Explore the vast range of titles available.

Structural variants (SVs) remain challenging to represent and study relative to point mutations despite their demonstrated importance. We show that variation graphs, as implemented in the vg toolkit, provide an effective means for leveraging SV catalogs for short-read SV genotyping experiments. We benchmark vg against state-of-the-art SV genotypers using three sequence-resolved SV catalogs generated by recent long-read sequencing studies. In addition, we use assemblies from 12 yeast strains to show that graphs constructed directly from aligned de novo assemblies improve genotyping compared to graphs built from intermediate SV catalogs in the VCF format.

Medulloblastoma, the most common malignant paediatric brain tumour, is currently treated with nonspecific cytotoxic therapies including surgery, whole-brain radiation, and aggressive chemotherapy. As medulloblastoma exhibits marked intertumoural heterogeneity, with at least four distinct molecular variants, previous attempts to identify targets for therapy have been underpowered because of small samples sizes. Here we report somatic copy number aberrations (SCNAs) in 1,087 unique medulloblastomas. SCNAs are common in medulloblastoma, and are predominantly subgroup-enriched. The most common region of focal copy number gain is a tandem duplication of SNCAIP , a gene associated with Parkinson’s disease, which is exquisitely restricted to Group 4α. Recurrent translocations of PVT1 , including PVT1-MYC and PVT1-NDRG1 , that arise through chromothripsis are restricted to Group 3. Numerous targetable SCNAs, including recurrent events targeting TGF-β signalling in Group 3, and NF-κB signalling in Group 4, suggest future avenues for rational, targeted therapy. Medulloblastoma is the most common malignant brain tumour in children; having assembled over 1,000 samples the authors report that somatic copy number aberrations are common in medulloblastoma, in particular a tandem duplication of SNCAIP , a gene associated with Parkinson’s disease, which is restricted to subgroup 4α, and translocations of PVT1 , which are restricted to Group 3. The medulloblastoma genome dissected Medulloblastoma is the most common malignant brain tumour in children. Four papers published in the 2 August 2012 issue of Nature use whole-genome and other sequencing techniques to produce a detailed picture of the genetics and genomics of this condition. Notable findings include the identification of recurrent mutations in genes not previously implicated in medulloblastoma, with significant genetic differences associated with the four biologically distinct subgroups and clinical outcomes in each. Potential avenues for therapy are suggested by the identification of targetable somatic copy-number alterations, including recurrent events targeting TGFβ signalling in Group 3, and NF-κB signalling in Group 4 medulloblastomas.

Structural variants (SVs) can contribute to oncogenesis through a variety of mechanisms. Despite their importance, the identification of SVs in cancer genomes remains challenging. Here, we present a framework that integrates optical mapping, high-throughput chromosome conformation capture (Hi-C), and whole-genome sequencing to systematically detect SVs in a variety of normal or cancer samples and cell lines. We identify the unique strengths of each method and demonstrate that only integrative approaches can comprehensively identify SVs in the genome. By combining Hi-C and optical mapping, we resolve complex SVs and phase multiple SV events to a single haplotype. Furthermore, we observe widespread structural variation events affecting the functions of noncoding sequences, including the deletion of distal regulatory sequences, alteration of DNA replication timing, and the creation of novel three-dimensional chromatin structural domains. Our results indicate that noncoding SVs may be underappreciated mutational drivers in cancer genomes. The authors present an integrative framework for identifying structural variants (SVs) in cancer that applies optical mapping, Hi-C, and whole-genome sequencing. They find SVs affecting distal regulatory sequences, DNA replication, and three-dimensional chromatin structure.

