DeepIndel: A ResNet-Based Method for Accurate Insertion and Deletion Detection from Long-Read Sequencing

Structural variations (SVs) play a pivotal role in human genetics, influencing gene expression, disease mechanisms, and phenotypic diversity. Despite the advancements in short-read sequencing technologies, long-read sequencing offers superior resolution for detecting SVs, particularly in complex genomic regions. In this study, DeepIndel, a novel computational framework, is presented that leverages long-read sequencing data combined with a deep learning model to identify SV breakpoints accurately. This approach captures complex breakpoint patterns by aligning long reads to a reference genome and extracting 23 key features at each genomic location, including read support, candidate length, and strand-specific information. DeepIndel has been evaluated on the HG002 dataset, achieving exceptional performance with high precision and reliability in detecting insertions and deletions, with F1 scores (94.27% for insertions, 91.09% for deletions) and thereby demonstrating significant improvements over existing state-of-the-art tools, offering a more precise and robust approach to SV detection. This work advances structural variant analysis, with promising implications for genomic research, disease understanding, and personalized medicine.

@article{Sifat2025,
  title     = {DeepIndel: A ResNet-Based Method for Accurate Insertion and Deletion Detection from Long-Read Sequencing},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {8},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Md. Shadmim Hasan Sifat and Khandokar Md. Rahat Hossain},
  doi       = {10.14569/IJACSA.2025.0160850},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160850}
}