolution. At wo-step alignment algorithm is proposed for recognizing peaks generated by the same peptide but detected in different samples. The quality of LC-MS data is evaluated using statistical tests and alignment quality tests.

Original language	English
Pages (from-to)	4054-4059
Number of pages	6
Journal	Bioinformatics
Volume	21
Issue number	21
DOIs	https://doi.org/10.1093/bioinformatics/bti660
Publication status	Published - Nov 2005
Externally published	Yes

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abstract = "Motivation: In a liquid chromatography-mass spectrometry (LC-MS)-based expressional proteomics, multiple samples from different groups are analyzed in parallel. It is necessary to develop a data mining system to perform peak quantification, peak alignment and data quality assurance. Results: We have developed an algorithm for spectrum deconvolution. At wo-step alignment algorithm is proposed for recognizing peaks generated by the same peptide but detected in different samples. The quality of LC-MS data is evaluated using statistical tests and alignment quality tests.",

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AU - Asara, John M.

AU - Adamec, Jiri

AU - Ouzzani, Mourad

AU - Elmagarmid, Ahmed K.

PY - 2005/11

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AB - Motivation: In a liquid chromatography-mass spectrometry (LC-MS)-based expressional proteomics, multiple samples from different groups are analyzed in parallel. It is necessary to develop a data mining system to perform peak quantification, peak alignment and data quality assurance. Results: We have developed an algorithm for spectrum deconvolution. At wo-step alignment algorithm is proposed for recognizing peaks generated by the same peptide but detected in different samples. The quality of LC-MS data is evaluated using statistical tests and alignment quality tests.

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Data pre-processing in liquid chromatography-mass spectrometry-based proteomics

Abstract

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