Background Plant pathogens secrete effector proteins into their hosts to promote colonisation. Among these are avirulence (Avr) effectors, which can be recognised by specific host immune receptors, triggering an immune response that prevents pathogen progression. This recognition exerts strong evolutionary pressure on pathogens to alter and/or eliminate Avr genes to escape recognition. Consequently, understanding Avr gene evolution is critical for developing effective resistance deployment strategies. However, identifying and validating Avr effectors remains a significant challenge, especially for fungal plant pathogens, leading to a limited catalogue of Avr genes. This challenge is particularly pronounced for obligate biotrophic pathogens such as the wheat leaf (brown) rust fungus Puccinia triticina ( Pt ), where only two Avr genes have been confirmed to date. Results In this study, we conducted a k -mer-based genome-wide association study (GWAS) to detect a broad spectrum of structural genetic variations — including single nucleotide polymorphisms (SNPs), insertions and deletions (indels) and copy number variations (CNVs) — that may contribute to the gain of virulence in Pt . Analysis of k -mers linked to avirulence phenotypes of Pt isolates across eleven leaf rust resistance ( Lr ) loci, revealed a distinct association peak on chromosome 10B corresponding to avirulence against Lr20 . Assembly of the associated k -mers produced a 50 bp sequence that was located near two candidate effector genes, one of which — termed Pt76_024702 — also displayed high levels of expression during both early and later stages of infection. Furthermore, the genomic region harbouring Pt76_024702 exhibited large-scale deletions in certain Pt lineages virulent to Lr20 , particularly those infecting durum wheat ( Triticum turgidum ssp. durum ). Conclusion These findings highlight Pt76_024702 as a compelling candidate for AvrLr20 and demonstrate the significant potential of the presented k- mer-based GWAS approach to enhance the Avr gene catalogue. This strategy is particularly promising for complex fungal pathogens such as the notorious wheat rust pathogens where conventional approaches have previously proved challenging.

Background Genomic structural variations (SV) are important determinants of genotypic and phenotypic changes in many organisms. However, the detection of SV from next-generation sequencing data remains challenging. Results In this study, DNA from a Chinese family quartet is sequenced at three different sequencing centers in triplicate. A total of 288 derivative data sets are generated utilizing different analysis pipelines and compared to identify sources of analytical variability. Mapping methods provide the major contribution to variability, followed by sequencing centers and replicates. Interestingly, SV supported by only one center or replicate often represent true positives with 47.02% and 45.44% overlapping the long-read SV call set, respectively. This is consistent with an overall higher false negative rate for SV calling in centers and replicates compared to mappers (15.72%). Finally, we observe that the SV calling variability also persists in a genotyping approach, indicating the impact of the underlying sequencing and preparation approaches. Conclusions This study provides the first detailed insights into the sources of variability in SV identification from next-generation sequencing and highlights remaining challenges in SV calling for large cohorts. We further give recommendations on how to reduce SV calling variability and the choice of alignment methodology.

Background Structural variants are genomic variants that impact at least 50 nucleotides. Structural variants can play major roles in diversity and human health. Many structural variants are difficult to interpret and understand with existing visualization tools, especially when comprised of inverted sequences or multiple breakend pairs. Results We present SVTopo, a tool to visualize germline structural variants with supporting evidence from high-accuracy long reads in easily understood figures. We include examples of 101 visually complex structural variants from seven unrelated human genomes, manually assigned to ten categories. These demonstrate a broad spectrum of rearrangement and showcase the frequency of complex structural variants in human genomes. Conclusions SVTopo shows breakpoint evidence in ways that aid reasoning about the impact of multi-breakpoint rearrangements. The images created aid human reasoning about the result of structural variation on gene and regulatory regions.

