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pkDACLASS: Open source software for analyzing MALDI-TOF data.

Ndukum J, Atlas M, Datta S - Bioinformation (2011)

Bottom Line: However, reproducibility of the results using this technology was in question.Complete data analysis comprises data preprocessing, monoisotopic peak detection through statistical model fitting and testing, alignment of the monoisotopic peaks for multiple samples and classification of the normal and diseased samples through the detected peaks.The software provides flexibility to the users to accomplish the complete and integrated analysis in one step or conduct analysis as a flexible platform and reveal the results at each and every step of the analysis.

View Article: PubMed Central - PubMed

ABSTRACT

Unlabelled: In recent years, mass spectrometry has become one of the core technologies for high throughput proteomic profiling in biomedical research. However, reproducibility of the results using this technology was in question. It has been realized that sophisticated automatic signal processing algorithms using advanced statistical procedures are needed to analyze high resolution and high dimensional proteomic data, e.g., Matrix-Assisted Laser Desorption/Ionization Time-of-Flight (MALDI-TOF) data. In this paper we present a software package-pkDACLASS based on R which provides a complete data analysis solution for users of MALDITOF raw data. Complete data analysis comprises data preprocessing, monoisotopic peak detection through statistical model fitting and testing, alignment of the monoisotopic peaks for multiple samples and classification of the normal and diseased samples through the detected peaks. The software provides flexibility to the users to accomplish the complete and integrated analysis in one step or conduct analysis as a flexible platform and reveal the results at each and every step of the analysis.

Availability: The database is available for free at http://cran.r-project.org/web/packages/pkDACLASS/index.html.

No MeSH data available.


Flow chart of the algorithmic steps in pkDACLASS. The arrowsindicate direction of analysis with input raw data as the start and classificationas the last stage in the analysis.
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Figure 1: Flow chart of the algorithmic steps in pkDACLASS. The arrowsindicate direction of analysis with input raw data as the start and classificationas the last stage in the analysis.

Mentions: The software allows flexibility in terms of aligning the samples. A flow chartof the algorithm is provided in Figure 1 above. To demonstrate the superiorityof pkDACLASS using this dataset, a comparative study was carried out withtwo other existing peak detection methods namely LIMPIC [11] and PeakHarvester [12,13]. The LIMPIC method proposed by [11] used a Kaiser digitalmoving window filter to obtain smoothed signal, then subtracted a signal trendfor baseline removal. Once the baseline removal was completed, a localmaxima is used to find the most significant peaks after eliminating the featureswith intensities lower than a non-uniform threshold proportional to the noiselevel. Then, the detected peaks are classified as either protein or noise peaks onthe basis of their m/z values (see [11] for further details). On the other hand,the Peak Harvester [12,13] method utilizes existing database knowledge toestablish a linear equation between M the mean of a Poisson distribution andthe peptide’s molecular weight m which is known. More details provided in[12,13]. The number of peaks detected by pkDACLASS and the two othercompeting algorithms, number of peaks retained after alignment using ouralignment algorithm FLEC, alignment algorithm of caMassClass andSpecAlign [14] and the results of the classification performances of thealgorithms are reported in the supplementary document available throughhttp://www.susmitadatta.org/Supp/pkDACLASS.


pkDACLASS: Open source software for analyzing MALDI-TOF data.

Ndukum J, Atlas M, Datta S - Bioinformation (2011)

Flow chart of the algorithmic steps in pkDACLASS. The arrowsindicate direction of analysis with input raw data as the start and classificationas the last stage in the analysis.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3064853&req=5

Figure 1: Flow chart of the algorithmic steps in pkDACLASS. The arrowsindicate direction of analysis with input raw data as the start and classificationas the last stage in the analysis.
Mentions: The software allows flexibility in terms of aligning the samples. A flow chartof the algorithm is provided in Figure 1 above. To demonstrate the superiorityof pkDACLASS using this dataset, a comparative study was carried out withtwo other existing peak detection methods namely LIMPIC [11] and PeakHarvester [12,13]. The LIMPIC method proposed by [11] used a Kaiser digitalmoving window filter to obtain smoothed signal, then subtracted a signal trendfor baseline removal. Once the baseline removal was completed, a localmaxima is used to find the most significant peaks after eliminating the featureswith intensities lower than a non-uniform threshold proportional to the noiselevel. Then, the detected peaks are classified as either protein or noise peaks onthe basis of their m/z values (see [11] for further details). On the other hand,the Peak Harvester [12,13] method utilizes existing database knowledge toestablish a linear equation between M the mean of a Poisson distribution andthe peptide’s molecular weight m which is known. More details provided in[12,13]. The number of peaks detected by pkDACLASS and the two othercompeting algorithms, number of peaks retained after alignment using ouralignment algorithm FLEC, alignment algorithm of caMassClass andSpecAlign [14] and the results of the classification performances of thealgorithms are reported in the supplementary document available throughhttp://www.susmitadatta.org/Supp/pkDACLASS.

Bottom Line: However, reproducibility of the results using this technology was in question.Complete data analysis comprises data preprocessing, monoisotopic peak detection through statistical model fitting and testing, alignment of the monoisotopic peaks for multiple samples and classification of the normal and diseased samples through the detected peaks.The software provides flexibility to the users to accomplish the complete and integrated analysis in one step or conduct analysis as a flexible platform and reveal the results at each and every step of the analysis.

View Article: PubMed Central - PubMed

ABSTRACT

Unlabelled: In recent years, mass spectrometry has become one of the core technologies for high throughput proteomic profiling in biomedical research. However, reproducibility of the results using this technology was in question. It has been realized that sophisticated automatic signal processing algorithms using advanced statistical procedures are needed to analyze high resolution and high dimensional proteomic data, e.g., Matrix-Assisted Laser Desorption/Ionization Time-of-Flight (MALDI-TOF) data. In this paper we present a software package-pkDACLASS based on R which provides a complete data analysis solution for users of MALDITOF raw data. Complete data analysis comprises data preprocessing, monoisotopic peak detection through statistical model fitting and testing, alignment of the monoisotopic peaks for multiple samples and classification of the normal and diseased samples through the detected peaks. The software provides flexibility to the users to accomplish the complete and integrated analysis in one step or conduct analysis as a flexible platform and reveal the results at each and every step of the analysis.

Availability: The database is available for free at http://cran.r-project.org/web/packages/pkDACLASS/index.html.

No MeSH data available.