Automated detection of differentially expressed fragments in mRNA differential display

Abstract

We present a novel method for the automated detection of fragments showing dissimilar expression in mRNA differential display. The analysis is based on aligning the numerical electrophoretic lane data in respect of a given distance function defined on a set of fragments, or signal peaks in general. We presume that significant dissimilarities between peaks result in extreme score values computed for aligned peak pairs. Whereas in sequence comparison, an overall sequence similarity score is conventionally used, the current method defines a special dissimilarity score for searching the peak pairs showing the largest relative differences between the lanes. The output of the analysis is a highly reduced list of peak pairs, along with a set of associated features extracted from the lanes. Only the peaks of this list need to be visually confirmed instead of the vast amount of peaks in the original electrophoretic results. The results obtained by the algorithm correlate well with results of visual evaluation of the same electropherograms. The current algorithm may be applied to the study of complex expression patterns in multiple lanes and, in general, to automated recognition of variously defined patterns of quantitative electrophoretic data.

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

“…It is formulated as $true{\sum }_{i=1}^{n_k}{H}_i^k$ = constant for a set of electropherograms P k ( 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.…”

Conversion of cDNA differential display results (DDRT-PCR) into quantitative transcription profiles

“…In our procedure we completely separated band matching from the comparison of band intensities. Another potential problem with signal processing in [21] is the compensation for differences in loading volumes between lanes. Instead of normalizing lane intensities, the Size distribution of amplified cDNA fragments Figure 4 Size distribution of amplified cDNA fragments.…”

mRNA differential display of gene expression in colonic carcinoma

“…Allelic frequency can be confirmed by analyzing the corresponding fragments from the denaturing DD-PCR-gel. It is evident that this kind of approach is best solved by automated analysis of numerical DD-PCR data [20,21] produced in an automated multicapillary sequencer. A modern 384-capillary multicolor electrophoretic device can analyze several million DD-PCR fragments in 24 h. The 2880 electrophoretic samples of the current study could be analyzed in 3 h.…”

“…DD-PCR is based on mispriming and for that reason the method is only semiquantitative. At the same time, the method is competitive PCR by nature, and the relative levels of different fragments in a sample can thus be used to normalize the apparent expression levels of individual genes in a sample, and the corresponding fragments in different samples can be compared [21]. If the mispriming and the rest of the process can be rendered reproducible enough, even small changes in mRNA expression may be detected reliably.…”