Non-expressed HLA alleles become a potential problem in the transition of the HLA typing methodology from serologic typing to more accurate DNA typing. In this study, a novel nonexpressed A*24 allele identified from two members of a Korean family was characterized. At the DNA sequence level, the nonexpressed allele (A*24023) is apparently normal; the complete genomic sequence was identical to HLA-A*2402101, from the 5'-upstream region to the 3'-downstream region, except for a single silent substitution at codon 211 (GCG-->GCA) in exon 4. A DNA methylation analysis using methylation-sensitive restriction enzymes, however, showed that the nonexpressed A*24023 allele from an apparently normal individual was highly methylated in regions covering exons and introns as well as the 5'-upstream region. This result suggests that hypermethylation of the HLA-A gene may induce gene inactivation in the normal individuals.
Copy number variations (CNVs) are structural variants associated with human diseases. Recent studies verified that disease-related genes are based on the extraction of rare de novo and transmitted CNVs from exome sequencing data. The need for more efficient and accurate methods has increased, which still remains a challenging problem due to coverage biases, as well as the sparse, small-sized, and noncontinuous nature of exome sequencing. In this study, we developed a new CNV detection method, ExCNVSS, based on read coverage depth evaluation and scale-space filtering to resolve these problems. We also developed the method ExCNVSS_noRatio, which is a version of ExCNVSS, for applying to cases with an input of test data only without the need to consider the availability of a matched control. To evaluate the performance of our method, we tested it with 11 different simulated data sets and 10 real HapMap samples' data. The results demonstrated that ExCNVSS outperformed three other state-of-the-art methods and that our method corrected for coverage biases and detected all-sized CNVs even without matched control data.
RNA decay is an important regulatory mechanism for gene expression at the posttranscriptional level. Although the main pathways and major enzymes that facilitate this process are well defined, global analysis of RNA turnover remains under-investigated. Recent advances in the application of next-generation sequencing technology enable its use in order to examine various RNA decay patterns at the genome-wide scale. In this study, we investigated human RNA decay patterns using parallel analysis of RNA end-sequencing (PARE-seq) data from XRN1-knockdown HeLa cell lines, followed by a comparison of steady state and degraded mRNA levels from RNA-seq and PARE-seq data, respectively. The results revealed 1103 and 1347 transcripts classified as stable and unstable candidates, respectively. Of the unstable candidates, we found that a subset of the replication-dependent histone transcripts was polyadenylated and rapidly degraded. Additionally, we identified 380 endonucleolytically cleaved candidates by analyzing the most abundant PARE sequence on a transcript. Of these, 41.4% of genes were classified as unstable genes, which implied that their endonucleolytic cleavage might affect their mRNA stability. Furthermore, we identified 1877 decapped candidates, including HSP90B1 and SWI5, having the most abundant PARE sequences at the 5′-end positions of the transcripts. These results provide a useful resource for further analysis of RNA decay patterns in human cells.
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