A novel comprehensive wave-form MS data processing method

Shuo Chen; Ming Li; Don Hong; Dean Billheimer; Huiming Li; Baogang J. Xu; Yu Shyr

doi:10.1093/bioinformatics/btp060

A novel comprehensive wave-form MS data processing method

Shuo Chen, Ming Li, Don Hong, Dean Billheimer, Huiming Li, Baogang J. Xu, Yu Shyr

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

15 Scopus citations

Abstract

Motivation: Mass spectrometry (MS) can generate high-throughput protein profiles for biomedical research to discover biologically related protein patterns-biomarkers. The noisy functional MS data collected by current technologies, however, require consistent, sensitive and robust data-processing techniques for successful biomedical application. Therefore, it is important to detect features precisely for each spectrum, quantify them well and assign a unique label to features from the same protein-peptide across spectra. Results: In this article, we propose a new comprehensive MS data preprocessing package, Wave-spec, which includes several novel algorithms. It can overcome several conventional difficulties. Wave-spec can be applied to multiple types of MS data generated with different MS technologies. Results from this new package were evaluated and compared to several existing approaches based on a MALDI-TOF MS dataset.

Original language	English (US)
Pages (from-to)	808-814
Number of pages	7
Journal	Bioinformatics
Volume	25
Issue number	6
DOIs	https://doi.org/10.1093/bioinformatics/btp060
State	Published - Mar 2009
Externally published	Yes

ASJC Scopus subject areas

Statistics and Probability
Biochemistry
Molecular Biology
Computer Science Applications
Computational Theory and Mathematics
Computational Mathematics

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10.1093/bioinformatics/btp060

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@article{7790d2ba3a484add876551f876b84316,

title = "A novel comprehensive wave-form MS data processing method",

abstract = "Motivation: Mass spectrometry (MS) can generate high-throughput protein profiles for biomedical research to discover biologically related protein patterns-biomarkers. The noisy functional MS data collected by current technologies, however, require consistent, sensitive and robust data-processing techniques for successful biomedical application. Therefore, it is important to detect features precisely for each spectrum, quantify them well and assign a unique label to features from the same protein-peptide across spectra. Results: In this article, we propose a new comprehensive MS data preprocessing package, Wave-spec, which includes several novel algorithms. It can overcome several conventional difficulties. Wave-spec can be applied to multiple types of MS data generated with different MS technologies. Results from this new package were evaluated and compared to several existing approaches based on a MALDI-TOF MS dataset.",

author = "Shuo Chen and Ming Li and Don Hong and Dean Billheimer and Huiming Li and Xu, {Baogang J.} and Yu Shyr",

note = "Funding Information: Funding: Lung Cancer Special Program of Research Excellence (SPORE, 2P50 CA090949-06A1); Breast Cancer SPORE (5P50 CA098131-05); GI SPORE (2P50 CA095103-06); Cancer Center Support Grant (CCSG, 5P30 CA068485-12).",

year = "2009",

month = mar,

doi = "10.1093/bioinformatics/btp060",

language = "English (US)",

volume = "25",

pages = "808--814",

journal = "Bioinformatics",

issn = "1367-4803",

publisher = "Oxford University Press",

number = "6",

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T1 - A novel comprehensive wave-form MS data processing method

AU - Chen, Shuo

AU - Li, Ming

AU - Hong, Don

AU - Billheimer, Dean

AU - Li, Huiming

AU - Xu, Baogang J.

AU - Shyr, Yu

N1 - Funding Information: Funding: Lung Cancer Special Program of Research Excellence (SPORE, 2P50 CA090949-06A1); Breast Cancer SPORE (5P50 CA098131-05); GI SPORE (2P50 CA095103-06); Cancer Center Support Grant (CCSG, 5P30 CA068485-12).

PY - 2009/3

Y1 - 2009/3

N2 - Motivation: Mass spectrometry (MS) can generate high-throughput protein profiles for biomedical research to discover biologically related protein patterns-biomarkers. The noisy functional MS data collected by current technologies, however, require consistent, sensitive and robust data-processing techniques for successful biomedical application. Therefore, it is important to detect features precisely for each spectrum, quantify them well and assign a unique label to features from the same protein-peptide across spectra. Results: In this article, we propose a new comprehensive MS data preprocessing package, Wave-spec, which includes several novel algorithms. It can overcome several conventional difficulties. Wave-spec can be applied to multiple types of MS data generated with different MS technologies. Results from this new package were evaluated and compared to several existing approaches based on a MALDI-TOF MS dataset.

AB - Motivation: Mass spectrometry (MS) can generate high-throughput protein profiles for biomedical research to discover biologically related protein patterns-biomarkers. The noisy functional MS data collected by current technologies, however, require consistent, sensitive and robust data-processing techniques for successful biomedical application. Therefore, it is important to detect features precisely for each spectrum, quantify them well and assign a unique label to features from the same protein-peptide across spectra. Results: In this article, we propose a new comprehensive MS data preprocessing package, Wave-spec, which includes several novel algorithms. It can overcome several conventional difficulties. Wave-spec can be applied to multiple types of MS data generated with different MS technologies. Results from this new package were evaluated and compared to several existing approaches based on a MALDI-TOF MS dataset.

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A novel comprehensive wave-form MS data processing method

Abstract

ASJC Scopus subject areas

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