Global gene expression analysis by combinatorial optimization.
Generally, there is a trade-off between methods of gene expression analysis that are precise but labor-intensive, e.g. RT-PCR, and methods that scale up to global coverage but are not quite as quantitative, e.g. microarrays. In the present paper, we show how how a known method of gene expression profiling (K. Kato, Nucleic Acids Res. 23, 3685-3690 (1995)), which relies on a fairly small number of steps, can be turned into a global gene expression measurement by advanced data post-processing, with potentially little loss of accuracy. Post-processing here entails solving an ancillary combinatorial optimization problem. Validation is performed on in silico experiments generated from the FANTOM data base of full-length mouse cDNA. We present two variants of the method. One uses state-of-the-art commercial software for solving problems of this kind, the other a code developed by us specifically for this purpose, released in the public domain under GPL license.
Ameur
Adam
A
SICS, Swedish Institute of Computer Science, P.O. Box 1263, S-164 29 Kista, Sweden.
Aurell
Erik
E
Carlsson
Mats
M
Westholm
Jakub Orzechowski
JO
D000465:Algorithms; D000818:Animals; D019295:Computational Biology / Q000379:methods*; D004262:DNA Restriction Enzymes / Q000378:metabolism; D018076:DNA, Complementary / Q000378:metabolism; D019992:Databases as Topic; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation; D051379:Mice; D008957:Models, Genetic; D015233:Models, Statistical; D008962:Models, Theoretical; D020411:Oligonucleotide Array Sequence Analysis; D016133:Polymerase Chain Reaction; D012984:Software; D013223:Statistics as Topic / Q000379:methods
D018076:DNA, Complementary; D004262:DNA Restriction Enzymes