Volume 15 Supplement 7
Analysis of interactions between the epigenome and structural mutability of the genome using Genboree workbench tools
© Coarfa et al.; licensee BioMed Central Ltd. 2014
Published: 28 May 2014
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.
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.
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.
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.
Historically, the first link ever discovered between chromatin structure and epigenetics was due to a structural genomic variant - a breakpoint induced by a chromosomal inversion on the × chromosome in Drosophila . This variant explained position-effect variegation of the Drosophila eye color by stochastic spreading of heterochromation across an inversion-induced breakpoint and stochastic silencing of the Drosophila eye color gene . We now also know that interactions between the genome and the epigenome may be bi-directional, one direction being exemplified by position-effect variegation of Drosophila's eye color, and the other direction suggested by our recent discovery that hypomethylation of genomic DNA in human germline associates with local genomic instability .
The opportunity to gain insights into epigenome-genome interactions is fast emerging. Structural genomic variants, including inversions, duplications, deletions, and translocations are being mapped on large scale in human germline and in cancer using mate-pair sequencing. A number of informatics challenges are yet to be addressed before this sequencing data becomes useful for analysis in the context of the epigenome. The detection algorithms must achieve sufficient accuracy to enable genome-scale correlation of chromosomal breakpoints with epigenomic features even without costly validation experiments. The structural aberrations should be interpretable in the context of rapidly growing public databases of transcription factor binding sites and epigenomic profiles. One practical challenge is the deployment of multiple analysis tools and databases with reproducible and transparent records of analyses.
To address these challenges we developed a structural variant analysis toolset, the Structural Variants Analysis Toolset, and deployed it using the Genboree Workbench, a collaborative environment for integrative analysis of genomic and epigenomic data. A key tool within the toolset is Breakout, a parallel algorithm for breakpoint calling from mate-pair reads that, when compared to state-of-the-art tools, achieves superior sensitivity and specificity. Breakout retains sensitivity even for low read coverage and is therefore suitable for profiling samples where only a small fraction of cells carry specific aberrations such as tumor samples with low tumor cell purity. Additional tools within the toolset enable integrative analysis using structural variation databases, such as the one generated by the 1000 Genomes Project [3–5], and other genomic and epigenomic databases required for integrative interpretation of breakpoint data, including ENCODE [6, 7] and the Human Epigenome Atlas produced by the NIH Epigenomic Roadmap Project .
We herein apply the Genboree Workbench tools to address a number of questions regarding the interactions of the epigenome with structural variation of the genome. We examine those interactions in both directions. In one direction, we identify structural genomic variants in cancer and their association with epigenomic changes in cis. In the other direction, we validate and then extend our recent discovery that hypomethylation of genomic DNA in human germline associates with local genomic instability . We validate the association at high resolution, using mate-pair sequencing data, by performing an extensive meta-analysis using ChIP-Seq data and epigenomic marks from ENCODE, Cistrome and other projects. Finally, we expand our analysis beyond germline to genomic instability in breast cancer and discover cell-type specific associations between the epigenome and selective structural mutability.
Breakpoint calling using Breakout
To discover breakpoints in tumor samples using long insert (4-6 Kbp) mate pairs, available widely on platforms such as SOLiD, Illumina, or 454, we developed Breakout, a novel chromosomal breakpoints detection algorithm and software package. To harness the parallelism of multicore processors, Breakout decomposes analysis steps into balanced segments that can be processed independently. The input consists of SAM/BAM  files containing the uniquely mapped mate pairs, as produced by mapping programs such as BFAST , bwa [11, 12], or Pash . Breakout calculates the distribution of insert sizes for all mate pairs with both ends mapped on the same chromosome in expected strand orientation. The consistent mate pair range Imin to Imax is calculated to capture the 0.5-99.5 percentile range of the distribution. A user can override these bounds and select a custom range instead.
Breakout splits mappings of forward and reverse reads into balanced groups. For each read group, it hashes forward read mappings, and then streams over the reverse read mappings. It separates the mate pairs into two classes: consistent mate pairs, defined as those with read mappings on the same chromosome, with the expected relative strand orientation, and within the insert size range (Imin, Imax), and inconsistent mate pairs.
