Fast and accurate approximate inference of transcript expression from RNA-seq data.
Fast and accurate approximate inference of transcript expression from RNA-seq data.
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DOI：
10.1093/bioinformatics/btv483
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发表时间：
2015-12-15
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通讯作者：
Rattray M
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文献类型：
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作者：
Hensman J;Papastamoulis P;Glaus P;Honkela A;Rattray M
动机：将mRNA-seq读取到它们的原始转录本是转录本表达估计中的基本任务。当由于转录物共享序列（例如替代同种型或等位基因）而存在分配中的模糊性时，可以通过概率推断来解决该问题。与竞争方法相比，贝叶斯方法已被证明可以提供准确的转录本丰度估计。然而，精确的贝叶斯推理是棘手的，近似的方法，如马尔可夫链蒙特卡罗和变分贝叶斯（VB）通常使用。虽然提供了高度的准确性和建模灵活性，但标准实现对于大型数据集和复杂的转录组注释可能非常缓慢。结果如下：我们提出了一种新的近似推理方案的基础上VB和应用它的现有模型的转录表达推断RNA-seq数据。VB算法的最新进展被用来改善算法的收敛性，超过标准的变分贝叶斯期望最大化算法。我们将我们的算法应用于模拟和生物数据集，表现出速度的显着提高，表达水平估计的准确性只有很小的损失。我们对七种流行的替代方法进行了比较研究，并证明我们的新算法提供了出色的准确性和重复间一致性，同时在计算时间上保持竞争力。可用性和实现：这些方法是用R和C++实现的，并且可作为BitSeq项目的一部分在github.com/BitSeq上提供。该方法也可通过BitSeq Bioconductor包获得。可以通过github.com/BitSeq/BitSeqVB_benchmarking访问用于再现所有仿真结果的源代码。联系人：james. sheffield.ac.uk或panagiotis. manchester.ac.uk或Magnus. manchester.ac.uk补充信息：补充数据可在生物信息学在线获得。
Motivation: Assigning RNA-seq reads to their transcript of origin is a fundamental task in transcript expression estimation. Where ambiguities in assignments exist due to transcripts sharing sequence, e.g. alternative isoforms or alleles, the problem can be solved through probabilistic inference. Bayesian methods have been shown to provide accurate transcript abundance estimates compared with competing methods. However, exact Bayesian inference is intractable and approximate methods such as Markov chain Monte Carlo and Variational Bayes (VB) are typically used. While providing a high degree of accuracy and modelling flexibility, standard implementations can be prohibitively slow for large datasets and complex transcriptome annotations. Results: We propose a novel approximate inference scheme based on VB and apply it to an existing model of transcript expression inference from RNA-seq data. Recent advances in VB algorithmics are used to improve the convergence of the algorithm beyond the standard Variational Bayes Expectation Maximization algorithm. We apply our algorithm to simulated and biological datasets, demonstrating a significant increase in speed with only very small loss in accuracy of expression level estimation. We carry out a comparative study against seven popular alternative methods and demonstrate that our new algorithm provides excellent accuracy and inter-replicate consistency while remaining competitive in computation time. Availability and implementation: The methods were implemented in R and C++, and are available as part of the BitSeq project at github.com/BitSeq. The method is also available through the BitSeq Bioconductor package. The source code to reproduce all simulation results can be accessed via github.com/BitSeq/BitSeqVB_benchmarking. Contact: james.hensman@sheffield.ac.uk or panagiotis.papastamoulis@manchester.ac.uk or Magnus.Rattray@manchester.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.