Affiliations: SICS, Swedish Institute of Computer Science, P.O. Box
1263, SE-164 29 Kista, Sweden. Tel.: +46 8 633 15 00; Fax: +46 8 751 72 30;
E-mail: [email protected]
Note: [] Present address: KTH – Royal Institute of Technology, AlbaNova
University Center, SE-106 91 Stockholm, Sweden
Note: [] Corresponding author
Abstract: 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 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.
Keywords: global gene expression, combinatorial optimization
