Electrophoretic signal comparison applied to mRNA differential display analysis.
Gene expression analysis by electrophoretic methods is currently limited by the labor-intensive visual evaluation of the electrophoretic signal profiles. For this purpose, we present a flexible approach to computer-assisted comparison of quantitative electrophoretic patterns between multiple expression signals. Gaussian curves are first fitted to the complex peak mixtures, and the resulting approximate signals are then aligned and compared on a peak-by-peak basis with respect to specific patterns defined by the investigator. The rationale of the method is to produce a compressed list of exceptional expression patterns quantified by a set of associated numeric features. A score value is attached to each pattern in such a way that large values identify the most potential findings to be focused on in visual analysis instead of the vast amount of original electrophoretic results. The validity of the method is demonstrated by analyzing a large set of electrophoretic data from mRNA differential display experiments monitoring changes in gene expression patterns in human colonic carcinoma. The automated identification of variously defined gene expression patterns agrees well with the visual evaluation of the same electropherograms. The general comparison approach may also be found useful with other gene expression profiling instruments.
Aittokallio
T
T
Turku Centre for Computer Science (TUCS), University of Turku, Turku, Finland. tero.aittokallio@utu.fi
Pahikkala
T
T
Ojala
P
P
Nevalainen
T J
TJ
Nevalainen
O
O
D000230:Adenocarcinoma / Q000737:chemistry / Q000235:genetics; D000368:Aged; D000465:Algorithms; D015179:Colorectal Neoplasms / Q000737:chemistry / Q000235:genetics*; D004591:Electrophoresis, Polyacrylamide Gel / Q000379:methods*; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D008297:Male; D008875:Middle Aged; D008956:Models, Chemical; D008957:Models, Genetic; D015233:Models, Statistical; D012333:RNA, Messenger / Q000032:analysis* / Q000737:chemistry; D012016:Reference Values; D016415:Sequence Alignment / Q000379:methods*; D013269:Stochastic Processes
D012333:RNA, Messenger