Integrating breakpoint information from multiple samples in the context of known structural polymorphisms and other genomic information.
When mapping breakpoints in cancer cells it is of interest to identify the breakpoints that are likely of germline origin by comparing against the breakpoints detected using the mate-pairs from the 1000 Genomes project and other databases of structural polymorphisms. Genboree Workbench enables such integration via a collection of tools.
The Collect Insert Size tool quantifies the distribution of the mate pair insert size, and suggests to the user a range for lower and upper insert size bounds corresponding to the 0.5-99.5 percentiles of the cumulative insert size distribution.
The Intersect SVs tool performs elementary set operations of intersection and difference on Breakout outputs. Overlapping breakpoints are considered identical for the purpose of these set operations and are defined as follows. Let breakpoint B1 have the coordinates chrA:[b1,e1]-chrB[b2,e2] and the breakpoint B2 have the coordinates chrA:[b1',e1']-chrB:[b2',e2']. Let Imax be the maximum insert size for the two experiments. Breakpoints B1 and B2 overlap if min(|b1-b1'|,|e1-e1'|,|b1-e1'|,|e1-b1'|) <=Imax and min(|b2-b2'|,|e2-e2'|,|b2-e2'|,|e2-b2'|) <=Imax.
The Report Multiple SVs tool calculates overlaps among breakpoint sets from multiple samples or between breakpoints and or other genomic features. For each input set, the tool reports at breakpoint level overlaps with breakpoints from other input sets, with RefSeq genes, and even with custom genomic features uploaded by a user. This is useful for distinguishing polymorphic germline breakpoints from breakpoints detected in cancer genomes. Whereas some tumor samples are sequenced in parallel with normal controls, many patient samples and most cell lines have no available germline reference. We computed breakpoints based on long insert mate pairs datasets from the 1000 Genomes project database , and by running the Report Multiple SVs tool we could identify overlapping breakpoints and thus separate the breakpoints detected in cancer samples into two groups - the overlapping ones that are likely of germline origin and the non-overlapping ones that are putatively of somatic origin.
Integrative analysis of epigenomic features and breakpoints
The amount of epigenomic data that is becoming publicly available is rapidly growing [14–18], making it possible to explore epigenomic correlates of chromosomal aberrations on a large scale. One specific epigenomic data set is the Human Epigenome Atlas (http://www.epigenomeatlas.org), generated by the NIH Epigenomic Roadmap Initiative.
To enable a basic exploration of the epigenomic correlates of breakpoints, we developed the Epigenomic Enrichment tool, which determines enrichment of epigenomic features in the vicinity of breakpoints. In some use cases, the enrichment may suggest that the epigenomic features cause genomic instability. The inputs are structural variation tracks and the tracks containing discrete epigenomic features such as histone modification peaks, areas of low/intermediate/high methylation. Enrichment values are calculated separately for each class of variant: deletions, insertions, inversions, and translocations. In the first step of the algorithm, epigenomic features within a user-defined window (50,000 bp default) surrounding the breakpoints are identified. Next, a permutation test determines the number of features expected to occur by chance around breakpoints and reports the average enrichment and the associated p-value.
Integration of the tools within the Genboree Workbench
The Genboree Workbench was designed around a core of robust principles. Tools are exposed to users via a uniform graphical interface: the user is presented with a data tree, a data object details panel, an input panel, and an output panel (Figure 2). Tools are available in the cascading menu at the top of the page. The data tree displays the natural hierarchy of object within Genboree: user groups have one or more databases for holding the data and within a database are several types of entities--including data files such as FASTA sequence files, SAM/BAM files, Excel spreadsheets with clinical metadata, results of previous tool runs, etc. When selected in the data selection tree, the details of the object are displayed in a dedicated panel. The researcher drags-and-drops input data from the tree into the Input Data panel, and drags a suitable database in which to store the tool results into the Output Targets panel. Tools whose input and output criteria have been met are highlighted in the tool menu which, when clicked, cause the appropriate dialog to appear for reviewing and customizing tool-specific parameters. Wherever possible, Workbench tool dialogs will have sensible default parameters defined to handle the most common scenarios. Each tool has a help dialog describing each parameter, as well as the input and output panel criteria for the tool.
