Comprehensive discovery of structural variation (SV) from whole genome sequencing data requires multiple detection signals including read-pair, split-read, read-depth and prior knowledge. Owing to technical challenges, extant SV discovery algorithms either use one signal in isolation, or at best use two sequentially. We present LUMPY, a novel SV discovery framework that naturally integrates multiple SV signals jointly across multiple samples. We show that LUMPY yields improved sensitivity, especially when SV signal is reduced owing to either low coverage data or low intra-sample variant allele frequency. We also report a set of 4,564 validated breakpoints from the NA12878 human genome. https://github.com/arq5x/lumpy-sv.

We have identified tens of thousands of short extrachromosomal circular DNAs (microDNA) in mouse tissues as well as mouse and human cell lines. These microDNAs are 200–400 bp long, derived from unique non-repetitive sequence and are enriched in the 5' untranslated regions of genes, exons and CpG islands. Chromosomal loci that are enriched sources of microDNA in adult brain are somatically mosaic for micro-deletions that appear to arise from the excision of microDNAs. Germline microdeletions identified by the "Thousand Genomes" project may also arise from the excision of microDNAs in the germline lineage. We have thus identified a new DNA entity in mammalian cells and provide evidence that their generation leaves behind deletions in different genomic loci. Single nucleotide polymorphisms and copy number variations are known sources of genetic variation between individuals (1–5), but there is also great interest in variations that arise during generation of somatic tissues like the mammalian brain, leading to genetic mosaicism between somatic cells. To identify sites of intramolecular homologous recombination during brain development, we searched for extrachromosomal circular DNA (eccDNA) derived from excised chromosomal regions in normal mouse embryonic brains.

Genomic association studies of common or rare protein-coding variation have established robust statistical approaches to account for multiple testing. Here, we present a comparable framework to evaluate rare and de novo noncoding single nucleotide variants, insertion/deletions, and all classes of structural variation from whole-genome sequencing (WGS). Integrating genomic annotations at the level of nucleotides, genes, and regulatory regions, we define 51,801 annotation categories. Analyses of 519 autism spectrum disorder families did not identify association with any categories after correction for 4,123 effective tests. Without appropriate correction, biologically plausible associations are observed in both cases and controls. Despite excluding previously identified gene-disrupting mutations, coding regions still exhibited the strongest associations. Thus, in autism the contribution of de novo noncoding variation is probably modest compared to de novo coding variants. Robust results from future WGS studies will require large cohorts and comprehensive analytical strategies that consider the substantial multiple testing burden.

SpeedSeq is an open-source genome analysis platform that accomplishes alignment, variant detection and functional annotation of a 50× human genome in 13 hours on a low-cost server, alleviating a bioinformatics bottleneck that typically demands weeks of computation with extensive hands-on expert involvement. SpeedSeq offers competitive or superior performance to current methods for detecting germline and somatic single nucleotide variants, indels, and structural variants, and includes novel functionality for streamlined interpretation.

SpeedSeq is an open-source genome analysis platform that accomplishes alignment, variant detection and functional annotation of a 50× human genome in 13 hours on a low-cost server, alleviating a bioinformatics bottleneck that typically demands weeks of computation with extensive hands-on expert involvement. SpeedSeq offers competitive or superior performance to current methods for detecting germline and somatic single nucleotide variants, indels, and structural variants, and includes novel functionality for streamlined interpretation.Technical advances in second-generation DNA sequencing technologies have reduced both the cost and time required to generate whole-genome sequencing (WGS) data, creating opportunities in healthcare and academic research to survey the human genome with unprecedented depth and scope. However, bottlenecks in computational processing and variant interpretation have hindered adoption of these technologies for time-sensitive and large-scale projects. Using a standard pipeline based on BWA 1 , GATK 2 , SAMtools 3 , and Picard, processing a 50× human genome from raw sequence data to variant calls on a 32-thread server requires 60-70 hours (Supplementary Note 1). Furthermore, distinguishing Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use

A key goal of whole genome sequencing (WGS) for human genetics studies is to interrogate all forms of variation, including single nucleotide variants (SNV), small insertion/deletion (indel) variants and structural variants (SV). However, tools and resources for the study of SV have lagged behind those for smaller variants. Here, we used a scalable pipeline 22 to map and characterize SV in 17,795 deeply sequenced human genomes. We publicly release site-frequency data to create the largest WGS-based SV resource to date. On average, individuals carry 2.9 rare SVs that alter coding regions, affecting the dosage or structure of 4.2 genes and accounting for 4.0-11.2% of rare high-impact coding alleles. Based on a computational model, we estimate that SVs account for 17.2% of rare alleles genome-wide with predicted deleterious effects equivalent to loss-of-function coding alleles; ~90% of such SVs are non-coding deletions (mean 19.1 per genome). We report 158,991 ultra-rare SVs and show that ~2% of individuals carry ultra-rare megabase-scale SVs, nearly half of which are balanced or complex rearrangements. Finally, we infer the dosage sensitivity of genes and non-coding elements, revealing trends related to element class and conservation. This work will help guide SV analysis and interpretation in the era of WGS.

Tumor genomes are generally thought to evolve through a gradual accumulation of mutations, but the observation that extraordinarily complex rearrangements can arise through single mutational events suggests that evolution may be accelerated by punctuated changes in genome architecture. To assess the prevalence and origins of complex genomic rearrangements (CGRs), we mapped 6179 somatic structural variation breakpoints in 64 cancer genomes from seven tumor types and screened for clusters of three or more interconnected breakpoints. We find that complex breakpoint clusters are extremely common: 154 clusters comprise 25% of all somatic breakpoints, and 75% of tumors exhibit at least one complex cluster. Based on copy number state profiling, 63% of breakpoint clusters are consistent with being CGRs that arose through a single mutational event. CGRs have diverse architectures including focal breakpoint clusters, largescale rearrangements joining clusters from one or more chromosomes, and staggeringly complex chromothripsis events. Notably, chromothripsis has a significantly higher incidence in glioblastoma samples (39%) relative to other tumor types (9%). Chromothripsis breakpoints also show significantly elevated intra-tumor allele frequencies relative to simple SVs, which indicates that they arise early during tumorigenesis or confer selective advantage. Finally, assembly and analysis of 4002 somatic and 6982 germline breakpoint sequences reveal that somatic breakpoints show significantly less microhomology and fewer templated insertions than germline breakpoints, and this effect is stronger at CGRs than at simple variants. These results are inconsistent with replication-based models of CGR genesis and strongly argue that nonhomologous repair of concurrently arising DNA double-strand breaks is the predominant mechanism underlying complex cancer genome rearrangements.

Deep catalogs of genetic variation from thousands of humans enable the detection of intraspecies constraint by identifying coding regions with a scarcity of variation. While existing techniques summarize constraint for entire genes, single gene-wide metrics conceal regional constraint variability within each gene. Therefore, we have created a detailed map of constrained coding regions (CCRs) by leveraging variation observed among 123,136 humans from the Genome Aggregation Database. The most constrained CCRs are enriched for pathogenic variants in ClinVar and mutations underlying developmental disorders. CCRs highlight protein domain families under high constraint and suggest unannotated or incomplete protein domains. The highest-percentile CCRs complement existing variant prioritization methods when evaluating de novo mutations in studies of autosomal dominant disease. Finally, we identify highly constrained CCRs within genes lacking known disease associations. This observation suggests that CCRs may identify regions under strong purifying selection that, when mutated, cause severe developmental phenotypes or embryonic lethality.