Advanced Search
Volume 42 Issue 6
Jun.  2020
Turn off MathJax
Article Contents
Wenbin LIU, Bing WANG, Gang FANG, Xiaolong SHI, Peng XU. Study on the Differential Analysis of Alternative Splicing Based on the Median Value Jensen-Shannon Divergence[J]. Journal of Electronics & Information Technology, 2020, 42(6): 1392-1400. doi: 10.11999/JEIT190941
Citation: Wenbin LIU, Bing WANG, Gang FANG, Xiaolong SHI, Peng XU. Study on the Differential Analysis of Alternative Splicing Based on the Median Value Jensen-Shannon Divergence[J]. Journal of Electronics & Information Technology, 2020, 42(6): 1392-1400. doi: 10.11999/JEIT190941

Study on the Differential Analysis of Alternative Splicing Based on the Median Value Jensen-Shannon Divergence

doi: 10.11999/JEIT190941
Funds:  The National Key R&D Program of China (2019YFA0706402), The National Natural Science Foundation of China (61572367, 61573017, 61972107, 61972109)
  • Received Date: 2019-11-22
  • Rev Recd Date: 2020-04-24
  • Available Online: 2020-05-13
  • Publish Date: 2020-06-22
  • Alternative splicing is an important mechanism of protein diversity in a wide range of organisms, which plays an important role in the fine regulation of cell proliferation, differentiation, development, apoptosis and a series of important biological processes. In recent years, it is found that the occurrence of multiple complex diseases is often accompanied by the disordered expression of splicing isoforms. In order to study the difference of splicing isoforms on the whole distribution, a differential analysis method of Alternative Splicing (AS) based on the median value by Jensen-Shannon (JS) divergence is proposed in this paper. The results show the method can finds plenty of genes with significant differences in the overall distribution of splicing isoforms. These genes are not only concentrated in some cancer related pathways, but also in some signaling pathways based on alternative splicing regulation, cell division process and protein function. In addition, compared with the gene-level differential analysis, the genes with significant difference in alternative splicing also have better performance in survival analysis. In conclusion, the proposed method will lay a foundation for further revealing the mechanism of alternative splicing in cancer.
  • loading
  • WANG E T, SANDBERG R, LUO Shujun, et al. Alternative isoform regulation in human tissue transcriptomes[J]. Nature, 2008, 456(7221): 470–476. doi: 10.1038/nature07509
    ZHOU Yujie, ZHU Guiqi, ZHANG Qingwei, et al. Survival-associated alternative messenger RNA splicing signatures in pancreatic ductal adenocarcinoma: A study based on RNA-sequencing data[J]. DNA and Cell Biology, 2019, 38(11): 1207–1222. doi: 10.1089/dna.2019.4862
    XIE Zucheng, WU Huayu, DANG Yiwu, et al. Role of alternative splicing signatures in the prognosis of glioblastoma[J]. Cancer Medicine, 2019, 8(18): 7623–7636. doi: 10.1002/cam4.2666
    LI Mingxue, WANG Dun, HE Jianhua, et al. Bcl-XL: A multifunctional anti-apoptotic protein[J]. Pharmacological Research, 2020, 151: 104547. doi: 10.1016/j.phrs.2019.104547
    KOLE R, KRAINER A R, and ALTMAN S. RNA therapeutics: Beyond RNA interference and antisense oligonucleotides[J]. Nature Reviews Drug Discovery, 2012, 11(2): 125–140. doi: 10.1038/nrd3625
    SONG Jukun, LIU Yongda, SU Jiaming, et al. Systematic analysis of alternative splicing signature unveils prognostic predictor for kidney renal clear cell carcinoma[J]. Journal of Cellular Physiology, 2019, 234(12): 22753–22764. doi: 10.1002/jcp.28840
    DOU Tonghai, XU Jiaxi, GAO Yuan, et al. Evolution of peroxisome proliferator-activated receptor gamma alternative splicing[J]. Frontiers in Bioscience (Elite Edition) , 2010, 2: 1334–1343. doi: 10.2741/e193
    LI Ji, CHOI P S, CHAFFER C L, et al. An alternative splicing switch in FLNB promotes the mesenchymal cell state in human breast cancer[J]. eLife, 2018, 7: e37184. doi: 10.7554/eLife.37184
    SHEN Shihao, PARK J W, LU Zhixiang, et al. rMATS: Robust and flexible detection of differential alternative splicing from replicate RNA-Seq data[J]. Proceedings of the National Academy of Sciences of the United States of America, 2014, 111(51): E5593–E5601. doi: 10.1073/pnas.1419161111
    欧书华, 刘学军, 张礼. 基于KL散度的RNA-Seq数据差异异构体比例检测[J]. 计算机工程与科学, 2017, 39(1): 158–164. doi: 10.3969/j.issn.1007-130X.2017.01.022

