MOTIVATION: Structural variants (SVs) influence gene regulation, disease progression, and diagnostics, yet integrating SV calls across platforms remains difficult due to inconsistent annotations, limited merging flexibility, and fragmented workflows. Ambiguous breakend (BND) annotations, which comprise many variant calls, are often discarded or misclassified, hindering variant characterization. Existing tools lack advanced merging operations essential for precise identification of disease-specific or somatic variants across samples or patient groups. Additionally, current SV analysis pipelines require extensive manual intervention and complex parameter tuning, compromising reproducibility and scalability. Addressing these gaps is crucial for improving the accuracy, interpretability, and clinical utility of SV analyses. RESULTS: We developed OctopuSV and TentacleSV to address these long-standing challenges in SV analysis. OctopuSV features a specialized BND correction module that converts ambiguous BND annotations into canonical SV types, recovering important variants that are often overlooked by existing tools. Additionally, it provides advanced set operations (difference, complement, custom-defined) that enable sophisticated variant filtering without programming expertise, critical for identifying tumor-specific SVs or variants unique to specific sample groups. TentacleSV completes our solution by automating the entire SV analysis process from raw sequencing data to high-confidence callsets, ensuring consistency and reproducibility across projects. Benchmarking across short-read and long-read platforms showed superior F1 score, complete SV type consistency compared to existing tools. Our framework enables experimental biologists and clinical researchers to perform sophisticated analyses ranging from cancer subtype-specific SV identification to multi-sample comparative studies without requiring specialized programming skills. AVAILABILITY AND IMPLEMENTATION: All codes are available at https://github.com/ylab-hi/OctopuSV; https://github.com/ylab-hi/TentacleSV.

Studies have identified genes and molecular pathways regulating cancer metastasis. However, it remains largely unknown whether metastatic potentials of cancer cells from different lineage types are driven by the same or different gene networks. Here, we aim to address this question through integrative analyses of 493 human cancer cells' transcriptomic profiles and their metastatic potentials in vivo. Using an unsupervised approach and considering both gene coexpression and protein-protein interaction networks, we identify different gene networks associated with various biological pathways (i.e. inflammation, cell cycle, and RNA translation), the expression of which are correlated with metastatic potentials across subsets of lineage types. By developing a regularized random forest regression model, we show that the combination of the gene module features expressed in the native cancer cells can predict their metastatic potentials with an overall Pearson correlation coefficient of 0.90. By analyzing transcriptomic profile data from cancer patients, we show that these networks are conserved in vivo and contribute to cancer aggressiveness. The intrinsic expression levels of these networks are correlated with drug sensitivity. Altogether, our study provides novel comparative insights into cancer cells' intrinsic gene networks mediating metastatic potentials across different lineage types, and our results can potentially be useful for designing personalized treatments for metastatic cancers.

SUMMARY: HLA allele imputation from SNP genotypes has become increasingly useful, but its accuracy is heavily dependent on the reference panels used. HLA-IMPUTER implements HIBAG algorithm for HLA imputation with different population specific reference panels, including a new Han Chinese reference panel derived from 10 689 samples. We provide a convenient platform for researchers to impute HLA alleles and perform association analysis. AVAILABILITY AND IMPLEMENTATION: http://wyanglab.org: 3838/RefPanelWebsite/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: Insertion and deletion (indels) have been recognized as an important source generating tumor-specific mutant peptides (neoantigens). The focus of indel-derived neoantigen identification has been on leveraging DNA sequencing such as whole exome sequencing, with the effort of using RNA-seq less well explored. Here we present ScanNeo, a fast-streamlined computational pipeline for analyzing RNA-seq to predict neoepitopes derived from small to large-sized indels. We applied ScanNeo in a prostate cancer cell line and validated our predictions with matched mass spectrometry data. Finally, we demonstrated that indel neoantigens predicted from RNA-seq were associated with checkpoint inhibitor response in a cohort of melanoma patients. AVAILABILITY AND IMPLEMENTATION: ScanNeo is implemented in Python. It is freely accessible at the GitHub repository (https://github.com/ylab-hi/ScanNeo). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.