Background DNBSEQ platforms have been widely used for variation detection, including single-nucleotide variants (SNVs) and short insertions and deletions (INDELs), which is comparable to Illumina. However, the performance and even characteristics of structural variations (SVs) detection using DNBSEQ platforms are still unclear. Results In this study, we assessed the detection of SVs using 40 tools on eight DNBSEQ whole-genome sequencing (WGS) datasets and two Illumina WGS datasets of NA12878. Our findings confirmed that the performance of SVs detection using the same tool on DNBSEQ and Illumina datasets was highly consistent, with correlations greater than 0.80 on metrics of number, size, precision and sensitivity, respectively. Furthermore, we constructed a “DNBSEQ” SV set (4,785 SVs) from the DNBSEQ datasets and an “Illumina” SV set (6,797 SVs) from the Illumina datasets. We found that these two SV sets were highly consistent of SV sites and genomic characteristics, including repetitive regions, GC distribution, difficult-to-sequence regions, and gene features, indicating the robustness of our comparative analysis and highlights the value of both platforms in understanding the genomic context of SVs. Conclusions Our study systematically analyzed and characterized germline SVs detected on WGS datasets sequenced from DNBSEQ platforms, providing a benchmark resource for further studies of SVs using DNBSEQ platforms.

Background Pigs are not only a key source of animal protein worldwide, but also serve as important models in biological research. With the rapid development of short- and long-read sequencing technologies, genetic studies in pigs have advanced considerably. Although extensive research has been conducted on single-nucleotide polymorphisms (SNPs) and small insertions/deletions (indels), which has provided important insights into pig domestication, evolution, and trait formation, structural variants (SVs) remain underexplored due to technical limitations in sequencing resolution, challenges in variant detection, and insufficient population-scale sampling. Results In this study, we constructed the Pig Structural Variant Reference Panel (PSVRP) by integrating 21 long-read and 1,193 short-read whole-genome resequencing datasets from globally diverse pig populations. A total of 319,058 high-confidence SVs were identified, comprising 196,620 insertions and 122,438 deletions. Phylogenetic and ADMIXTURE analyses revealed clear divergence between Asian and European pigs, consistent with results derived from SNPs and indels data. Selection scans highlighted candidate genes associated with key traits, such as EPAS1 and NOVA1 for high-altitude adaptation, and PLAG1 and MIB1 for body size regulation. Conclusions The PSVRP provides a high-resolution, population-scale pig SVs genotyping resource. This comprehensive panel deepens our understanding of genetic variation, facilitates the discovery of functional variants underlying adaptive and economic traits, and offers new insights for precision pig breeding.

Structural variations (SVs) are a key type of cancer genomic alterations, contributing to oncogenesis and progression of many cancers, including colorectal cancer (CRC). However, SVs in CRC remain difficult to be reliably detected due to limited SV-detection capacity of the commonly used short-read sequencing. This study investigated the somatic SVs in 21 pairs of CRC samples by Nanopore whole-genome long-read sequencing. 5200 novel somatic SVs from 21 CRC patients (494 SVs / patient) were identified. A 4.9-Mbp long inversion that silences APC expression (confirmed by RNA-seq) and an 11.2-kbp inversion that structurally alters CFTR were identified. Two novel gene fusions that might functionally impact the oncogene RNF38 and the tumor-suppressor SMAD3 were detected. RNF38 fusion possesses metastasis-promoting ability confirmed by in vitro migration and invasion assay, and in vivo metastasis experiments. This work highlighted the various applications of long-read sequencing in cancer genome analysis, and shed new light on how somatic SVs structurally alter critical genes in CRC. The investigation on somatic SVs via nanopore sequencing revealed the potential of this genomic approach in facilitating precise diagnosis and personalized treatment of CRC.

Existing methods for identifying structural variants (SVs) from short read datasets are inaccurate. This complicates disease-gene identification and efforts to understand the consequences of genetic variation. In response, we have created Wham (Whole-genome Alignment Metrics) to provide a single, integrated framework for both structural variant calling and association testing, thereby bypassing many of the difficulties that currently frustrate attempts to employ SVs in association testing. Here we describe Wham, benchmark it against three other widely used SV identification tools-Lumpy, Delly and SoftSearch-and demonstrate Wham's ability to identify and associate SVs with phenotypes using data from humans, domestic pigeons, and vaccinia virus. Wham and all associated software are covered under the MIT License and can be freely downloaded from github (https://github.com/zeeev/wham), with documentation on a wiki (http://zeeev.github.io/wham/). For community support please post questions to https://www.biostars.org/.