A normalization strategy applied to HiCEP (an AFLP-based expression profiling) analysis: toward the strict alignment of valid fragments across electrophoretic patterns.
BACKGROUND
Gene expression analysis based on comparison of electrophoretic patterns is strongly dependent on the accuracy of DNA fragment sizing. The current normalization strategy based on molecular weight markers has limited accuracy because marker peaks are often masked by intense peaks nearby. Cumulative errors in fragment lengths cause problems in the alignment of same-length fragments across different electropherograms, especially for small fragments (< 100 bp). For accurate comparison of electrophoretic patterns, further inspection and normalization of electrophoretic data after fragment sizing by conventional strategies is needed.


RESULTS
Here we describe a method for the normalization of a set of time-course electrophoretic data to be compared. The method uses Gaussian curves fitted to the complex peak mixtures in each electropherogram. It searches for target ranges for which patterns are dissimilar to the other patterns (called "dissimilar ranges") and for references (a kind of mean or typical pattern) in the set of resultant approximate patterns. It then constructs the optimal normalized pattern whose correlation coefficient against the reference in the range achieves the highest value among various combinations of candidates. We applied the procedure to time-course electrophoretic data produced by HiCEP, an AFLP-based expression profiling method which can detect a slight expression change in DNA fragments. We obtained dissimilar ranges whose electrophoretic patterns were obviously different from the reference and as expected, most of the fragments in the detected ranges were short (< 100 bp). The normalized electrophoretic patterns also agreed well with reference patterns.


CONCLUSIONS
The normalization strategy presented here demonstrates the importance of pre-processing before electrophoretic signal comparison, and we anticipate its usefulness especially for temporal expression analysis by the electrophoretic method.
Kadota
Koji
K
Transcriptome Research Center, National Institute of Radiological Sciences (NIRS), 9-1, Anagawa-4-chome, Chiba-shi 263-8555, Japan. kadota@nirs.go.jp <kadota@nirs.go.jp>
Fukumura
Ryutaro
R
Rodrigue
Joseph J
JJ
Araki
Ryoko
R
Abe
Masumi
M
D000465:Algorithms; D000818:Animals; D019295:Computational Biology / Q000379:methods*; D004247:DNA / Q000737:chemistry; D016172:DNA Fingerprinting; D017931:DNA Primers / Q000737:chemistry; D030541:Databases, Genetic; D004586:Electrophoresis / Q000379:methods*; D020869:Gene Expression Profiling; D007091:Image Processing, Computer-Assisted; D051379:Mice; D008957:Models, Genetic; D015233:Models, Statistical; D008970:Molecular Weight; D016011:Normal Distribution; D020411:Oligonucleotide Array Sequence Analysis; D012150:Polymorphism, Restriction Fragment Length; D012333:RNA, Messenger / Q000378:metabolism; D019105:Random Amplified Polymorphic DNA Technique; D015203:Reproducibility of Results; D016415:Sequence Alignment; D017422:Sequence Analysis, DNA; D013997:Time Factors
D017931:DNA Primers; D012333:RNA, Messenger; D004247:DNA