基于层次模型的 RNA 测序数据分析差异 RNA 编辑位点的统计推断。
Statistical inference of differential RNA-editing sites from RNA-sequencing data by hierarchical modeling.
机构信息
Bioinformatics Interdepartmental Program.
Department of Statistics.
出版信息
Bioinformatics. 2020 May 1;36(9):2796-2804. doi: 10.1093/bioinformatics/btaa066.
MOTIVATION
RNA-sequencing (RNA-seq) enables global identification of RNA-editing sites in biological systems and disease. A salient step in many studies is to identify editing sites that statistically associate with treatment (e.g. case versus control) or covary with biological factors, such as age. However, RNA-seq has technical features that incumbent tests (e.g. t-test and linear regression) do not consider, which can lead to false positives and false negatives.
RESULTS
In this study, we demonstrate the limitations of currently used tests and introduce the method, RNA-editing tests (REDITs), a suite of tests that employ beta-binomial models to identify differential RNA editing. The tests in REDITs have higher sensitivity than other tests, while also maintaining the type I error (false positive) rate at the nominal level. Applied to the GTEx dataset, we unveil RNA-editing changes associated with age and gender, and differential recoding profiles between brain regions.
AVAILABILITY AND IMPLEMENTATION
REDITs are implemented as functions in R and freely available for download at https://github.com/gxiaolab/REDITs. The repository also provides a code example for leveraging parallelization using multiple cores.
动机
RNA 测序（RNA-seq）能够在生物系统和疾病中全面鉴定 RNA 编辑位点。许多研究中的一个重要步骤是识别那些在统计学上与治疗（例如病例与对照）相关或与年龄等生物学因素相关的编辑位点。然而，RNA-seq 具有现行检验（例如 t 检验和线性回归）未考虑的技术特征，这可能导致假阳性和假阴性。
结果
在这项研究中，我们展示了现行检验的局限性，并介绍了一种方法，即 RNA 编辑检验（REDITs），这是一套利用贝塔二项式模型来识别差异 RNA 编辑的检验方法。REDITs 中的检验比其他检验具有更高的灵敏度，同时也将误报率（假阳性）维持在名义水平。应用于 GTEx 数据集，我们揭示了与年龄和性别相关的 RNA 编辑变化，以及大脑区域之间的差异重编码特征。
可用性和实现
REDITs 作为 R 中的函数实现，并可在 https://github.com/gxiaolab/REDITs 上免费下载。该存储库还提供了一个使用多个核心进行并行化的代码示例。
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