Massive collection of full-length complementary DNA clones and microarray analyses: Keys to rice transcriptome analysis.
Abstract
Completion of the high-precision genome sequence analysis of rice led to the collection of about 35,000 full-length cDNA clones and the determination of their complete sequences. Mapping of these full-length cDNA sequences has given us information on (1) the number of genes expressed in the rice genome; (2) the start and end positions and exon–intron structures of rice genes; (3) alternative transcripts; (4) possible encoded proteins; (5) non-protein-coding (np) RNAs; (6) the density of gene localization on the chromosome; (7) setting the parameters of gene prediction programs; and (8) the construction of a microarray system that monitors global gene expression. Manual curation for rice gene annotation by using mapping information on full-length cDNA and EST assemblies has revealed about 32,000 expressed genes in the rice genome. Analysis of major gene families, such as those encoding membrane transport proteins (pumps, ion channels, and secondary transporters), along with the evolution from bacteria to higher animals and plants, reveals how gene numbers have increased through adaptation to circumstances. Family-based gene annotation also gives us a new way of comparing organisms. Massive amounts of data on gene expression under many kinds of physiological conditions are being accumulated in rice oligoarrays (22K and 44K) based on full-length cDNA sequences. Cluster analyses of genes that have the same promoter cis-elements, that have similar expression profiles, or that encode enzymes in the same metabolic pathways or signal transduction cascades give us clues to understanding the networks of gene expression in rice. As a tool for that purpose, we recently developed “RiCES”, a tool for searching for cis-elements in the promoter regions of clustered genes.
Citation
S. Kikuchi. Massive collection of full-length complementary DNA clones and microarray analyses: Keys to rice transcriptome analysis. In: QP–PQ: Quantum Probability and White Noise Analysis, Volume XXIV: Quantum Bio-Informatics II–From Quantum Information to Bio-Informatics (Accardi L, Freudenberg W and Ohya M, eds). Part of series QP–PQ: Quantum Probability and White Noise Analysis. World Scientific Publishing Co. Inc, Singapore, Singapore (2009) 265-289. ISBN 978-981-4273-74-9, 981-4273-74-0