TSAPA：鉴定植物组织特异性可变多聚腺苷酸化位点。
TSAPA: identification of tissue-specific alternative polyadenylation sites in plants.
机构信息
Department of Automation.
Innovation Center for Cell Signaling Network, Xiamen University, Xiamen, China.
出版信息
Bioinformatics. 2018 Jun 15;34(12):2123-2125. doi: 10.1093/bioinformatics/bty044.
SUMMARY
Alternative polyadenylation (APA) is now emerging as a widespread mechanism modulated tissue-specifically, which highlights the need to define tissue-specific poly(A) sites for profiling APA dynamics across tissues. We have developed an R package called TSAPA based on the machine learning model for identifying tissue-specific poly(A) sites in plants. A feature space including more than 200 features was assembled to specifically characterize poly(A) sites in plants. The classification model in TSAPA can be customized by selecting desirable features or classifiers. TSAPA is also capable of predicting tissue-specific poly(A) sites in unannotated intergenic regions. TSAPA will be a valuable addition to the community for studying dynamics of APA in plants.
AVAILABILITY AND IMPLEMENTATION
https://github.com/BMILAB/TSAPA.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
摘要
可变多聚腺苷酸化（APA）现在作为一种广泛存在的机制正在被深入研究，这突显了在不同组织中对 APA 动态进行分析时，定义组织特异性多聚腺苷酸位点的必要性。我们基于机器学习模型开发了一个名为 TSAPA 的 R 包，用于鉴定植物中的组织特异性多聚腺苷酸位点。该模型构建了一个包含 200 多个特征的特征空间，用于专门描述植物中的多聚腺苷酸位点。TSAPA 中的分类模型可以通过选择理想的特征或分类器进行定制。TSAPA 还能够预测注释基因间区的组织特异性多聚腺苷酸位点。对于研究植物中 APA 的动态变化，TSAPA 将是该领域的一个有价值的补充。
可用性和实现
https://github.com/BMILAB/TSAPA。
补充信息
补充数据可在“Bioinformatics”在线获取。
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