MOTIVATION: Integration of viruses into infected host cell DNA can cause DNA damage and disrupt genes. Recent cost reductions and growth of whole genome sequencing has produced a wealth of data in which viral presence and integration detection is possible. While key research and clinically relevant insights can be uncovered, existing software has not achieved widespread adoption, limited in part due to high computational costs, the inability to detect a wide range of viruses, as well as precision and sensitivity. RESULTS: Here, we describe VIRUSBreakend, a high-speed tool that identifies viral DNA presence and genomic integration. It utilizes single breakends, breakpoints in which only one side can be unambiguously placed, in a novel virus-centric variant calling and assembly approach to identify viral integrations with high sensitivity and a near-zero false discovery rate. VIRUSBreakend detects viral integrations anywhere in the host genome including regions such as centromeres and telomeres unable to be called by existing tools. Applying VIRUSBreakend to a large metastatic cancer cohort, we demonstrate that it can reliably detect clinically relevant viral presence and integration including HPV, HBV, MCPyV, EBV and HHV-8. AVAILABILITY AND IMPLEMENTATION: VIRUSBreakend is part of the Genomic Rearrangement IDentification Software Suite (GRIDSS). It is available under a GPLv3 license from https://github.com/PapenfussLab/VIRUSBreakend. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

UNLABELLED: Sambamba is a high-performance robust tool and library for working with SAM, BAM and CRAM sequence alignment files; the most common file formats for aligned next generation sequencing data. Sambamba is a faster alternative to samtools that exploits multi-core processing and dramatically reduces processing time. Sambamba is being adopted at sequencing centers, not only because of its speed, but also because of additional functionality, including coverage analysis and powerful filtering capability. AVAILABILITY AND IMPLEMENTATION: Sambamba is free and open source software, available under a GPLv2 license. Sambamba can be downloaded and installed from http://www.open-bio.org/wiki/Sambamba.Sambamba v0.5.0 was released with doi:10.5281/zenodo.13200.

MOTIVATION: ChIP-chip and ChIP-seq technologies provide genome-wide measurements of various types of chromatin marks at an unprecedented resolution. With ChIP samples collected from different tissue types and/or individuals, we can now begin to characterize stochastic or systematic changes in epigenetic patterns during development (intra-individual) or at the population level (inter-individual). This requires statistical methods that permit a simultaneous comparison of multiple ChIP samples on a global as well as locus-specific scale. Current analytical approaches are mainly geared toward single sample investigations, and therefore have limited applicability in this comparative setting. This shortcoming presents a bottleneck in biological interpretations of multiple sample data. RESULTS: To address this limitation, we introduce a parametric classification approach for the simultaneous analysis of two (or more) ChIP samples. We consider several competing models that reflect alternative biological assumptions about the global distribution of the data. Inferences about locus-specific and genome-wide chromatin differences are reached through the estimation of multivariate mixtures. Parameter estimates are obtained using an incremental version of the Expectation-Maximization algorithm (IEM). We demonstrate efficient scalability and application to three very diverse ChIP-chip and ChIP-seq experiments. The proposed approach is evaluated against several published ChIP-chip and ChIP-seq software packages. We recommend its use as a first-pass algorithm to identify candidate regions in the epigenome, possibly followed by some type of second-pass algorithm to fine-tune detected peaks in accordance with biological or technological criteria. AVAILABILITY: R source code is available at http://gbic.biol.rug.nl/supplementary/2009/ChromatinProfiles/. Access to Chip-seq data: GEO repository GSE17937.

UNLABELLED: GENOTRACE identifies the genomic organization for a cDNA using raw data from genome sequencing projects in progress (trace archives). Local genomic contigs are generated, allowing for example the design of PCR primers in intronic sequences to amplify coding regions of a gene, needed for example for mutation or SNP detection. AVAILABILITY: The package and examples of output files can be downloaded from http://rat.niob.knaw.nl/GENOTRACE