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GOGOT: a method for the identification of differentially expressed fragments from cDNA-AFLP data.

Kadota K, Araki R, Nakai Y, Abe M - Algorithms Mol Biol (2007)

Bottom Line: The output of the analysis is a highly reduced list of differentially expressed TDFs.The validity of the automated ranking of TDFs by the special statistic was confirmed by the visual evaluation of a third party.The current algorithm may be applied to other electrophoretic data and temporal microarray data.

View Article: PubMed Central - HTML - PubMed

Affiliation: Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan. kadota@iu.a.u-tokyo.ac.jp

ABSTRACT

Background: One-dimensional (1-D) electrophoretic data obtained using the cDNA-AFLP method have attracted great interest for the identification of differentially expressed transcript-derived fragments (TDFs). However, high-throughput analysis of the cDNA-AFLP data is currently limited by the need for labor-intensive visual evaluation of multiple electropherograms. We would like to have high-throughput ways of identifying such TDFs.

Results: We describe a method, GOGOT, which automatically detects the differentially expressed TDFs in a set of time-course electropherograms. Analysis by GOGOT is conducted as follows: correction of fragment lengths of TDFs, alignment of identical TDFs across different electropherograms, normalization of peak heights, and identification of differentially expressed TDFs using a special statistic. The output of the analysis is a highly reduced list of differentially expressed TDFs. Visual evaluation confirmed that the peak alignment was performed perfectly for the TDFs by virtue of the correction of peak fragment lengths before alignment in step 1. The validity of the automated ranking of TDFs by the special statistic was confirmed by the visual evaluation of a third party.

Conclusion: GOGOT is useful for the automated detection of differentially expressed TDFs from cDNA-AFLP temporal electrophoretic data. The current algorithm may be applied to other electrophoretic data and temporal microarray data.

No MeSH data available.


Effect of peak height normalization by GOGOTnormH. Electropherograms when peak height normalizations are performed using (a) all the reported TDFs (a conventional method used in [12, 28]) and (b) a subset of the selected TDFs (GOGOTnormH).
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Figure 3: Effect of peak height normalization by GOGOTnormH. Electropherograms when peak height normalizations are performed using (a) all the reported TDFs (a conventional method used in [12, 28]) and (b) a subset of the selected TDFs (GOGOTnormH).

Mentions: One simple approach is to assume that the average peak height of all the reported TDFs among samples is approximately the same [12,28]. It is formulated as = constant for a set of electropherograms Pk (k = 1,..., m). However, this approach sometimes fails because it includes two kinds of questionable peaks [22]. One is peaks near a preset detection limit, resulting in some peaks being detected and others not (for example, two peaks at 217 bp and four at 223.5 bp in Fig. 3). The other is peaks incorrectly identified as either broad peaks or two overlapping peaks because of the similar appearance of these two types.


GOGOT: a method for the identification of differentially expressed fragments from cDNA-AFLP data.

Kadota K, Araki R, Nakai Y, Abe M - Algorithms Mol Biol (2007)

Effect of peak height normalization by GOGOTnormH. Electropherograms when peak height normalizations are performed using (a) all the reported TDFs (a conventional method used in [12, 28]) and (b) a subset of the selected TDFs (GOGOTnormH).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC1904450&req=5

Figure 3: Effect of peak height normalization by GOGOTnormH. Electropherograms when peak height normalizations are performed using (a) all the reported TDFs (a conventional method used in [12, 28]) and (b) a subset of the selected TDFs (GOGOTnormH).
Mentions: One simple approach is to assume that the average peak height of all the reported TDFs among samples is approximately the same [12,28]. It is formulated as = constant for a set of electropherograms Pk (k = 1,..., m). However, this approach sometimes fails because it includes two kinds of questionable peaks [22]. One is peaks near a preset detection limit, resulting in some peaks being detected and others not (for example, two peaks at 217 bp and four at 223.5 bp in Fig. 3). The other is peaks incorrectly identified as either broad peaks or two overlapping peaks because of the similar appearance of these two types.

Bottom Line: The output of the analysis is a highly reduced list of differentially expressed TDFs.The validity of the automated ranking of TDFs by the special statistic was confirmed by the visual evaluation of a third party.The current algorithm may be applied to other electrophoretic data and temporal microarray data.

View Article: PubMed Central - HTML - PubMed

Affiliation: Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan. kadota@iu.a.u-tokyo.ac.jp

ABSTRACT

Background: One-dimensional (1-D) electrophoretic data obtained using the cDNA-AFLP method have attracted great interest for the identification of differentially expressed transcript-derived fragments (TDFs). However, high-throughput analysis of the cDNA-AFLP data is currently limited by the need for labor-intensive visual evaluation of multiple electropherograms. We would like to have high-throughput ways of identifying such TDFs.

Results: We describe a method, GOGOT, which automatically detects the differentially expressed TDFs in a set of time-course electropherograms. Analysis by GOGOT is conducted as follows: correction of fragment lengths of TDFs, alignment of identical TDFs across different electropherograms, normalization of peak heights, and identification of differentially expressed TDFs using a special statistic. The output of the analysis is a highly reduced list of differentially expressed TDFs. Visual evaluation confirmed that the peak alignment was performed perfectly for the TDFs by virtue of the correction of peak fragment lengths before alignment in step 1. The validity of the automated ranking of TDFs by the special statistic was confirmed by the visual evaluation of a third party.

Conclusion: GOGOT is useful for the automated detection of differentially expressed TDFs from cDNA-AFLP temporal electrophoretic data. The current algorithm may be applied to other electrophoretic data and temporal microarray data.

No MeSH data available.