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Global gene expression analysis by combinatorial optimization
Swedish Inst. of Computer Science.
Swedish Inst. of Computer Science.
Swedish Inst. of Computer Science.
Swedish Inst. of Computer Science.
2004 (English)In: In Silico Biology, ISSN 1386-6338, Vol. 4, no 2, 225-241 p.Article 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 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.

Place, publisher, year, edition, pages
2004. Vol. 4, no 2, 225-241 p.
Keyword [en]
global gene expression, combinatorial optimization
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:kth:diva-55930PubMedID: 15107026Scopus ID: 2-s2.0-8444244045OAI: oai:DiVA.org:kth-55930DiVA: diva2:472174
Note
QC 20120117Available from: 2012-01-03 Created: 2012-01-03 Last updated: 2017-12-08Bibliographically approved

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