Some tools operate on the result files or entire result folders of other tools, and file format conversion are handled automatically. In all cases, the user is informed whether the tool job has been accepted, rejected (and why), or if they first need to confirm a warning condition. Tools are queued for execution along with jobs submitted by other users and generally run on a first-come, first-serve basis. The Workbench is also the primary means for sharing tool results with collaborators and downloading the result file(s), which are generally compressed to save storage space and network bandwidth. The Genboree Workbench can be accessed at http://genboree.org/java-bin/workbench.jsp, using browsers such as Internet Explorer, version 8 and higher, and Mozilla Firefox, version 6 and higher. A tutorial and sample datasets are available at http://genboree.org/breakpoints.
Breakout and the epigenome enrichment analysis tools can be downloaded at http://www.brl.bcm.tmc.edu/breakout/breakoutDownload.rhtml.
Breakout exhibits superior performance on low-coverage breakpoint detection
To compare Breakout with other structural variation analysis tools, we used as a benchmark structural variants in the HCC1954 breast cancer cell line. HCC1954 was previously characterized using high-coverage mapping with short insert size pairs and high stringency validation by Stephens et al . The set of 244 previously reported somatic variants  were used as a positive control. An HCC1954 dataset was sequenced using the SOLiD, yielding 72 million mate pairs; the reads were mapped onto the human genome, NCBI Build 36/UCSC hg18 using bfast .
Overlap with previously published and validated set of 244 somatic variants in HCC1954.
Intra- chromosomal breakpoints
1 read pair
VariationHunter, 3 read pairs
Breakout, 3 read pairs
GASV, 3 read pairs
1 read pair
Breakout, 3 read pairs
GASV, 3 read pairs
The Genboree Toolset accurately detects genes affected by putatively somatic mutations in the PC-3 prostate cancer cell line
To validate our integrative analysis toolset, we analyzed structural variation in PC-3, a metastatic prostate cancer cell line, and PrEC, a non-tumorigenic epithelial prostate cell line. The two cell lines were sequenced using SOLiD mate pair sequencing, at clone coverage of 8.5x for PC-3 and 10.1x for PrEC, and mapped to NCBI Build 37/hg19 using Bfast. Breakout identified a total of 382 breakpoints in PC-3 and 1184 in PrEC, using a minimum threshold of at least 3 mate pairs per breakpoint.
We further determined breakpoints that might affect a RefSeq gene by occurring within 2kb of a gene. A subset of these breakpoints not present in PrEC or the 1000 genomes dataset were validated using PCR sequencing. The PCR primers for cross-breakpoint PCR were designed using the basic pipeline described in our earlier breast cancer study . The overall PCR validation rate for translocations was 80% (8 out of 10). For a subset of structural variants with potential gene fusions, PCR primers and conditions were further optimized, leading to a validation rate of 83% (5 out of 6). The set of 193 structural variants unique to PC-3 was used to nominate 202 potentially affected genes for further study, including known translocated oncogenes MSI2 and RAD51L1(RAD51B) .
The Genboree Toolset detects epigenomically mediated regulation of genes affected by somatic aberrations
The set of 193 putatively somatic structural variants unique to PC-3 and the 202 potentially affected genes were next analyzed for enrichment of TF binding sites and other epigenomic features. Gene ontology and pathway analysis (GSEA) [26, 27] revealed a significant enrichment in genes with promoter regions containing a progesterone receptor motif (p = 5.95 × 10-5; GSEA motif V$PR_01 ), as well as for homeobox gene MEIS1 (p = 1.12 × 10-9), ESRRA (estrogen-related receptor alpha, p = 3.47 × 10-4), and FOXA1 (p = 7.82 × 10-4).
