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.

MOTIVATION: The Oxford Nanopore sequencing enables to directly detect methylation states of bases in DNA from reads without extra laboratory techniques. Novel computational methods are required to improve the accuracy and robustness of DNA methylation state prediction using Nanopore reads. RESULTS: In this study, we develop DeepSignal, a deep learning method to detect DNA methylation states from Nanopore sequencing reads. Testing on Nanopore reads of Homo sapiens (H. sapiens), Escherichia coli (E. coli) and pUC19 shows that DeepSignal can achieve higher performance at both read level and genome level on detecting 6 mA and 5mC methylation states comparing to previous hidden Markov model (HMM) based methods. DeepSignal achieves similar performance cross different DNA methylation bases, different DNA methylation motifs and both singleton and mixed DNA CpG. Moreover, DeepSignal requires much lower coverage than those required by HMM and statistics based methods. DeepSignal can achieve 90% above accuracy for detecting 5mC and 6 mA using only 2× coverage of reads. Furthermore, for DNA CpG methylation state prediction, DeepSignal achieves 90% correlation with bisulfite sequencing using just 20× coverage of reads, which is much better than HMM based methods. Especially, DeepSignal can predict methylation states of 5% more DNA CpGs that previously cannot be predicted by bisulfite sequencing. DeepSignal can be a robust and accurate method for detecting methylation states of DNA bases. AVAILABILITY AND IMPLEMENTATION: DeepSignal is publicly available at https://github.com/bioinfomaticsCSU/deepsignal. SUPPLEMENTARY INFORMATION: Supplementary data are available at bioinformatics online.