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. 2014;15 Suppl 7(Suppl 7):S2.
doi: 10.1186/1471-2105-15-S7-S2. Epub 2014 May 28.

Analysis of Interactions Between the Epigenome and Structural Mutability of the Genome Using Genboree Workbench Tools

Free PMC article

Analysis of Interactions Between the Epigenome and Structural Mutability of the Genome Using Genboree Workbench Tools

Cristian Coarfa et al. BMC Bioinformatics. .
Free PMC article

Abstract

Background: Interactions between the epigenome and structural genomic variation are potentially bi-directional. In one direction, structural variants may cause epigenomic changes in cis. In the other direction, specific local epigenomic states such as DNA hypomethylation associate with local genomic instability.

Methods: To study these interactions, we have developed several tools and exposed them to the scientific community using the Software-as-a-Service model via the Genboree Workbench. One key tool is Breakout, an algorithm for fast and accurate detection of structural variants from mate pair sequencing data.

Results: By applying Breakout and other Genboree Workbench tools we map breakpoints in breast and prostate cancer cell lines and tumors, discriminate between polymorphic breakpoints of germline origin and those of somatic origin, and analyze both types of breakpoints in the context of the Human Epigenome Atlas, ENCODE databases, and other sources of epigenomic profiles. We confirm previous findings that genomic instability in human germline associates with hypomethylation of DNA, binding sites of Suz12, a key member of the PRC2 Polycomb complex, and with PRC2-associated histone marks H3K27me3 and H3K9me3. Breakpoints in germline and in breast cancer associate with distal regulatory of active gene transcription. Breast cancer cell lines and tumors show distinct patterns of structural mutability depending on their ER, PR, or HER2 status.

Conclusions: The patterns of association that we detected suggest that cell-type specific epigenomes may determine cell-type specific patterns of selective structural mutability of the genome.

Figures

Figure 1
Figure 1
Breakout iterative grid clustering strategy. Breakout employs iterative grid clustering of mate pairs in three steps: (A) Identifies read pairs mapped on the same pair of chromosomes. (B) For each chromosome pair, it performs coarse-level greedy clustering. (C) It refines mate pair clusters. The example shows detection of a breakpoint between chromosomes 2 and 3.
Figure 2
Figure 2
Genboree Workbench interface. A user can explore a data tree containing various data types: sequencing results, tool results, in multiple formats, via a data selection panel. The user drags files to be used as tool inputs into the Input Data panel, and an output databases into the Output Targets panel. Tools which can run on the types of inputs and outputs selected are highlighted in the menu and are ready for launch using either default or user-specified parameters.
Figure 3
Figure 3
ROC-type curves for Breakout, VariationHunter, and GASV on the HCC1954 benchmark. Variation Hunter, optimized for normal genomes, lacks sensitivity for cancer genomes. For structural variants with high read coverage Breakout and GASV achieve similar performance. Breakout outperforms GASV with respect to sensitivity and specificity for somatic structural variants with low read coverage
Figure 4
Figure 4
Breakpoint analysis for the PC-3 prostate cancer cells. (A). Breakdown of the breakpoints detected for PC-3 and PrEC: 48% of the PC-3 breakpoints and 65% of the PrEC breakpoints overlap with the breakpoints called in the 1000 Genomes Project data; 25% of the PC-3 breakpoints are common with the PrEC breakpoints. (B) Circos Plot of the PC-3 breakpoints before the subtraction of the PrEC and the 1000 Genomes Project breakpoints. (C) Circos Plot of the PC-3 breakpoints after the subtraction of the PrEC and the 1000 Genomes Project breakpoints. (D) Enrichment of epigenomic features nearby PC-3 breakpoints.
Figure 5
Figure 5
Hierarchical clustering of breakpoint sets based on enrichment of germline methylation deserts. DNA methylation deserts were defined as the 100 Kbp windows with the 5% lowest methylation in sperm. The two methylation desert tracks each corresponds to average methylation in two sperm samples, as described in [2]. Note the strong association of germline hypomethylation with breakpoints in germline but not with putatively somatic breakpoints.
Figure 6
Figure 6
Hierarchical clustering of breakpoint sets based on enrichment for transcription factor binding and epigenomic features. Note the distinct patterns of enrichment for germline breakpoints (columns to the left) and putatively somatic breakpoints (columns to the right).
Figure 7
Figure 7
Patterns of enrichment segregating breast cancer subtypes by estrogen receptor status (ER), progesterone receptor status (PR), or HER2 status (ERBB2). (A) ER+ vs ER-; note that genes with the Pol2 mark and transcribed in MCF7, an ER+ cell line, are affected by breakpoints in ER- cells but not in ER+ cells. (B) PR+ vs PR-. (C) HER2+ vs HER2-.

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