Using the Epigenomic Enrichment tool, we quantified the enrichments of a large set of epigenomic features determined for the LnCAP prostate cancer cell line  around the putative somatic breakpoints from our PC-3 dataset. Significant enrichment was discovered (p < 0.05) for the active chromatin mark H3K4me3 and for PolII around insertions, for the transcription elongation mark H3K36me3 around both insertions and translocations; loss of the repressive marks H3K9me3 and H3K27me3 was discovered around translocations, as shown in Figure 4D.
Breakpoints present in human germline strongly associate with genomic hypomethylation
We have recently shown that hypomethylation of genomic DNA in human germline marks unstable regions in the human genome instability . We set out to validate this association at higher resolution using mate-pair sequencing data. Because the epigenomes of breast cancer cells differ from those in human germline [30, 31], we also asked if the pattern of structural mutability in breast cancer cells differ from the pattern of structural polymorphisms that arose in human germline. To answer these questions, we used three sets of maps of chromosomal breakpoints in breast cancer cell lines and tumors. The first breast cancer set was a map of aberrations we obtained from three breast cancer cell lines (HCC1954, MDA-MB-231, and MDA-MB-361) and two breast tumors. The tumor samples were obtained as anonymized samples from the National Breast Cancer Tissue Resource of NIH P50CA58183, maintained at the Baylor College of Medicine. All five samples were sequenced using SOLiD mate pair sequencing at a clone coverage of 9.7-13.5x. Breakpoints for all five samples were computed and analyzed using the toolset, and separated into germline and somatic enriched.
We imported additional breast cancer datasets from large studies [20, 32] of breast tumors; the breakpoints were experimentally validated as being somatic. To ensure sufficient coverage of each of sample, we focused our analysis on samples that contained at least ten translocations.
Distinct patterns of epigenomic associations for structural mutability between germline and breast cancer
To further compare the pattern of structural mutability in breast cancer with the pattern of structural polymorphisms that arose in human germline, we analyzed enrichment scores for transcription factor binding sites and other epigenomic features around putative somatic and polymorphic breakpoints. We employed a total of 259 epigenomic feature tracks from the following sources: 148 transcription factor binding tracks generated by the ENCODE project [6, 7]; 66 transcription factor binding tracks and 25 other epigenomic marks in breast cancer from the Cistrome database [33, 34]; and 20 normal epigenomic tracks from the Human Epigenome Atlas Release 2 .
The enrichment score for 74 discriminating features was higher near somatic breakpoints, and for the remaining 33 discriminating epigenomic features it was higher near germline breakpoints. Among the 74 features enriched around somatic breakpoints were ESR1 (8 datasets, in 7 cases after treatment with estradiol), FoxA1 (2 datasets), PGR, and SRC-3. We observed strong enrichment around somatic translocations for 49 transcription factors profiled by the ENCODE project, including GATA-1/2, c-Jun, CEBPB, FoxA1, ERα, and p300. The enrichment patterns support previous results suggesting the role of ER in genomic instability. Specifically, we found strong and consistent enrichment for the estrogen receptor binding around somatic translocations. Patterns were similar for both ER-positive and ER-negative breast cancers, consistent with previous results [35–37]. To examine possible interactions between ESR1 and other transcription factors, for each of the ESR1 experiment, we identified somatic breakpoints within 50 kb of an ESR1 binding site, and then identified genes within 10 kb of such breakpoints. A total of 40 genes were identified by this method in least three cancer samples. Enrichment analysis of the 40 genes by GSEA indicated enrichment for ETS1 (p-value = 3.23 × 10-4) and p53 (p-value = 3.71 × 10-4) binding in promoter regions of these genes. The enrichment patterns suggest that transcription factors either individually or as part of larger complexes may promote genomic instability. Enrichment analysis around germline breakpoints (illustrated in Figure 6) revealed striking enrichment for binding sites of Suz12, a key member of the PRC2 Polycomb complex. This confirms our previous independent finding that PRC2 strongly associates with genomic instability in human germline . Moreover, histone marks H3K27me3 and H3K9me3 show similar enrichment pattern, consistent with the established role of EZH2 and Suz12, the two constituent members of the PRC2 complex as respective "writers" of H3K27me3 and H3K9me3.