    OU Shuhua, LIU Xuejun, and ZHANG Li. Differential isoform ratio detection based on KL divergence for RNA-Seq data[J]. Computer Engineering &Science, 2017, 39(1): 158–164. doi: 10.3969/j.issn.1007-130X.2017.01.022
    LIU Jingwei, LI Hao, SHEN Shixuan, et al. Alternative splicing events implicated in carcinogenesis and prognosis of colorectal cancer[J]. Journal of Cancer, 2018, 9(10): 1754–1764. doi: 10.7150/jca.24569
    ZONG Zhen, LI Hui, YI Chenghao, et al. Genome-wide profiling of prognostic alternative splicing signature in colorectal cancer[J]. Frontiers in Oncology, 2018, 8: 537. doi: 10.3389/fonc.2018.00537
    ZHANG Zijun, PAN Zhicheng, YING Yi, et al. Deep-learning augmented RNA-seq analysis of transcript splicing[J]. Nature Methods, 2019, 16(4): 307–310. doi: 10.1038/s41592-019-0351-9
    WHITFIELD M L, GEORGE L K, GRANT G D, et al. Common markers of proliferation[J]. Nature Reviews Cancer, 2006, 6(2): 99–106. doi: 10.1038/nrc1802
    INOUE K and FRY E A. Aberrant splicing of the DMP1-ARF-MDM2-p53 pathway in cancer[J]. International Journal of Cancer, 2016, 139(1): 33–41. doi: 10.1002/ijc.30003
    MUNDING E M, SHIUE L, KATZMAN S, et al. Competition between pre-mRNAs for the splicing machinery drives global regulation of splicing[J]. Molecular Cell, 2013, 51(3): 338–348. doi: 10.1016/j.molcel.2013.06.012
    CHU Xiufeng, ZHANG Ting, WANG Jie, et al. Alternative splicing variants of human Fbx4 disturb cyclin D1 proteolysis in human cancer[J]. Biochemical and Biophysical Research Communications, 2014, 447(1): 158–164. doi: 10.1016/j.bbrc.2014.03.129
    BAILEY M H, TOKHEIM C, PORTA-PARDO E, et al. Comprehensive characterization of cancer driver genes and mutations[J]. Cell, 2018, 173(2): 371–385. e18. doi: 10.1016/j.cell.2018.02.060
    曾勇, 舒欢, 胡江平, 等. 基于BP神经网络的自适应伪最近邻分类[J]. 电子与信息学报, 2016, 38(11): 2774–2779. doi: 10.11999/JEIT160133

    ZENG Yong, SHU Huan, HU Jiangping, et al. Adaptive pseudo nearest neighbor classification based on BP neural network[J]. Journal of Electronics &Information Technology, 2016, 38(11): 2774–2779. doi: 10.11999/JEIT160133
    陈素根, 吴小俊. 基于特征值分解的中心支持向量机算法[J]. 电子与信息学报, 2016, 38(3): 557–564. doi: 10.11999/JEIT150693

    CHEN Sugen and WU Xiaojun. Eigenvalue proximal support vector machine algorithm based on eigenvalue decoposition[J]. Journal of Electronics &Information Technology, 2016, 38(3): 557–564. doi: 10.11999/JEIT150693
    ZHANG Yangjun, YAN Libin, ZENG Jin, et al. Pan-cancer analysis of clinical relevance of alternative splicing events in 31 human cancers[J]. Oncogene, 2019, 38(40): 6678–6695. doi: 10.1038/s41388-019-0910-7
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(4)  / Tables(2)

    Article Metrics

    Article views (1732) PDF downloads(67) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return