Jiandong Shi

Structural variants (SVs) are major contributors to genome diversity and disease susceptibility, particularly in cancer. Although third-generation sequencing technologies have substantially improved SV detection sensitivity, accurate detection of complex SVs remains challenging due to fragmented and heterogeneous alignment signals, as well as the dependence of many existing methods on predefined variant models. In this paper, we propose gSV, a general SV detector that integrates alignment-based and assembly-based approaches with the maximum exact match strategy, with particular emphasis on resolving SVs with complex or atypical alignment signatures. Without predefined assumptions about SV types, gSV captures diverse variant signals, enabling the detection of SVs that are usually missed by conventional tools. Benchmarking using both simulated datasets and real long-read sequencing data demonstrates that gSV achieves improved sensitivity and overall detection performance compared with current state-of-the-art SV callers, particularly for simple and complex SV events with complex alignment patterns. Unique SV discoveries in four breast cancer cell lines, particularly in cancer-associated genes, demonstrate the potential biological relevance of gSV-enabled discoveries. Furthermore, analysis of a breast cancer cohort from the Chinese population highlights the utility of gSV for population-scale genomic studies. Collectively, gSV provides a unified framework for comprehensive SV discovery in both research and clinical genomics settings.

Recent advances in long‐read sequencing technologies have empowered the detection of structural variations (SVs) associated with genetic diseases. Despite the availability of numerous SV callers and efforts to merge SVs from multiple tools, there remains limited research on quantifying the confidence levels for the reported results. In this work, we propose a Bayesian integration model that combines SV calls from different tools. Notably, we introduce an approach for false discovery rate (FDR) control and provide a quantitative measure for the merged SVs. Our model can handle cases where certain tools lack quality scores, showcasing its flexibility in incorporating additional tools. Through extensive simulation studies, we evaluate the performance of our method under various conditions, demonstrating the FDR estimation accuracy and improved F1 score. Furthermore, we validate our model using simulated human genome sequencing data and the HG002 dataset.

Structural variants (SVs) are of increasing significance with the advancement of the third-generation sequencing technologies, but detecting complex SVs is still challenging for existing SV detection tools. In this paper, we propose gSV, a general SV detector that integrates alignment-based and assembly-based approaches with the maximum exact match (MEM) strategy. Without predefined assumptions about SV types, gSV captures all potential variant signals, enabling the detection of SVs with complex alignment patterns that are usually missed by other tools. Evaluations using both simulated and real datasets demonstrate that gSV outperforms state-of-the-art tools in detecting both simple and complex SVs. Unique SV discoveries in four breast cancer cell lines, particularly in cancer-associated genes, validate the clinical utility of gSV. The application in a breast cancer cohort from the Chinese population further illustrates the usefulness of our new tool in genomic studies.

Background: Genomic variations, including single-nucleotide polymorphisms, small insertions and deletions, and structural variations, are crucial for understanding evolution and disease. However, comprehensive simulation tools for benchmarking genomic analysis methods are lacking. Existing simulators do not accurately represent the nonuniform distribution and length patterns of structural variations in human genomes, and simulating complex structural variations remains challenging. Results: We present BVSim, a flexible tool that provides probabilistic simulations of genomic variations, primarily focusing on human patterns while accommodating diverse species. BVSim effectively simulates both simple and complex structural variations and small variants by mimicking real-life variation distributions, which often exhibit higher frequencies near telomeres and within tandem repeat regions. Notably, BVSim allows users to input single or multiple benchmark samples from any reference genome, enabling the tool to summarize and represent the unique distribution patterns of structural variation positions and lengths specific to those species. Its compatibility with standard file formats facilitates seamless integration into various genomic research workflows, making it a very useful resource for benchmarking downstream tools such as variant callers. With numerical experiments, we show that BVSim generated more realistic sequences significantly different from other simulators’ outputs. Conclusions: BVSim is written in Python and freely available to noncommercial users under the GPL3 license. Source code, application guide, and toy examples are provided on the GitHub page at https://github.com/YongyiLuo98/BVSim. The tool is registered in SciCrunch (RRID:SCR_026926), bio.tools (biotools:BVSim), and WorkflowHub (doi:10.48546/WORKFLOWHUB.WORKFLOW.1361.1).

Motivation: Haplotypes are crucial for various genetic analyses, but reconstructing haplotypes from sequencing data remains a significant challenge. Current methods for haplotype reconstruction typically rely on a procedure of two separated stages, variant calling and phasing, but phasing overlooks the errors in variant calling. Additionally, the complexity of haplotype reconstruction increases with the number of homologous chromosomes in the sample, a common scenario in polyploid species or cell mixture sequencing. Results: To address the challenges above, we propose a unified probabilistic framework that directly utilizes sequencing reads to estimate haplotypes and sequencing error profiles. Rather than focusing solely on variant loci used by traditional phasing methods, our approach models all loci covered by any sequencing read to enhance the estimation of error profiles in sequencing data, thereby increasing the statistical power of haplotype inference, especially for low-coverage datasets. Evaluations on both simulated and real sequencing data demonstrate the superior performance of our method, particularly in scenarios characterized by high sequencing error rates, low coverage, or polyploidy. Availability and implementation: Related codes and dataset can be found at: https://github.com/new-zbc/DIHap.