Somatic breakpoints associate with distal regulatory sites of active gene transcription
We examined enrichment patterns differences of the epigenomic marks from the Cistrome database mapped specifically in breast cancer cell lines. Within 50 Kbp of somatic breakpoints we found enrichment of H3K4me1, H3K9ac, and weak enhancers (defined according to ) and depletion of H3K27me3, H3K9me3, and H3K4me3. In summary, somatic translocations tend to preferentially occur at distal regulatory sites (enhancers) that carry open chromatin marks associated with active gene transcription, as opposed to promoters, gene bodies (actively transcribed or not), or areas with inactive chromatin marks.
We next examined genes regulated by mutable enhancers and, using gene set analysis, searched for other regulators that may be indirectly associated with genomic instability. For each of the enriched enhancer datasets, we identified somatic breakpoints within 50 kb of an enhancer site, then the genes within 10 kb of such translocations. After limiting the genes to those nominated in at least 2 somatic samples, we obtained a list of 127 individual genes, including known oncogenes such as ERBB2, IGF1R, and MYC. Using the ENCODE dataset, we found enrichments for p300 binding in the vicinity of somatic translocations. Gene ontology and pathway analysis (GSEA) [26, 27] revealed a significant enrichment in genes with promoter regions containing motifs bound by MEIS1 (p-value = 8.12 × 10-9.), MYOD1 (p-value = 5.18 × 10-6), ETS2 (p-value = 4.57 × 10-5), and P53 (p-value = 9.17 × 10-4).
Distinct patterns of structural mutability in breast cancer cell lines and tumors
The challenges of identifying, integrating and interpreting chromosomal aberrations in cancer in the context of the epigenome can be effectively addressed using the Breakout algorithm and related tools within the Genboree Workbench. Researchers are empowered to translate the results of large genome-wide experiments into meaningful and experimentally testable hypotheses. The Genboree Workbench framework allows integration of other breakpoint callers, visualization packages, and additional integrative analysis tools.
By applying Breakout and other Genboree Workbench tools we mapped breakpoints in breast and prostate cancer cell lines and tumors, discriminated 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 and Cistrome databases. Using the toolset we identified somatic structural variants in prostate cancer, genes affected by the variants, and detected epigenomic footprints of their regulation.
We confirm and extend our previous findings about the association of the epigenome and selective structural mutability of the human genome . We validate the association between hypomethylation and genomic instability in germline at higher resolution using independent mate-pair sequencing data from the 1000 Genomes Project. We also show that only germline breakpoints show striking enrichment for regions hypomethylated in germline but that somatic breakpoints detected in breast cancer do not. The original study established association between breakpoints in human germline and the binding sites of the PRC2 polycomb complex only indirectly. We now validate this association directly by performing an extensive meta-analysis using ChIP-seq data and epigenomic marks from ENCODE, Cistrome and other projects. As anticipated by the previously reported results, the breakpoints in germline strongly associate with binding sites of Suz12, a key member of the PRC2 Polycomb complex, and with PRC2-associated histone marks H3K27me3 and H3K9me3. The breakpoints in breast cancer associate with different sets of transcription factor binding sites and epigenomic states, such as distal regulatory sites associated with active gene transcription. Finally, we identify distinct patterns of selective structural mutability in breast cancer cell lines that associate with the status of key oncogenes such as ER, PR, or HER2.
In summary, the results obtained using Breakout and related tools in the Genboree Workbench suggest that structural mutations are not randomly distributed relative to the epigenome. Cell-type specific patterns of associations between epigenomic states and structural mutations suggest that the epigenome and transcription factors play roles in determining selective structural mutability of the genome in both somatic cells and in germline.
Software availability and requirements
Breakout and the other tools presented are part of the Genboree Workbench and can be accessed at the address http://genboree.org/java-bin/workbench.jsp Supported browsers are Internet Explorer versions 8 and above, Mozilla Firefox versions 7 and above. A tutorial and sample datasets are available at http://genboree.org/breakpoints Breakout and the epigenome enrichment analysis tools can be downloaded at http://www.brl.bcm.tmc.edu/breakout/breakoutDownload.rhtml.
