Kazuo Murakami

Antisense transcripts with rice full-length cDNAs

BackgroundNatural antisense transcripts control gene expression through post-transcriptional gene silencing by annealing to the complementary sequence of the sense transcript. Because many genome and mRNA sequences have become available recently, genome-wide searches for sense-antisense transcripts have been reported, but few plant sense-antisense transcript pairs have been studied. The Rice Full-Length cDNA Sequencing Project has enabled computational searching of a large number of plant sense-antisense transcript pairs.ResultsWe identified sense-antisense transcript pairs from 32,127 full-length rice cDNA sequences produced by this project and public rice mRNA sequences by aligning the cDNA sequences with rice genome sequences. We discovered 687 bidirectional transcript pairs in rice, including sense-antisense transcript pairs. Both sense and antisense strands of 342 pairs (50%) showed homology to at least one expressed sequence tag other than that of the pair. Microarray analysis showed 82 pairs (32%) out of 258 pairs on the microarray were more highly expressed than the median expression intensity of 21,938 rice transcriptional units. Both sense and antisense strands of 594 pairs (86%) had coding potential.ConclusionsThe large number of plant sense-antisense transcript pairs suggests that gene regulation by antisense transcripts occurs in plants and not only in animals. On the basis of our results, experiments should be carried out to analyze the function of plant antisense transcripts.

Gene organization in rice revealed by full-length cDNA mapping and gene expression analysis through microarray.

Rice (Oryza sativa L.) is a model organism for the functional genomics of monocotyledonous plants since the genome size is considerably smaller than those of other monocotyledonous plants. Although highly accurate genome sequences of indica and japonica rice are available, additional resources such as full-length complementary DNA (FL-cDNA) sequences are also indispensable for comprehensive analyses of gene structure and function. We cross-referenced 28.5K individual loci in the rice genome defined by mapping of 578K FL-cDNA clones with the 56K loci predicted in the TIGR genome assembly. Based on the annotation status and the presence of corresponding cDNA clones, genes were classified into 23K annotated expressed (AE) genes, 33K annotated non-expressed (ANE) genes, and 5.5K non-annotated expressed (NAE) genes. We developed a 60mer oligo-array for analysis of gene expression from each locus. Analysis of gene structures and expression levels revealed that the general features of gene structure and expression of NAE and ANE genes were considerably different from those of AE genes. The results also suggested that the cloning efficiency of rice FL-cDNA is associated with the transcription activity of the corresponding genetic locus, although other factors may also have an effect. Comparison of the coverage of FL-cDNA among gene families suggested that FL-cDNA from genes encoding rice- or eukaryote-specific domains, and those involved in regulatory functions were difficult to produce in bacterial cells. Collectively, these results indicate that rice genes can be divided into distinct groups based on transcription activity and gene structure, and that the coverage bias of FL-cDNA clones exists due to the incompatibility of certain eukaryotic genes in bacteria.

Wavelet profiles: their application in Oryza sativa DNA sequence analysis

Here we introduce our application of the wavelet analysis method to DNA sequences. In the signal processing field, Fourier transform is popular for analyzing wave data. However, although this method can process frequency information, it fails to handle locational data. In contrast, the wavelet method accommodates both locational and frequency information for wave analysis. The wavelet method is now increasing in its importance for signal processing. Fast Fourier transform is already applied to biological sequence analysis using correlations. We introduce a new method, called wavelet profile, for biological sequence analysis. Our method is based on multiresolution analysis of wavelet transform, offering data decomposition in several scaling at the same time. We applied our wavelet profile method to identifying gene loci among O. sativa genomic sequences.

Bidirectional transcript pairs divided into five categories according to their patterns of exon-intron structure

Copyright information: Taken from Antisense transcripts with rice full-length cDNAsGenome Biology 2003;5(1):R5-R5.Published online 11 Dec 2003PMCID:PMC395737.Copyright