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GOGOT：一种从cDNA-AFLP数据中鉴定差异表达片段的方法。

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

作者信息

Kadota Koji, Araki Ryoko, Nakai Yuji, Abe Masumi

机构信息

Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan.

出版信息

Algorithms Mol Biol. 2007 May 30;2:5. doi: 10.1186/1748-7188-2-5.

DOI:10.1186/1748-7188-2-5
PMID:17535446
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC1904450/
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.

摘要

背景

使用cDNA-AFLP方法获得的一维（1-D）电泳数据对于鉴定差异表达的转录本衍生片段（TDF）引起了极大的兴趣。然而，目前cDNA-AFLP数据的高通量分析受到对多个电泳图谱进行劳动密集型视觉评估需求的限制。我们希望拥有高通量的方法来识别此类TDF。

结果

我们描述了一种名为GOGOT的方法，它可以自动检测一组时间进程电泳图谱中差异表达的TDF。GOGOT的分析过程如下：校正TDF的片段长度，跨不同电泳图谱比对相同的TDF，归一化峰高，并使用特殊统计方法识别差异表达的TDF。分析的输出是一份大幅精简的差异表达TDF列表。视觉评估证实，由于在步骤1比对前对峰片段长度进行了校正，TDF的峰比对非常完美。第三方的视觉评估证实了通过特殊统计方法对TDF进行自动排序的有效性。

结论

GOGOT可用于从cDNA-AFLP时间电泳数据中自动检测差异表达的TDF。当前算法可应用于其他电泳数据和时间微阵列数据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c665/1904450/cf31ebeaf799/1748-7188-2-5-5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c665/1904450/0a159f9467a6/1748-7188-2-5-1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c665/1904450/c8bfc9a0ba62/1748-7188-2-5-2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c665/1904450/bb8220888fd9/1748-7188-2-5-3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c665/1904450/5f6f199d85c1/1748-7188-2-5-4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c665/1904450/cf31ebeaf799/1748-7188-2-5-5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c665/1904450/0a159f9467a6/1748-7188-2-5-1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c665/1904450/c8bfc9a0ba62/1748-7188-2-5-2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c665/1904450/bb8220888fd9/1748-7188-2-5-3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c665/1904450/5f6f199d85c1/1748-7188-2-5-4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c665/1904450/cf31ebeaf799/1748-7188-2-5-5.jpg

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