Splice Junction Analysis

Introduction of Splice Junction Analysis

In higher eukaryotes, the precursor mRNA (pre-mRNA) of some genes produces different mRNA splicing isoforms through different splicing methods ( selecting different splicing sites), and this process is called alternative splicing. Alternative splicing allows a gene to produce multiple different transcripts. These transcripts have their own specific expression and function in different tissues at different stages of cell or individual differentiation and development. This greatly enriches the types and quantities of coding RNA and non-coding RNA, thereby increasing the complexity of the transcriptome and proteome. Most genes with protein-coding capabilities can be regulated by alternative splicing, resulting in a variety of protein subtypes with different functions. Abnormal alternative splicing is also widely involved in a variety of diseases, such as cancer. Therefore, exploring alternative splicing events is crucial for further understanding the functions of different transcripts in organisms.

Studies have shown that 35% of human genetic diseases may be caused by alternative splicing of RNA, such as retinitis pigmentosa and spinal muscular atrophy. With the development of artificial intelligence, methods such as deep learning combined with Bayesian hypothesis testing have been used in RNA alternative splicing analysis, which effectively improves the accuracy of RNA-seq quantitative differential splicing. Accurately identifying and analyzing the splice site through splice junctions analysis is of great significance for the treatment of related diseases and drug development.

Fig 1. Different forms of alternative splicing. - CD ComputaBio.

Fig 1. Different forms of alternative splicing.

Splice Junction Analysis Process

Splice junction analysis process mainly including the following steps:

  • Alignment and generation of assembled transcripts.
  • Generation of assembled transcripts.
  • Junction extraction.
  • Junction annotation and integration.
  • Visualization of analysis results.

Application Filed

  • Research on the occurrence and development of various diseases, especially cancer.
  • Provide clues for disease treatment.
  • Drug discovery and research.
  • Animal physiological function research.
  • Research on the molecular biology mechanism of plant growth and development.

CD ComputaBio provides splice junction analysis based on different software (such as DARTS, GeneSplicer, MaxEntScan, dbscSNV,S-CAP, MMSplice, clinVar, spliceAI, MISO, and rMATS, etc.). In addition, with the help of artificial intelligence methods, the advantages of deep learning and Bayesian hypothesis testing statistical models are used to provide a better means of alternative splicing junction analysis for sequencing data. Improving the sensitivity and accuracy of alternative splicing analysis. For splice junction 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 for DNA sequencing data or RNA sequencing data. 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.


  • Zhang Z, et al. Deep-learning augmented RNA-seq analysis of transcript splicing[J]. Nature Methods, 2019, 16(4):307-310.
  • Zhao J, et al. ASJA: A Program for Assembling Splice Junctions Analysis[J]. Computational and Structural Biotechnology Journal, 2019, 17.
  • Dlamini Z, et al. Abnormalities in alternative splicing in diabetes: therapeutic targets[J]. Journal of Molecular Endocrinology, 2017, 59(2):R93-R107.


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