This work was supported by the National Institutes of Health [U01 DA025956 to AM]; Caroline Wiess Law Foundation [to SEM]; and Dan L Duncan Cancer Center [Pilot Grant to AM and SEM]. The authors acknowledge the joint participation by Diana Helis Henry Medical Research Foundation through its direct engagement in the continuous active conduct of medical research in conjunction with Baylor College of Medicine. Breast tumor tissue was provided by the National Breast Cancer Tissue Resource of NIH P50CA58183.
Funding for the publication of this article was provided by the Caroline Wiess Law Foundation [to SEM].
This article has been published as part of BMC Bioinformatics Volume 15 Supplement 7, 2014: Selected articles from the 10th Annual Biotechnology and Bioinformatics Symposium (BIOT 2013). The full contents of the supplement are available online at http://www.biomedcentral.com/bmcbioinformatics/supplements/15/S7
- Muller HJ: The remaking of chromosomes. Collecting Net. 1938, 181-195. XIIIGoogle Scholar
- Li J, Harris RA, Cheung SW, Coarfa C, Jeong M, Goodell MA, White LD, Patel A, Kang SH, Shaw C: Genomic hypomethylation in the human germline associates with selective structural mutability in the human genome. PLoS genetics. 2012, 8: e1002692-10.1371/journal.pgen.1002692.PubMed CentralView ArticlePubMedGoogle Scholar
- Siva N: 1000 Genomes project. Nat Biotechnol. 2008, 26: 256-PubMedGoogle Scholar
- Mills RE, Walter K, Stewart C, Handsaker RE, Chen K, Alkan C, Abyzov A, Yoon SC, Ye K, Cheetham RK: Mapping copy number variation by population-scale genome sequencing. Nature. 2011, 470: 59-65. 10.1038/nature09708.PubMed CentralView ArticlePubMedGoogle Scholar
- A map of human genome variation from population-scale sequencing. Nature. 2010, 467: 1061-1073. 10.1038/nature09534.Google Scholar
- Birney E, Stamatoyannopoulos JA, Dutta A, Guigo R, Gingeras TR, Margulies EH, Weng Z, Snyder M, Dermitzakis ET, Thurman RE: Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature. 2007, 447: 799-816. 10.1038/nature05874.View ArticlePubMedGoogle Scholar
- Myers RM, Stamatoyannopoulos J, Snyder M, Dunham I, Hardison RC, Bernstein BE, Gingeras TR, Kent WJ, Birney E, Wold B, Crawford GE: A user's guide to the encyclopedia of DNA elements (ENCODE). PLoS Biol. 2011, 9: e1001046-10.1371/journal.pbio.1001046.View ArticleGoogle Scholar
- Bernstein BE, Stamatoyannopoulos JA, Costello JF, Ren B, Milosavljevic A, Meissner A, Kellis M, Marra MA, Beaudet AL, Ecker JR: The NIH Roadmap Epigenomics Mapping Consortium. Nature biotechnology. 2010, 28: 1045-1048. 10.1038/nbt1010-1045.PubMed CentralView ArticlePubMedGoogle Scholar
- Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R: The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009, 25: 2078-2079. 10.1093/bioinformatics/btp352.PubMed CentralView ArticlePubMedGoogle Scholar
- Homer N, Merriman B, Nelson SF: BFAST: an alignment tool for large scale genome resequencing. PLoS One. 2009, 4: e7767-10.1371/journal.pone.0007767.PubMed CentralView ArticlePubMedGoogle Scholar
- Li H, Durbin R: Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics. 2010, 26: 589-595. 10.1093/bioinformatics/btp698.PubMed CentralView ArticlePubMedGoogle Scholar
- Li H, Durbin R: Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009, 25: 1754-1760. 10.1093/bioinformatics/btp324.PubMed CentralView ArticlePubMedGoogle Scholar
