Alternative polyadenylation (APA) enables a gene to generate multiple transcripts with different 3′ ends, which is dynamic across different cell types or conditions. Many computational methods have been developed to characterize sample-specific APA using the corresponding RNA-seq data, but suffered from high error rate on both polyadenylation site (PAS) identification and quantification of PAS usage (PAU), and bias toward 3′ untranslated regions. Here we developed a tool for APA identification and quantification (APAIQ) from RNA-seq data, which can accurately identify PAS and quantify PAU in a transcriptome-wide manner. Using 3′ end-seq data as the benchmark, we showed that APAIQ outperforms current methods on PAS identification and PAU quantification, including DaPars2, Aptardi, mountainClimber, SANPolyA, and QAPA. Finally, applying APAIQ on 421 RNA-seq samples from liver cancer patients, we identified >540 tumor-associated APA events and experimentally validated two intronic polyadenylation candidates, demonstrating its capacity to unveil cancer-related APA with a large-scale RNA-seq data set.

中文翻译：

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使用 APAIQ 从 RNA-seq 数据中准确识别和量化替代聚腺苷酸化

选择性聚腺苷酸化 (APA) 使基因能够生成具有不同 3' 末端的多个转录本，这在不同的细胞类型或条件下是动态的。已经开发了许多计算方法来使用相应的 RNA-seq 数据来表征样本特定的 APA，但是在聚腺苷酸化位点 (PAS) 识别和 PAS 使用 (PAU) 的量化方面都存在高错误率，并且偏向 3' 未翻译区域. 在这里，我们开发了一种用于从 RNA-seq 数据进行 APA 识别和量化 (APAIQ) 的工具，它可以在转录组范围内准确识别 PAS 和量化 PAU。使用 3' 端序列数据作为基准，我们表明 APAIQ 在 PAS 识别和 PAU 量化方面优于现有方法，包括 DaPars2、Aptardi、mountainClimber、SANPolyA 和 QAPA。最后，