Liu Yuanhang, Wu Ping, Zhou Jingqi, Johnson-Pais Teresa L, Lai Zhao, Chowdhury Wasim H, Rodriguez Ronald, Chen Yidong
Greehey Children's Cancer Research Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA.
Department of Cellular and Structure Biology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA.
BMC Bioinformatics. 2017 Oct 3;18(Suppl 11):384. doi: 10.1186/s12859-017-1803-9.
RNA sequencing (RNA-seq) is a high throughput technology that profiles gene expression in a genome-wide manner. RNA-seq has been mainly used for testing differential expression (DE) of transcripts between two conditions and has recently been used for testing differential alternative polyadenylation (APA). In the past, many algorithms have been developed for detecting differentially expressed genes (DEGs) from RNA-seq experiments, including the one we developed, XBSeq, which paid special attention to the context-specific background noise that is ignored in conventional gene expression quantification and DE analysis of RNA-seq data.
We present several major updates in XBSeq2, including alternative statistical testing and parameter estimation method for detecting DEGs, capacity to directly process alignment files and methods for testing differential APA usage. We evaluated the performance of XBSeq2 against several other methods by using simulated datasets in terms of area under the receiver operating characteristic (ROC) curve (AUC), number of false discoveries and statistical power. We also benchmarked different methods concerning execution time and computational memory consumed. Finally, we demonstrated the functionality of XBSeq2 by using a set of in-house generated clear cell renal carcinoma (ccRCC) samples.
We present several major updates to XBSeq. By using simulated datasets, we demonstrated that, overall, XBSeq2 performs equally well as XBSeq in terms of several statistical metrics and both perform better than DESeq2 and edgeR. In addition, XBSeq2 is faster in speed and consumes much less computational memory compared to XBSeq, allowing users to evaluate differential expression and APA events in parallel. XBSeq2 is available from Bioconductor: http://bioconductor.org/packages/XBSeq/.
RNA测序（RNA-seq）是一种高通量技术，可在全基因组范围内分析基因表达。RNA-seq主要用于检测两种条件下转录本的差异表达（DE），最近也被用于检测差异可变多聚腺苷酸化（APA）。过去，已经开发了许多算法用于从RNA-seq实验中检测差异表达基因（DEG），包括我们开发的XBSeq，它特别关注在传统的RNA-seq数据基因表达定量和DE分析中被忽略的上下文特异性背景噪声。
我们展示了XBSeq2的几个主要更新，包括用于检测DEG的替代统计检验和参数估计方法、直接处理比对文件的能力以及检测差异APA使用情况的方法。我们通过使用模拟数据集，根据受试者工作特征（ROC）曲线下面积（AUC）、错误发现数量和统计功效，评估了XBSeq2与其他几种方法的性能。我们还对不同方法在执行时间和消耗的计算内存方面进行了基准测试。最后，我们通过使用一组内部生成的透明细胞肾细胞癌（ccRCC）样本展示了XBSeq2的功能。
我们展示了XBSeq的几个主要更新。通过使用模拟数据集，我们证明，总体而言，XBSeq2在几个统计指标方面与XBSeq表现相当，并且两者都比DESeq2和edgeR表现更好。此外，与XBSeq相比，XBSeq2速度更快，消耗的计算内存少得多，允许用户并行评估差异表达和APA事件。可从Bioconductor获取XBSeq2：http://bioconductor.org/packages/XBSeq/ 。
