Systematic Analysis of microRNA Targeting Impacted by Small Insertions and Deletions in Human Genome

<div><p>MicroRNAs (miRNAs) are small noncoding RNA that play an important role in posttranscriptional regulation of mRNA. Genetic variations in miRNAs or their target sites have been shown to alter miRNA function and have been associated with risk for several diseases. Previous studies have focused on the most abundant type of genetic variations, single nucleotide polymorphisms (SNPs) that affect miRNA-mRNA interactions. Here, we systematically identified small insertions and deletions (indels) in miRNAs and their target sites, and investigated the effects of indels on miRNA targeting. We studied the distribution of indels in miRNAs and their target sites and found that indels in mature miRNAs, experimentally supported miRNA target sites and PAR-CLIP footprints have significantly lower density compared to flanking regions. We identified over 20 indels in the seed regions of miRNAs, which may disrupt the interactions between these miRNAs and their target genes. We also identified hundreds of indels that alter experimentally supported miRNA target sites. We mapped these genes to human disease pathways to identify indels that affect miRNA targeting in these pathways. We also used the results of genome-wide association studies (GWAS) to identify potential links between miRNA-related indels and diseases.</p> </div

Identifying Subspace Gene Clusters from Microarray Data Using Low-Rank Representation

<div><p>Identifying subspace gene clusters from the gene expression data is useful for discovering novel functional gene interactions. In this paper, we propose to use low-rank representation (LRR) to identify the subspace gene clusters from microarray data. LRR seeks the lowest-rank representation among all the candidates that can represent the genes as linear combinations of the bases in the dataset. The clusters can be extracted based on the block diagonal representation matrix obtained using LRR, and they can well capture the intrinsic patterns of genes with similar functions. Meanwhile, the parameter of LRR can balance the effect of noise so that the method is capable of extracting useful information from the data with high level of background noise. Compared with traditional methods, our approach can identify genes with similar functions yet without similar expression profiles. Also, it could assign one gene into different clusters. Moreover, our method is robust to the noise and can identify more biologically relevant gene clusters. When applied to three public datasets, the results show that the LRR based method is superior to existing methods for identifying subspace gene clusters.</p> </div

Location of somatic mutations in 3′UTRs.

<p>For each somatic mutation, the percentage of the distance from the start of the 3′UTR to the somatic mutation compared to the total length of the 3′UTR was calculated. The figure shows the number of mutations in rolling windows of 5% of the 3′UTR length for somatic mutations in (A) all cancer types, (B) lung cancer, (C) SCLC, (D) melanoma, and (E) prostate cancer.</p

Clustering and subspace clustering of a gene expression matrix: (A) a gene cluster must contain all columns, (B) subspace clusters correspond to arbitrary subsets of rows and columns, shown here as rectangles.

<p>Clustering and subspace clustering of a gene expression matrix: (A) a gene cluster must contain all columns, (B) subspace clusters correspond to arbitrary subsets of rows and columns, shown here as rectangles.</p

Enriched combinations of significant annotations of Biological Process of Cluster C17: (A) pie graph, (B) bar graph.

<p>Enriched combinations of significant annotations of Biological Process of Cluster C17: (A) pie graph, (B) bar graph.</p

Selected human disease pathways containing genes with indels and SNPs in miRNA target sites.

<p>Selected human disease pathways containing genes with indels and SNPs in miRNA target sites.</p

Genes in the pancreatic cancer pathway containing SNPs and indels that altered experimentally supported target sites.

<p>Genes containing only indels (pink), only SNPs (yellow), and both SNPs and indels (green) in target sites are within colored rectangles. The miRNAs that have been shown to target these genes are shown with red text for disrupted sites and blue text for created sites.</p

Two heatmaps of expression values of genes analyzed by the proposed algorithm from the yeast dataset: (A) a heatmap of expression values of genes in Cluster C17, and the heatmap shows similar expression patterns of genes in different samples, (B) a heatmap of expression values of genes in Cluster C14, and the heatmap shows different expression patterns of genes in different samples (denoted as a and b).

<p>Two heatmaps of expression values of genes analyzed by the proposed algorithm from the yeast dataset: (A) a heatmap of expression values of genes in Cluster C17, and the heatmap shows similar expression patterns of genes in different samples, (B) a heatmap of expression values of genes in Cluster C14, and the heatmap shows different expression patterns of genes in different samples (denoted as a and b).</p

Overview of the study.

<p>Somatic mutations within putative miRNA target sites are linked with cancer-related genes and miRNAs as well as the results of cancer association studies.</p

Density of all genetic variants (a) and indels (b) in dbSNP 135 as well as indels (c) from the GATK resource bundle in pre-miRNAs, mature miRNAs, miRNA seed regions, and flanking regions.

<p>Flanking regions 1 and 2 represent successive sequences adjacent to pre-miRNAs that were equal to the length of the pre-miRNA (∼100 bp). Error bars indicate the standard error. The density of all genetic variants (a) in pre-miRNAs was significantly different from the density in flanking regions, mature miRNAs, and seed regions (*p<0.01, **0.01</p