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Global gene expression analysis by combinatorial optimization
Number of Authors: 4
2004 (English)In: In Silico Biology, ISSN 1434-3207, Vol. 4Article in journal (Refereed) Published
Abstract [en]

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 Research 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.

Place, publisher, year, edition, pages
2004, 4. Vol. 4
Keyword [en]
global gene expression, combinatorial optimization
National Category
Computer and Information Science
URN: urn:nbn:se:ri:diva-13654OAI: diva2:1034936
Available from: 2016-10-13 Created: 2016-10-13

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