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最新可变剪接分析工具rMATS: Robust and flexible detection of differential alternative splicing from replicate RNA-Seq data  

2015-07-06 09:58:26|  分类: 文献学习 |  标签: |举报 |字号 订阅

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Alternative splicing (AS) is an important mechanism of eukaryotic gene regulation. Deep RNA sequencing (RNA-Seq) has become a powerful approach for quantitative profiling of AS. With the increasing capacity of high-throughput sequencers, it has become common for RNA-Seq studies of AS to examine multiple biological replicates. We developed rMATS, a new statistical method for robust and flexible detection of differential AS from replicate RNA-Seq data. Besides the analysis of unpaired replicates, rMATS includes a model specifically designed for paired replicates, such as case–control matched pairs in clinical RNA-Seq datasets. We expect rMATS will be useful for genome-wide studies of AS in diverse research projects. Our data also provide new insights about the experimental design for RNA-Seq studies of AS.

ABSTRACT

Ultra-deep RNA sequencing (RNA-Seq) has become a powerful approach for genome-wide analysis of pre-mRNA alternative splicing. We previously developed multivariate analysis of transcript splicing (MATS), a statistical method for detecting differential alternative splicing between two RNA-Seq samples. Here we describe a new statistical model and computer program, replicate MATS (rMATS), designed for detection of differential alternative splicing from replicate RNA-Seq data. rMATS uses a hierarchical model to simultaneously account for sampling uncertainty in individual replicates and variability among replicates. In addition to the analysis of unpaired replicates, rMATS also includes a model specifically designed for paired replicates between sample groups. The hypothesis-testing framework of rMATS is flexible and can assess the statistical significance over any user-defined magnitude of splicing change. The performance of rMATS is evaluated by the analysis of simulated and real RNA-Seq data. rMATS outperformed two existing methods for replicate RNA-Seq data in all simulation settings, and RT-PCR yielded a high validation rate (94%) in an RNA-Seq dataset of prostate cancer cell lines. Our data also provide guiding principles for designing RNA-Seq studies of alternative splicing. We demonstrate that it is essential to incorporate biological replicates in the study design. Of note, pooling RNAs or merging RNA-Seq data from multiple replicates is not an effective approach to account for variability, and the result is particularly sensitive to outliers. The rMATS source code is freely available at rnaseq-mats.sourceforge.net/. As the popularity of RNA-Seq continues to grow, we expect rMATS will be useful for studies of alternative splicing in diverse RNA-Seq projects.
最新可变剪接分析工具rMATS: Robust and flexible detection of differential alternative splicing from replicate RNA-Seq data - 喜欢吃桃子 - wangyufeng的博客
 An example of rMATS unpaired analysis of prostate cancer cell lines. (A) The RNA-Seq read counts and estimated exon inclusion levels of ARHGAP17 exon 15 in a pair of epithelial (PC3E) and mesenchymal (GS689) cell lines, each with three biological replicates. (B) The log likelihood of observing the data given all possible combinations of 
ψi1,ψi2. In this likelihood-ratio test, the null hypothesis is |ψi1?ψi2|5% and the alternative hypothesis is |ψi1?ψi2|>5%. The combination of ψi1,ψi2 that maximizes the likelihood of observing the data under the constraint of the null hypothesis or without such a constraint is indicated.
Proc Natl Acad Sci U S A. 2014 Dec 23; 111(51): E5593–E5601.
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