With the development and application of next-generation sequencing (NGS) and target capture technology, the demand for an effective analysis method to accurately detect gene fusion from high-throughput data is growing. Hence, we developed a novel fusion gene analyzing method called single-end gene fusion (SEGF) by starting with single-end DNA-seq data. This approach takes raw sequencing data as input, and integrates the commonly used alignment approach basic local alignment search tool (BLAST) and short oligonucleotide analysis package (SOAP) with stringent passing filters to achieve successful fusion gene detection. To evaluate SEGF, we compared it with four other fusion gene discovery analysis methods by analyzing sequencing results of 23 standard DNA samples and DNA extracted from 286 lung cancer formalin fixed paraffin embedded (FFPE) samples. The results generated by SEGF indicated that it not only detected the fusion genes from standard samples and clinical samples, but also had the highest accuracy and sensitivity among the five compared methods. In addition, SEGF was capable of detecting complex gene fusion types from single-end NGS sequencing data compared with other methods. By using SEGF to acquire gene fusion information at DNA level, more useful information can be retrieved from the DNA panel or other DNA sequencing methods without generating RNA sequencing information to benefit clinical diagnosis or medication instruction. It was a timely and cost-effective measure with regard to research or diagnosis. Considering all the above, SEGF is a straightforward method without manipulating complicated arguments, providing a useful approach for the precise detection of gene fusion variation.
Keywords: fusion detection; single-end gene fusion; single-end next-generation sequencing data.