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The human interleukin-10 receptor (IL-10R) gene has previously been mapped to chromosome 11. Here, we have determined the precise location of the human IL-10R gene by the fluorescence in situ hybridization method, and have found that the IL-10R gene maps to chromosome 11q23.3.

Gene expression analysis based on comparison of electrophoretic patterns is strongly dependent on the accuracy of DNA fragment sizing. The current normalization strategy based on molecular weight markers has limited accuracy because marker peaks are often masked by intense peaks nearby. Cumulative errors in fragment lengths cause problems in the alignment of same-length fragments across different electropherograms, especially for small fragments (< 100 bp). For accurate comparison of electrophoretic patterns, further inspection and normalization of electrophoretic data after fragment sizing by conventional strategies is needed. Here we describe a method for the normalization of a set of time-course electrophoretic data to be compared. The method uses Gaussian curves fitted to the complex peak mixtures in each electropherogram. It searches for target ranges for which patterns are dissimilar to the other patterns (called "dissimilar ranges") and for references (a kind of mean or typical pattern) in the set of resultant approximate patterns. It then constructs the optimal normalized pattern whose correlation coefficient against the reference in the range achieves the highest value among various combinations of candidates. We applied the procedure to time-course electrophoretic data produced by HiCEP, an AFLP-based expression profiling method which can detect a slight expression change in DNA fragments. We obtained dissimilar ranges whose electrophoretic patterns were obviously different from the reference and as expected, most of the fragments in the detected ranges were short (< 100 bp). The normalized electrophoretic patterns also agreed well with reference patterns. The normalization strategy presented here demonstrates the importance of pre-processing before electrophoretic signal comparison, and we anticipate its usefulness especially for temporal expression analysis by the electrophoretic method.

We have developed an AFLP-based gene expression profiling method called 'high coverage expression profiling' (HiCEP) analysis. By making improvements to the selective PCR technique we have reduced the rate of false positive peaks to approximately 4% and consequently the number of peaks, including overlapping peaks, has been markedly decreased. As a result we can determine the relationship between peaks and original transcripts unequivocally. This will make it practical to prepare a database of all peaks, allowing gene assignment without having to isolate individual peaks. This precise selection also enables us to easily clone peaks of interest and predict the corresponding gene for each peak in some species. The procedure is highly reproducible and sensitive enough to detect even a 1.2-fold difference in gene expression. Most importantly, the low false positive rate enables us to analyze gene expression with wide coverage by means of four instead of six nucleotide recognition site restriction enzymes for fingerprinting mRNAs. Therefore, the method detects 70-80% of all transcripts, including non-coding transcripts, unknown and known genes. Moreover, the method requires no sequence information and so is applicable even to eukaryotes for which there is no genome information available.

Comparative sequence analyses have identified highly conserved genomic DNA sequences, including noncoding sequences, between humans and other species. By performing whole-genome comparisons of human and mouse, we have identified 611 conserved noncoding sequences longer than 500 bp, with more than 95% identity between the species. These long conserved noncoding sequences (LCNS) include 473 new sequences that do not overlap with previously reported ultraconserved elements (UCE), which are defined as aligned sequences longer than 200 bp with 100% identity in human, mouse, and rat. The LCNS were distributed throughout the genome except for the Y chromosome and often occurred in clusters within regions with a low density of coding genes. Many of the LCNS were also highly conserved in other mammals, chickens, frogs, and fish; however, we were unable to find orthologous sequences in the genomes of invertebrate species. In order to examine whether these conserved sequences are functionally important or merely mutational cold spots, we directly measured the frequencies of ENU-induced germline mutations in the LCNS of the mouse. By screening about 40.7 Mb, we found 35 mutations, including mutations at nucleotides that were conserved between human and fish. The mutation frequencies were equivalent to those found in other genomic regions, including coding sequences and introns, suggesting that the LCNS are not mutational cold spots at all. Taken together, these results suggest that mutations occur with equal frequency in LCNS but are eliminated by natural selection during the course of evolution.