Global gene expression analysis by combinatorial optimization.

Adam Ameur; Erik Aurell; Mats Carlsson; Jakub Orzechowski Westholm

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PubMed · 15107026

Global gene expression analysis by combinatorial optimization.

Adam Ameur·Erik Aurell·Mats Carlsson·Jakub Orzechowski Westholm

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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 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.

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Algorithms Animals Computational Biology DNA Restriction Enzymes DNA, Complementary Databases as Topic Gene Expression Profiling Gene Expression Regulation Mice Models, Genetic Models, Statistical Models, Theoretical Oligonucleotide Array Sequence Analysis Polymerase Chain Reaction Software Statistics as Topic Journal Article Research Support, Non-U.S. Gov't

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Adam Ameur, Erik Aurell, Mats Carlsson, Jakub Orzechowski Westholm. 2004. Global gene expression analysis by combinatorial optimization.. https://pubmed.ncbi.nlm.nih.gov/15107026/

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