Fusion genes are important targets and biomarkers for cancer therapy. Methods of accurately detecting fusion genes are needed in clinical practice. RNA-seq is widely used to detect active fusion genes. Long-read RNA-seq can sequence the full length of mRNA, and long-read RNA-seq is expected to detect fusion genes that cannot be detected by short-read RNA-seq. However, long-read RNA-seq has high basecalling error rates, and gap sequences may occur near the breakpoints of long reads that are not aligned to the genome. When gap sequences occur, it is impossible to identify the correct fusion gene or breakpoint using existing methods. To address these challenges in fusion gene detection, we introduce a novel algorithm, FUGAREC (fusion detection with gap re-alignment and breakpoint clustering). FUGAREC uniquely combines gap sequence re-alignment with breakpoint clustering. This approach not only enhances the detection of previously undetectable fusion genes but also significantly reduces false positives. We demonstrate that FUGAREC has high fusion gene detection performance on both simulated data and sequenced data of a breast cancer cell line.

The advent of antibody therapy has brought about a change in the treatment of diseases. The efficacy of antibody modeling relies on the intricate atomic interactions between antibodies and antigens. Traditional methods for determining antibody structures, such as X-ray crystallography, are costly and time-consuming. Computational docking offers a faster and more cost-effective approach to obtaining complex antibody and antigen complexes even in challenging scenarios. Rosetta, a widely employed software for protein structure modeling, incorporates a scoring function specifically tailored for modeling antibody-antigen interactions. The unique characteristics of the antibody-antigen interface can result in inaccurate predictions. Therefore, it is essential to understand the existing scoring function and the behavior of the antibody-antigen interface. In this study, we evaluated specific parameters within Rosetta-derived scoring functions, with a particular focus on the energy landscape of the structures they generated. We found that performance in antibody-antigen docking simulations could be enhanced by omitting parameters related to solvation. Also, we delved into the physico-chemical properties of antibody-antigen interfaces, paying special attention to the complementarity-determining regions and epitopes. Our exploration helped identify certain parameters that significantly influence docking simulation performance. These insights pave the way for the creation of more accurate scoring functions tailored for specific antibody-antigen interactions.