Data reduction of isotope-resolved LC-MS spectra

Peicheng Du; Rajagopalan Sudha; Michael B. Prystowsky; Ruth Hogue Angeletti

doi:10.1093/bioinformatics/btm083

Data reduction of isotope-resolved LC-MS spectra

Peicheng Du
, Rajagopalan Sudha
, Michael B. Prystowsky
, Ruth Hogue Angeletti

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

25 Scopus citations

Abstract

Motivation: Data reduction of liquid chromatography-mass spectrometry (LC-MS) spectra can be a challenge due to the inherent complexity of biological samples, noise and non-flat baseline. We present a new algorithm, LCMS-2D, for reliable data reduction of LC-MS proteomics data. Results: LCMS-2D can reliably reduce LC-MS spectra with multiple scans to a list of elution peaks, and subsequently to a list of peptide masses. It is capable of noise removal, and deconvoluting peaks that overlap in m/z, in retention time, or both, by using a novel iterative peak-picking step, a 'rescue' step, and a modified variable selection method. LCMS-2D performs well with three sets of annotated LC-MS spectra, yielding results that are better than those from PepList, msInspect and the vendor software BioAnalyst.

Original language	English (US)
Pages (from-to)	1394-1400
Number of pages	7
Journal	Bioinformatics
Volume	23
Issue number	11
DOIs	https://doi.org/10.1093/bioinformatics/btm083
State	Published - Jun 2007

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/btm083

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title = "Data reduction of isotope-resolved LC-MS spectra",

abstract = "Motivation: Data reduction of liquid chromatography-mass spectrometry (LC-MS) spectra can be a challenge due to the inherent complexity of biological samples, noise and non-flat baseline. We present a new algorithm, LCMS-2D, for reliable data reduction of LC-MS proteomics data. Results: LCMS-2D can reliably reduce LC-MS spectra with multiple scans to a list of elution peaks, and subsequently to a list of peptide masses. It is capable of noise removal, and deconvoluting peaks that overlap in m/z, in retention time, or both, by using a novel iterative peak-picking step, a 'rescue' step, and a modified variable selection method. LCMS-2D performs well with three sets of annotated LC-MS spectra, yielding results that are better than those from PepList, msInspect and the vendor software BioAnalyst.",

author = "Peicheng Du and Rajagopalan Sudha and Prystowsky, \{Michael B.\} and Angeletti, \{Ruth Hogue\}",

note = "Funding Information: We acknowledge NIH for financial support: CA101150 (RHA) and CA103547 (MBP). Thanks to Dr. Frank Suits for helpful discussions and careful review of the manuscript. Funding for open-access will be provided by {\textquoteleft}{\textquoteleft}CA101150 (RHA){\textquoteright}{\textquoteright} from NIH.",

year = "2007",

month = jun,

doi = "10.1093/bioinformatics/btm083",

language = "English (US)",

volume = "23",

pages = "1394--1400",

journal = "Bioinformatics",

issn = "1367-4803",

publisher = "Oxford University Press",

number = "11",

}

TY - JOUR

T1 - Data reduction of isotope-resolved LC-MS spectra

AU - Du, Peicheng

AU - Sudha, Rajagopalan

AU - Prystowsky, Michael B.

AU - Angeletti, Ruth Hogue

N1 - Funding Information: We acknowledge NIH for financial support: CA101150 (RHA) and CA103547 (MBP). Thanks to Dr. Frank Suits for helpful discussions and careful review of the manuscript. Funding for open-access will be provided by ‘‘CA101150 (RHA)’’ from NIH.

PY - 2007/6

Y1 - 2007/6

N2 - Motivation: Data reduction of liquid chromatography-mass spectrometry (LC-MS) spectra can be a challenge due to the inherent complexity of biological samples, noise and non-flat baseline. We present a new algorithm, LCMS-2D, for reliable data reduction of LC-MS proteomics data. Results: LCMS-2D can reliably reduce LC-MS spectra with multiple scans to a list of elution peaks, and subsequently to a list of peptide masses. It is capable of noise removal, and deconvoluting peaks that overlap in m/z, in retention time, or both, by using a novel iterative peak-picking step, a 'rescue' step, and a modified variable selection method. LCMS-2D performs well with three sets of annotated LC-MS spectra, yielding results that are better than those from PepList, msInspect and the vendor software BioAnalyst.

AB - Motivation: Data reduction of liquid chromatography-mass spectrometry (LC-MS) spectra can be a challenge due to the inherent complexity of biological samples, noise and non-flat baseline. We present a new algorithm, LCMS-2D, for reliable data reduction of LC-MS proteomics data. Results: LCMS-2D can reliably reduce LC-MS spectra with multiple scans to a list of elution peaks, and subsequently to a list of peptide masses. It is capable of noise removal, and deconvoluting peaks that overlap in m/z, in retention time, or both, by using a novel iterative peak-picking step, a 'rescue' step, and a modified variable selection method. LCMS-2D performs well with three sets of annotated LC-MS spectra, yielding results that are better than those from PepList, msInspect and the vendor software BioAnalyst.

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UR - https://www.scopus.com/pages/publications/34447321056#tab=citedBy

U2 - 10.1093/bioinformatics/btm083

DO - 10.1093/bioinformatics/btm083

M3 - Article

C2 - 17496000

AN - SCOPUS:34447321056

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VL - 23

SP - 1394

EP - 1400

JO - Bioinformatics

JF - Bioinformatics

IS - 11

ER -

Data reduction of isotope-resolved LC-MS spectra

Abstract

ASJC Scopus subject areas

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