Bioinformatic analysis of regulation of natural antisense transcripts by transposable elements in human mRNA.

Non-coding RNA is no longer considered to be "junk" DNA, based on evidence uncovered in recent decades. In particular, the important role played by natural antisense transcripts (NATs) in regulating the expression of genes is receiving increasing attention. However, the regulatory mechanisms of NATs remain incompletely understood. It is well-known that the insertion of transposable elements (TEs) can affect gene transcription. Using a bioinformatics approach, we identified NATs using human mRNA sequences from the UCSC Genome Browser Database. Our in silico analysis identified 1079 NATs and 700 sense-antisense gene pairs. We identified 179 NATs that showed evidence of having been affected by TEs during cellular gene expression. These findings may provide an understanding of the complex regulation mechanisms of NATs. If our understanding of NATs as modulators of gene expression is further enhanced, we can develop ways to control gene expression.

Gene selection tool (GST): A R-based tool for genetic disorders based on the sliding-window proportion test using whole-exome sequencing data.

Whole-exome sequencing (WES) can identify causative mutations in hereditary diseases. However, WES data might have a large candidate variant list, including false positives. Moreover, in families, it is more difficult to select disease-associated variants because many variants are shared among members. To reduce false positives and extract accurate candidates, we used a multilocus variant instead of a single-locus variant (SNV). We set up a specific window to analyze the multilocus variant and devised a sliding-window approach to observe all variants. We developed the gene selection tool (GST) based on proportion tests for linkage analysis using WES data. This tool is R program coded and has high sensitivity. We tested our code to find the gene for hereditary spastic paraplegia using SNVs from a specific family and identified the gene known to cause the disease in a significant gene list. The list identified other genes that might be associated with the disease.