ArticleAlgorithms for molecular biology : AMB2007
GOGOT: a method for the identification of differentially expressed fragments from cDNA-AFLP data.
Article in Algorithms for molecular biology : AMB, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Components of variance in transcriptomics based on electrophoretic separation of cDNA fragments (cDNA-AFLP).Electrophoresis · 2009Article
- Ranking differentially expressed genes from Affymetrix gene expression data: methods with reproducibility, sensitivity, and specificity.Algorithms for molecular biology : AMB · 2009Article
- Improved coverage of cDNA-AFLP by sequential digestion of immobilized cDNA.BMC genomics · 2008Article
- Analysis of manganese-regulated gene expression in the ligninolytic basidiomycete Ceriporiopsis subvermispora.Current genetics · 2008Article
- A weighted average difference method for detecting differentially expressed genes from microarray data.Algorithms for molecular biology : AMB · 2008Article
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Authors and funding
4 authors.
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Abstract
backgroundOne-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.
resultsWe 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.
conclusionGOGOT 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.
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