Fusion Analysis

Introduction of Fusion Analysis

Gene fusion refers to the fusion of some or all of the sequences of two different genes due to some mechanism (such as genome mutation) to form a new gene. Gene fusion includes fusion at the genome level and fusion at the transcriptome level. At the DNA level, a new gene composed of two or more genes is called a fusion gene. At the RNA level, a transcript composed of multiple transcripts is called a fusion transcript. In a broad sense, these two are called fusion genes. There are three main mechanisms for gene fusion: chromosomal translocation, interstitial deletion and chromosomal inversion.

As a product of chromosome structural variation, fusion genes have been proved to be related to the occurrence of certain cancers, and they are the key targets for research on certain cancers. Fusion genes are caused by chromosomal mutations, and fusion genes are often oncogenes. When genes that regulate cell proliferation, differentiation, and apoptosis are fused, it will directly affect downstream signal transmission pathways, leading to enhanced cell proliferation, apoptosis barriers, and differentiation barriers, affecting the expression of normal shapes and causing cancer. Therefore, through fusion analysis, accurately finding fusion gene events can improve the efficiency of disease and drug research.

Fig 1. Schematic diagram of gene fusion process. - CD ComputaBio.

Fig 1. Schematic diagram of gene fusion process.

Fusion Analysis Process

The gene fusion analysis identified by whole-genome sequencing can determine that the gene fusion is caused by a certain mutation at the genome level. The gene fusion identified by transcriptome sequencing data can determine whether the fusion gene can be expressed and the level of expression. Therefore, combining whole genome sequencing and transcriptome sequencing for gene fusion analysis can obtain more accurate identification results. The principle of fusion analysis is as follows:

  • First of all, map the reads obtained by sequencing to the genome. If it is a fusion gene, the fragment corresponding to the fusion gene will cover the connection point of the fusion gene, which is the aforementioned fusion point.First of all, the fragment corresponding to the fusion gene covers the connection point of the fusion gene, which is the fusion point mentioned above.
  • Secondly, if a read in read 1 or read 2 is located on both sides of the connection point, such a fragment is called split reads. If the two reads of read 1 and read 2 are not covered by the connection point, it is just that their comparison positions are located in two different gene, such fragments are called spanning reads.
  • Thirdly, among the above two kinds of reads, the split read directly detects the reads covering the connection point, so it is more convincing. Spanning reads can only indirectly indicate that it is a potential fusion gene, and its interpretation is slightly weaker. In actual analysis, the number of these two kinds of reads will be counted. The more the number, the greater the possibility of a true fusion gene.

Application Filed

  • Research on the occurrence and development of various diseases, especially cancer.
  • Fusion genes as potential drug targets for drug discovery and research.

CD ComputaBio provides fusion analysis based on different software (such as OAPfuse, FusionCatcher, EricScript, chimerascan and JAFFA, etc.). With the help of machine learning methods, such as the Naive Bayes algorithm, the fusion gene prediction machine learning model is established to score the predicted gene fusion results, thereby improving the accuracy of predicting the fusion gene. For fusion analysis, we provide you with a one-stop data analysis service, and based on your data, we will match you with the most suitable analysis software or joint analysis of multiple software. You only need to provide the original data, and we will provide you with a complete result report. In addition, we can also provide you with customized analysis services. For fusion analysis, if you have any questions, please feel free to contact us, we look forward to working with you.


  • Silvia L, et al. Comprehensive evaluation of fusion transcript detection algorithms and a meta-caller to combine top performing methods in paired-end RNA-seq data[J]. Nuclc Acids Research, 2016(5):e47-e47.
  • Soda M, et al. Identification of the transforming EML4-ALK fusion gene in non-small-cell lung cancer.[J]. Nature, 2007, 448(7153):561-566.


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