The H19 gene, located at human chromosome 11p15.5, is imprinted in most normal human tissues. However, imprinting is often lost in tumors suggesting H19 is a putative tumor suppressor. We analyzed the single nucleotide polymorphisms (SNPs) of a 16 kb region that includes the H19 gene and its imprinting control region (ICR) in the Korean population. To identify SNPs, we directly sequenced this region in 18 Korean subjects. We identified 64 SNPs, of which 7 were in the exons of H19, 2 were in the introns, 14 were in the 3' intergenic region and 41 were in the 5' intergenic region. Of the 64 SNPs, 21 had not previously been reported and thus appear to be unique to the Korean population. The identified SNPs of H19 in the Korean population may eventually be useful as genetic markers associated with various diseases. In this study, 7 of the 64 identified SNPs were at CTCF binding sites in the ICR and may affect regulation of H19 gene imprinting. Thus, several genetic variations of the H19 gene may be important markers in human diseases that involve genomic imprinting, including cancer.
Binding Sites ; Chromosomes, Human* ; DNA, Intergenic ; Exons ; Genetic Markers ; Genetic Variation ; Genomic Imprinting ; Haplotypes ; Humans ; Humans* ; Introns ; Polymorphism, Single Nucleotide*

Numerous studies have reported that genes with similar expression patterns are co-regulated. From gene expression data, we have assumed that genes having similar expression pattern would share similar transcription factor binding sites (TFBSs). These function as the binding regions for transcription factors (TFs) and thereby regulate gene expression. In this context, various analysis tools have been developed. However, they have shortcomings in the combined analysis of expression patterns and significant TFBSs and in the functional analysis of target genes of significantly overrepresented putative regulators. In this study, we present a web-based A Functional Clustering Analysis Tool for Predicted Transcription Regulatory Elements and Gene Ontology Terms (FCAnalyzer). This system integrates microarray clustering data with similar expression patterns, and TFBS data in each cluster. FCAnalyzer is designed to perform two independent clustering procedures. The first process clusters gene expression profiles using the K-means clustering method, and the second process clusters predicted TFBSs in the upstream region of previously clustered genes using the hierarchical biclustering method for simultaneous grouping of genes and samples. This system offers retrieved information for predicted TFBSs in each cluster using Match(TM) in the TRANSFAC database. We used gene ontology term analysis for functional annotation of genes in the same cluster. We also provide the user with a combinatorial TFBS analysis of TFBS pairs. The enrichment of TFBS analysis and GO term analysis is statistically by the calculation of P values based on Fisher's exact test, hypergeometric distribution and Bonferroni correction. FCAnalyzer is a web-based, user-friendly functional clustering analysis system that facilitates the transcriptional regulatory analysis of co-expressed genes. This system presents the analyses of clustered genes, significant TFBSs, significantly enriched TFBS combinations, their target genes and TFBS-TF pairs.
Binding Sites ; Cluster Analysis* ; Gene Expression ; Gene Ontology* ; Transcription Factors ; Transcriptome