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.

BACKGROUND: Whole exome capture sequencing allows researchers to cost-effectively sequence the coding regions of the genome. Although the exome capture sequencing methods have become routine and well established, there is currently a lack of tools specialized for variant calling in this type of data. RESULTS: Using statistical models trained on validated whole-exome capture sequencing data, the Atlas2 Suite is an integrative variant analysis pipeline optimized for variant discovery on all three of the widely used next generation sequencing platforms (SOLiD, Illumina, and Roche 454). The suite employs logistic regression models in conjunction with user-adjustable cutoffs to accurately separate true SNPs and INDELs from sequencing and mapping errors with high sensitivity (96.7%). CONCLUSION: We have implemented the Atlas2 Suite and applied it to 92 whole exome samples from the 1000 Genomes Project. The Atlas2 Suite is available for download at http://sourceforge.net/projects/atlas2/. In addition to a command line version, the suite has been integrated into the Genboree Workbench, allowing biomedical scientists with minimal informatics expertise to remotely call, view, and further analyze variants through a simple web interface. The existing genomic databases displayed via the Genboree browser also streamline the process from variant discovery to functional genomics analysis, resulting in an off-the-shelf toolkit for the broader community.

BACKGROUND: Microbial metagenomic analyses rely on an increasing number of publicly available tools. Installation, integration, and maintenance of the tools poses significant burden on many researchers and creates a barrier to adoption of microbiome analysis, particularly in translational settings. METHODS: To address this need we have integrated a rich collection of microbiome analysis tools into the Genboree Microbiome Toolset and exposed them to the scientific community using the Software-as-a-Service model via the Genboree Workbench. The Genboree Microbiome Toolset provides an interactive environment for users at all bioinformatic experience levels in which to conduct microbiome analysis. The Toolset drives hypothesis generation by providing a wide range of analyses including alpha diversity and beta diversity, phylogenetic profiling, supervised machine learning, and feature selection. RESULTS: We validate the Toolset in two studies of the gut microbiota, one involving obese and lean twins, and the other involving children suffering from the irritable bowel syndrome. CONCLUSIONS: By lowering the barrier to performing a comprehensive set of microbiome analyses, the Toolset empowers investigators to translate high-volume sequencing data into valuable biomedical discoveries.

BACKGROUND: Massively parallel sequencing readouts of epigenomic assays are enabling integrative genome-wide analyses of genomic and epigenomic variation. Pash 3.0 performs sequence comparison and read mapping and can be employed as a module within diverse configurable analysis pipelines, including ChIP-Seq and methylome mapping by whole-genome bisulfite sequencing. RESULTS: Pash 3.0 generally matches the accuracy and speed of niche programs for fast mapping of short reads, and exceeds their performance on longer reads generated by a new generation of massively parallel sequencing technologies. By exploiting longer read lengths, Pash 3.0 maps reads onto the large fraction of genomic DNA that contains repetitive elements and polymorphic sites, including indel polymorphisms. CONCLUSIONS: We demonstrate the versatility of Pash 3.0 by analyzing the interaction between CpG methylation, CpG SNPs, and imprinting based on publicly available whole-genome shotgun bisulfite sequencing data. Pash 3.0 makes use of gapped k-mer alignment, a non-seed based comparison method, which is implemented using multi-positional hash tables. This allows Pash 3.0 to run on diverse hardware platforms, including individual computers with standard RAM capacity, multi-core hardware architectures and large clusters.

We present a new technique for identifying scalability bottlenecks in executions of single-program, multiple-data (SPMD) parallel programs, quantifying their impact on performance, and associating this information with the program source code. Our performance analysis strategy involves three steps. First, we collect call path profiles for two or more executions on different numbers of processors. Second, we use our expectations about how the performance of executions should differ, e.g., linear speedup for strong scaling or constant execution time for weak scaling, to automatically compute the scalability of costs incurred at each point in a program's execution. Third, with the aid of an interactive browser, an application developer can explore a program's performance in a top-down fashion, see the contexts in which poor scaling behavior arises, and understand exactly how much each scalability bottleneck dilates execution time. Our analysis technique is independent of the parallel programming model. We describe our experiences applying our technique to analyze parallel programs written in Co-array Fortran and Unified Parallel C, as well as message-passing programs based on MPI.

TLS is the protocol of choice for securing today's e-commerce and online transactions but adding TLS to a Web server imposes a significant overhead relative to an insecure Web server on the same platform. We perform a comprehensive study of the performance costs of TLS. Our methodology is to profile TLS Web servers with trace-driven workloads, replace individual components inside TLS with no-ops, and measure the observed increase in server throughput. We estimate the relative costs of each TLS processing stage, identifying the areas for which future optimizations would be worthwhile. Our results show that while the RSA operations represent the largest performance cost in TLS Web servers, they do not solely account for TLS overhead. RSA accelerators are effective for e-commerce site workloads since they experience low TLS session reuse. Accelerators appear to be less effective for sites where all the requests are handled by a TLS server because they have a higher session reuse rate. In this case, investing in a faster CPU might provide a greater boost in performance. Our experiments show that having a second CPU is at least as useful as an RSA accelerator. Our results seem to suggest that, as CPUs become faster, the cryptographic costs of TLS will become dwarfed by the CPU costs of the nonsecurity aspects of a Web server. Optimizations aimed at general purpose Web servers should continue to be a focus of research and would benefit secure Web servers as well.

Co-array Fortran (CAF) and Unified Parallel C (UPC) are two emerging languages for single-program, multiple-data global address space programming. These languages boost programmer productivity by providing shared variables for inter-process communication instead of message passing. However, the performance of these emerging languages still has room for improvement. In this paper, we study the performance of variants of the NAS MG, CG, SP, and BT benchmarks on several modern architectures to identify challenges that must be met to deliver top performance. We compare CAF and UPC variants of these programs with the original Fortran+MPI code. Today, CAF and UPC programs deliver scalable performance on clusters only when written to use bulk communication. However, our experiments uncovered some significant performance bottlenecks of UPC codes on all platforms. We account for the root causes limiting UPC performance such as the synchronization model, the communication efficiency of strided data, and source-to-source translation issues. We show that they can be remedied with language extensions, new synchronization constructs, and, finally, adequate optimizations by the back-end C compilers.

No abstract available.