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Feature detection techniques for preprocessing proteomic data.

Sellers KF, Miecznikowski JC - Int J Biomed Imaging (2010)

Bottom Line: Numerous gel-based and nongel-based technologies are used to detect protein changes potentially associated with disease.Feature detection approaches are particularly interesting due to the increased computational speed associated with subsequent calculations.Such summary data corresponding to image features provide a significant reduction in overall data size and structure while retaining key information.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics and Statistics, Georgetown University, Washington, DC 20057, USA. kfs7@georgetown.edu

ABSTRACT
Numerous gel-based and nongel-based technologies are used to detect protein changes potentially associated with disease. The raw data, however, are abundant with technical and structural complexities, making statistical analysis a difficult task. Low-level analysis issues (including normalization, background correction, gel and/or spectral alignment, feature detection, and image registration) are substantial problems that need to be addressed, because any large-level data analyses are contingent on appropriate and statistically sound low-level procedures. Feature detection approaches are particularly interesting due to the increased computational speed associated with subsequent calculations. Such summary data corresponding to image features provide a significant reduction in overall data size and structure while retaining key information. In this paper, we focus on recent advances in feature detection as a tool for preprocessing proteomic data. This work highlights existing and newly developed feature detection algorithms for proteomic datasets, particularly relating to time-of-flight mass spectrometry, and two-dimensional gel electrophoresis. Note, however, that the associated data structures (i.e., spectral data, and images containing spots) used as input for these methods are obtained via all gel-based and nongel-based methods discussed in this manuscript, and thus the discussed methods are likewise applicable.

No MeSH data available.


Related in: MedlinePlus

Mass Spectrometry: The spectrum contains various kinds of noise that must be addressed via low-level analysis techniques. The focus of this paper addresses peak detection and quantification from such spectra.
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fig2: Mass Spectrometry: The spectrum contains various kinds of noise that must be addressed via low-level analysis techniques. The focus of this paper addresses peak detection and quantification from such spectra.

Mentions: Mass spectrometry is an analytic tool used to identify proteins, where the associated instrument (a mass spectrometer) measures the masses of molecules converted into ions via the mass-to-charge (m/z) ratio. This technology can be used to profile protein markers from tissue or bodily fluids, such as serum or plasma in order to compare biological samples from different patients or different conditions. Matrix assisted laser desorption and ionization—time of flight (MALDI-TOF) is a popular tool used by scientists, where a metal plate with the matrix containing the sample is placed into a vacuum chamber that is excited by a laser, causing the protein molecules to travel (or “fly”) through the tube until they strike a detector that records the time-of-flight for the various proteins under study; surface enhanced laser desorption and ionization—time of flight (SELDI-TOF) is an analog of MALDI-TOF. The interested reader is referred to [15] for discussion regarding the experimental design that creates the data, and elaboration on the MALDI and SELDI constructs. The resulting data are spectral functions containing the m/z ratio and associated intensity, where the peaks in the spectral plots correspond to proteins (or peptides) present in the sample; see Figure 2 for an example of a MALDI spectrum. The appeal of mass spectrometry lies in its ability to produce high-resolution measurements with reasonable reproducibility. These procedures generate large amounts of spectral data and can detect protein differential expression and modification in different treatment groups. Noisy data, however, can lead to a high rate of false positive peak identification. This is a significant issue when working to establish an unbiased, automated approach to detect protein changes, particularly in low-abundance proteins.


Feature detection techniques for preprocessing proteomic data.

Sellers KF, Miecznikowski JC - Int J Biomed Imaging (2010)

Mass Spectrometry: The spectrum contains various kinds of noise that must be addressed via low-level analysis techniques. The focus of this paper addresses peak detection and quantification from such spectra.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2864909&req=5

fig2: Mass Spectrometry: The spectrum contains various kinds of noise that must be addressed via low-level analysis techniques. The focus of this paper addresses peak detection and quantification from such spectra.
Mentions: Mass spectrometry is an analytic tool used to identify proteins, where the associated instrument (a mass spectrometer) measures the masses of molecules converted into ions via the mass-to-charge (m/z) ratio. This technology can be used to profile protein markers from tissue or bodily fluids, such as serum or plasma in order to compare biological samples from different patients or different conditions. Matrix assisted laser desorption and ionization—time of flight (MALDI-TOF) is a popular tool used by scientists, where a metal plate with the matrix containing the sample is placed into a vacuum chamber that is excited by a laser, causing the protein molecules to travel (or “fly”) through the tube until they strike a detector that records the time-of-flight for the various proteins under study; surface enhanced laser desorption and ionization—time of flight (SELDI-TOF) is an analog of MALDI-TOF. The interested reader is referred to [15] for discussion regarding the experimental design that creates the data, and elaboration on the MALDI and SELDI constructs. The resulting data are spectral functions containing the m/z ratio and associated intensity, where the peaks in the spectral plots correspond to proteins (or peptides) present in the sample; see Figure 2 for an example of a MALDI spectrum. The appeal of mass spectrometry lies in its ability to produce high-resolution measurements with reasonable reproducibility. These procedures generate large amounts of spectral data and can detect protein differential expression and modification in different treatment groups. Noisy data, however, can lead to a high rate of false positive peak identification. This is a significant issue when working to establish an unbiased, automated approach to detect protein changes, particularly in low-abundance proteins.

Bottom Line: Numerous gel-based and nongel-based technologies are used to detect protein changes potentially associated with disease.Feature detection approaches are particularly interesting due to the increased computational speed associated with subsequent calculations.Such summary data corresponding to image features provide a significant reduction in overall data size and structure while retaining key information.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics and Statistics, Georgetown University, Washington, DC 20057, USA. kfs7@georgetown.edu

ABSTRACT
Numerous gel-based and nongel-based technologies are used to detect protein changes potentially associated with disease. The raw data, however, are abundant with technical and structural complexities, making statistical analysis a difficult task. Low-level analysis issues (including normalization, background correction, gel and/or spectral alignment, feature detection, and image registration) are substantial problems that need to be addressed, because any large-level data analyses are contingent on appropriate and statistically sound low-level procedures. Feature detection approaches are particularly interesting due to the increased computational speed associated with subsequent calculations. Such summary data corresponding to image features provide a significant reduction in overall data size and structure while retaining key information. In this paper, we focus on recent advances in feature detection as a tool for preprocessing proteomic data. This work highlights existing and newly developed feature detection algorithms for proteomic datasets, particularly relating to time-of-flight mass spectrometry, and two-dimensional gel electrophoresis. Note, however, that the associated data structures (i.e., spectral data, and images containing spots) used as input for these methods are obtained via all gel-based and nongel-based methods discussed in this manuscript, and thus the discussed methods are likewise applicable.

No MeSH data available.


Related in: MedlinePlus