Low-coverage whole-genome sequencing (lcWGS) provides a cost-effective method for genome-wide copy number variation (CNV) profiling, yet its technical limitations and analytical variability require systematic evaluation. We benchmarked five CNV detection tools using simulated and real-world datasets, focusing on sequencing depth, formalin-fixed paraffin-embedded (FFPE) artifacts, tumor purity, multi-center reproducibility, and signature-level stability. Our results demonstrate that ichorCNA outperformed other tools in precision and runtime at high purity (≥50%), making it the optimal choice for lcWGS-based workflows. Prolonged FFPE fixation induced artifactual short-segment CNVs due to formalin-driven DNA fragmentation, a bias none of the tools could computationally correct, necessitating strict fixation time control or prioritization of fresh-frozen samples. Multi-center analysis revealed high reproducibility for the same tool across sequencing facilities, but comparisons between different tools showed low concordance. Copy number features extracted by the Wang et al. method exhibited superior stability across conditions compared with the Steele et al. method and the Tao et al. method. This study establishes actionable guidelines for lcWGS: prioritize ichorCNA (ensuring ≥50% tumor purity), optimize FFPE protocol, and use Wang et al. features to ensure robust copy number profiling in precision oncology.

Copy number alterations (CNAs) are a predominant source of genetic alterations in human cancer and play an important role in cancer progression. However comprehensive understanding of the mutational processes and signatures of CNA is still lacking. Here we developed a mechanism-agnostic method to categorize CNA based on various fragment properties, which reflect the consequences of mutagenic processes and can be extracted from different types of data, including whole genome sequencing (WGS) and single nucleotide polymorphism (SNP) array. The 14 signatures of CNA have been extracted from 2778 pan-cancer analysis of whole genomes WGS samples, and further validated with 10 851 the cancer genome atlas SNP array dataset. Novel patterns of CNA have been revealed through this study. The activities of some CNA signatures consistently predict cancer patients' prognosis. This study provides a repertoire for understanding the signatures of CNA in cancer, with potential implications for cancer prognosis, evolution and etiology.

SUMMARY: UCSC Xena platform provides huge amounts of processed cancer omics data from large cancer research projects (e.g. TCGA, CCLE and PCAWG) or individual research groups and enables unprecedented research opportunities. However, a graphical user interface-based tool for interactively analyzing UCSC Xena data and generating elegant plots is still lacking, especially for cancer researchers and clinicians with limited programming experience. Here, we present UCSCXenaShiny, an R Shiny package for quickly searching, downloading, exploring, analyzing and visualizing data from UCSC Xena data hubs. This tool could effectively promote the practical use of public data, and can serve as an important complement to the current Xena genomics explorer. AVAILABILITY AND IMPLEMENTATION: UCSCXenaShiny is an open source R package under GPLv3 license and it is freely available at https://github.com/openbiox/UCSCXenaShiny or https://cran.r-project.org/package=UCSCXenaShiny. The docker image is available at https://hub.docker.com/r/shixiangwang/ucscxenashiny. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: Mutational signatures are recurring DNA alteration patterns caused by distinct mutational events during the evolution of cancer. In recent years, several bioinformatics tools are available for mutational signature analysis. However, most of them focus on specific type of mutation or have limited scope of application. A pipeline tool for comprehensive mutational signature analysis is still lacking. Here we present Sigflow pipeline, which provides an one-stop solution for de novo signature extraction, reference signature fitting, signature stability analysis, sample clustering based on signature exposure in different types of genome DNA alterations including single base substitution, doublet base substitution, small insertion and deletion and copy number alteration. A Docker image is constructed to solve the complex and time-consuming installation issues, and this enables reproducible research by version control of all dependent tools along with their environments. Sigflow pipeline can be applied to both human and mouse genomes. AVAILABILITY AND IMPLEMENTATION: Sigflow is an open source software under academic free license v3.0 and it is freely available at https://github.com/ShixiangWang/sigflow or https://hub.docker.com/r/shixiangwang/sigflow. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.