- Coarfa C, Yu F, Miller CA, Chen Z, Harris RA, Milosavljevic A: Pash 3.0: A versatile software package for read mapping and integrative analysis of genomic and epigenomic variation using massively parallel DNA sequencing. BMC Bioinformatics. 2010, 11: 572-10.1186/1471-2105-11-572.PubMed CentralView ArticlePubMedGoogle Scholar
- Lister R, Pelizzola M, Dowen RH, Hawkins RD, Hon G, Tonti-Filippini J, Nery JR, Lee L, Ye Z, Ngo QM: Human DNA methylomes at base resolution show widespread epigenomic differences. Nature. 2009, 462: 315-322. 10.1038/nature08514.PubMed CentralView ArticlePubMedGoogle Scholar
- Harris RA, Wang T, Coarfa C, Nagarajan RP, Hong C, Downey SL, Johnson BE, Fouse SD, Delaney A, Zhao Y: Comparison of sequencing-based methods to profile DNA methylation and identification of monoallelic epigenetic modifications. Nat Biotechnol. 2010, 28: 1097-1105. 10.1038/nbt.1682.PubMed CentralView ArticlePubMedGoogle Scholar
- Lister R, Pelizzola M, Kida YS, Hawkins RD, Nery JR, Hon G, Antosiewicz-Bourget J, O'Malley R, Castanon R, Klugman S: Hotspots of aberrant epigenomic reprogramming in human induced pluripotent stem cells. Nature. 2011, 471: 68-73. 10.1038/nature09798.PubMed CentralView ArticlePubMedGoogle Scholar
- Ernst J, Kheradpour P, Mikkelsen TS, Shoresh N, Ward LD, Epstein CB, Zhang X, Wang L, Issner R, Coyne M: Mapping and analysis of chromatin state dynamics in nine human cell types. Nature. 2011, 473: 43-49. 10.1038/nature09906.PubMed CentralView ArticlePubMedGoogle Scholar
- Maunakea AK, Nagarajan RP, Bilenky M, Ballinger TJ, D'Souza C, Fouse SD, Johnson BE, Hong C, Nielsen C, Zhao Y: Conserved role of intragenic DNA methylation in regulating alternative promoters. Nature. 2010, 466: 253-257. 10.1038/nature09165.PubMed CentralView ArticlePubMedGoogle Scholar
- Richardson L, Ruby S: RESTful web services. 2007, Sebastopol, Calif.: O'ReillyGoogle Scholar
- Stephens PJ, McBride DJ, Lin ML, Varela I, Pleasance ED, Simpson JT, Stebbings LA, Leroy C, Edkins S, Mudie LJ: Complex landscapes of somatic rearrangement in human breast cancer genomes. Nature. 2009, 462: 1005-1010. 10.1038/nature08645.PubMed CentralView ArticlePubMedGoogle Scholar
- Hormozdiari F, Alkan C, Eichler EE, Sahinalp SC: Combinatorial algorithms for structural variation detection in high-throughput sequenced genomes. Genome Res. 2009, 19: 1270-1278. 10.1101/gr.088633.108.PubMed CentralView ArticlePubMedGoogle Scholar
- Sindi S, Helman E, Bashir A, Raphael BJ: A geometric approach for classification and comparison of structural variants. Bioinformatics. 2009, 25: i222-230. 10.1093/bioinformatics/btp208.PubMed CentralView ArticlePubMedGoogle Scholar
- Hormozdiari F, Hajirasouliha I, Dao P, Hach F, Yorukoglu D, Alkan C, Eichler EE, Sahinalp SC: Next-generation VariationHunter: combinatorial algorithms for transposon insertion discovery. Bioinformatics. 2010, 26: i350-357. 10.1093/bioinformatics/btq216.PubMed CentralView ArticlePubMedGoogle Scholar
- Hampton OA, Den Hollander P, Miller CA, Delgado DA, Li J, Coarfa C, Harris RA, Richards S, Scherer SE, Muzny DM: A sequence-level map of chromosomal breakpoints in the MCF7 breast cancer cell line yields insights into the evolution of a cancer genome. Genome Res. 2009, 19: 167-177.PubMed CentralView ArticlePubMedGoogle Scholar
- Futreal PA, Coin L, Marshall M, Down T, Hubbard T, Wooster R, Rahman N, Stratton MR: A census of human cancer genes. Nature reviews Cancer. 2004, 4: 177-183. 10.1038/nrc1299.PubMed CentralView ArticlePubMedGoogle Scholar
- Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP: Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 2005, 102: 15545-15550. 10.1073/pnas.0506580102.PubMed CentralView ArticlePubMedGoogle Scholar
- Mootha VK, Lindgren CM, Eriksson KF, Subramanian A, Sihag S, Lehar J, Puigserver P, Carlsson E, Ridderstrale M, Laurila E: PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet. 2003, 34: 267-273. 10.1038/ng1180.View ArticlePubMedGoogle Scholar
- Kastner P, Krust A, Turcotte B, Stropp U, Tora L, Gronemeyer H, Chambon P: Two distinct estrogen-regulated promoters generate transcripts encoding the two functionally different human progesterone receptor forms A and B. EMBO J. 1990, 9: 1603-1614.PubMed CentralPubMedGoogle Scholar
- Yu J, Mani RS, Cao Q, Brenner CJ, Cao X, Wang X, Wu L, Li J, Hu M, Gong Y: An integrated network of androgen receptor, polycomb, and TMPRSS2-ERG gene fusions in prostate cancer progression. Cancer Cell. 2010, 17: 443-454. 10.1016/j.ccr.2010.03.018.PubMed CentralView ArticlePubMedGoogle Scholar
- Radpour R, Kohler C, Haghighi MM, Fan AX, Holzgreve W, Zhong XY: Methylation profiles of 22 candidate genes in breast cancer using high-throughput MALDI-TOF mass array. Oncogene. 2009, 28: 2969-2978. 10.1038/onc.2009.149.View ArticlePubMedGoogle Scholar
- Dedeurwaerder S, Defrance M, Calonne E, Denis H, Sotiriou C, Fuks F: Evaluation of the Infinium Methylation 450K technology. Epigenomics. 2011, 3: 771-784. 10.2217/epi.11.105.View ArticlePubMedGoogle Scholar
- Banerji S, Cibulskis K, Rangel-Escareno C, Brown KK, Carter SL, Frederick AM, Lawrence MS, Sivachenko AY, Sougnez C, Zou L: Sequence analysis of mutations and translocations across breast cancer subtypes. Nature. 2012, 486: 405-409. 10.1038/nature11154.PubMed CentralView ArticlePubMedGoogle Scholar
- Liu T, Ortiz JA, Taing L, Meyer CA, Lee B, Zhang Y, Shin H, Wong SS, Ma J, Lei Y: Cistrome: an integrative platform for transcriptional regulation studies. Genome biology. 2011, 12: R83-10.1186/gb-2011-12-8-r83.PubMed CentralView ArticlePubMedGoogle Scholar
- Qin B, Zhou M, Ge Y, Taing L, Liu T, Wang Q, Wang S, Chen J, Shen L, Duan X: CistromeMap: A knowledgebase and web server for ChIP-Seq and DNase-Seq studies in mouse and human. Bioinformatics. 2012Google Scholar
- Fang M, Toher J, Morgan M, Davison J, Tannenbaum S, Claffey K: Genomic differences between estrogen receptor (ER)-positive and ER-negative human breast carcinoma identified by single nucleotide polymorphism array comparative genome hybridization analysis. Cancer. 2011, 117: 2024-2034. 10.1002/cncr.25770.PubMed CentralView ArticlePubMedGoogle Scholar
- Kabil A, Silva E, Kortenkamp A: Estrogens and genomic instability in human breast cancer cells--involvement of Src/Raf/Erk signaling in micronucleus formation by estrogenic chemicals. Carcinogenesis. 2008, 29: 1862-1868. 10.1093/carcin/bgn138.View ArticlePubMedGoogle Scholar
- Melchor L, Honrado E, Huang J, Alvarez S, Naylor TL, Garcia MJ, Osorio A, Blesa D, Stratton MR, Weber BL: Estrogen receptor status could modulate the genomic pattern in familial and sporadic breast cancer. Clinical cancer research : an official journal of the American Association for Cancer Research. 2007, 13: 7305-7313. 10.1158/1078-0432.CCR-07-0711.View ArticleGoogle Scholar
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