Dendrogram of H3K4me1 track clustering using “Similarity of relations to other sets of genomic features” and 14 gene ontology reference tracks.

<p>All sample pairs were placed into separate subclusters per tissue. The separation of sample pairs into their own subclusters was stronger in this case than in Figs <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0123261#pone.0123261.g001" target="_blank">1</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0123261#pone.0123261.g002" target="_blank">2</a>. The clustering was here performed according to the canonical case “Similarity of relations to other sets of genomic features”. Features were defined based on relative aggregated coverage (as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0123261#pone.0123261.g002" target="_blank">Fig 2</a>) across non-consecutive sets of genome positions. Each feature was aggregated across a set of positions defined by a particular reference track. The reference tracks were again based on gene regions associated with 14 randomly selected Gene Ontology (GO) terms. Standard Euclidian distance was used as distance between two feature vectors. Clustering was performed using standard hierarchical clustering with the average linkage criterion.</p

Dendrogram of H3K4me1 track clustering, using the “Direct sequence-level similarity” for the genomic region chr1-22.

<p>All samples, except the ones from brain, are placed into separate subclusters per tissue. The clustering were performed according to the canonical case “Direct sequence-level similarity”. Each individual bp-location of the genome were considered an independent feature. The distance between two feature vectors was defined as ratio of the intersection to the union of base pairs covered by two feature vectors. Clustering was performed using standard hierarchical clustering with the average linkage criterion.</p

Gene Ontology terms enriched by genes with different H3K4me1 occupancy in fetal and adult brain cell types.

<p>Table of the three top terms of the three top annotation clusters from David. The gene IDs submitted to DAVID were selected based on different H3K4me1 occupancy between fetal and adult brain clusters, by using the Genomic HyperBrowser. Benjamini = Benjamini-Hochberg.</p><p>Gene Ontology terms enriched by genes with different H3K4me1 occupancy in fetal and adult brain cell types.</p

Dendrogram of H3K4me1 track clustering using “Similarity of positional distribution along the genome” for the genomic region chr1-22.

<p>As in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0123261#pone.0123261.g001" target="_blank">Fig 1</a>, all sample pairs, except the one from brain, are placed into separate subclusters per tissue. The difference to the dendrogram of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0123261#pone.0123261.g001" target="_blank">Fig 1</a> is only the length of branches in the dendrogram. The clustering was here performed according to the canonical case “Similarity of positional distribution along the genome”. Features were defined based on relative aggregated coverage in bins (of consecutive positions) along the genome, where the raw base pair coverage in a bin was divided by the total base pair coverage of the track across the genome (to normalize for varying overall coverage densities between tracks). Bins were defined as consecutive 1 Mbps regions along all autosomes (set in the “region&scale” section of the interface). Standard Euclidian distance was used as distance between two feature vectors. Clustering was performed using standard hierarchical clustering with the average linkage criterion.</p

Heat map plot for H3K4me1 track clustering, using “Similarity of relations to other sets of genomic features” and 14 Gene Ontology (GO) based reference tracks.

<p>The value of a specific cell in the heat map represents the relative aggregated coverage of the corresponding H3K4me1 track (as specified in the row heading) in gene regions associated with a corresponding GO term (as specified in the column heading). The rows of the heat map correspond directly to the feature vectors used to calculate the dendrogram in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0123261#pone.0123261.g003" target="_blank">Fig 3</a>.</p

Integrative Analysis Reveals Relationships of Genetic and Epigenetic Alterations in Osteosarcoma

<div><h3>Background</h3><p>Osteosarcomas are the most common non-haematological primary malignant tumours of bone, and all conventional osteosarcomas are high-grade tumours showing complex genomic aberrations. We have integrated genome-wide genetic and epigenetic profiles from the EuroBoNeT panel of 19 human osteosarcoma cell lines based on microarray technologies.</p> <h3>Principal Findings</h3><p>The cell lines showed complex patterns of DNA copy number changes, where genomic copy number gains were significantly associated with gene-rich regions and losses with gene-poor regions. By integrating the datasets, 350 genes were identified as having two types of aberrations (gain/over-expression, hypo-methylation/over-expression, loss/under-expression or hyper-methylation/under-expression) using a recurrence threshold of 6/19 (>30%) cell lines. The genes showed in general alterations in either DNA copy number or DNA methylation, both within individual samples and across the sample panel. These 350 genes are involved in embryonic skeletal system development and morphogenesis, as well as remodelling of extracellular matrix. The aberrations of three selected genes, <em>CXCL5</em>, <em>DLX5</em> and <em>RUNX2</em>, were validated in five cell lines and five tumour samples using PCR techniques. Several genes were hyper-methylated and under-expressed compared to normal osteoblasts, and expression could be reactivated by demethylation using 5-Aza-2′-deoxycytidine treatment for four genes tested; <em>AKAP12, CXCL5</em>, <em>EFEMP1</em> and <em>IL11RA</em>. Globally, there was as expected a significant positive association between gain and over-expression, loss and under-expression as well as hyper-methylation and under-expression, but gain was also associated with hyper-methylation and under-expression, suggesting that hyper-methylation may oppose the effects of increased copy number for detrimental genes.</p> <h3>Conclusions</h3><p>Integrative analysis of genome-wide genetic and epigenetic alterations identified dependencies and relationships between DNA copy number, DNA methylation and mRNA expression in osteosarcomas, contributing to better understanding of osteosarcoma biology.</p> </div

Number of genes with alterations for two-way combinations.

<p>Plot of the number of genes with alterations for all 12 two-way combinations at different sample recurrence thresholds. The recurrence threshold of 6/19 cell lines (>30%) is indicated with a black line.</p

Hierarchical clustering of osteosarcoma cell lines and normal samples.

<p>Dendrograms from unsupervised hierarchical clustering of the osteosarcoma cell lines, normal bone and normal osteoblast samples based on genome-wide (A) mRNA expression (vst transformed and quantile normalised probe intensities), (B) DNA methylation (probe beta values) and (C) DNA copy number (probe intensities). The osteosarcoma cell lines have been colour-coded in gray and black, highlighting the two main subclusters, the normal bone samples in red and the normal osteoblast samples in blue. The clusters were made using Spearman correlation as distance measure and complete linkage.</p

Genomic location of 350 genes that recurrently showed two types of aberrations.

<p>Circos plot showing the genomic location of the 350 genes that recurrently showed two types of aberrations. The locations are colour-coded according to the type of aberrations; orange, hypo-methylation/over-expression; blue, hyper-methylation/under-expression; green, gain/over-expression; red, loss/under-expression; gray, (gain or hypo-methylation)/over-expression and (loss or hyper-methylation)/under-expression. The genomic location of the 14 homeobox genes in the list is indicated in the outermost circle.</p

Enrichment analysis of differentially methylated genes using DAVID.

<p>The first five terms in the first three clusters are shown, with enrichment score. The counts and population hits are the number of genes in the gene list and background gene list, respectively, mapping to a specific term. FDR, false discovery rate.</p>*<p>This cluster contained only three terms.</p