QUANTIFYING ALTERNATIVE SPLICING FROM PAIRED-END RNA-SEQUENCING DATA.
QUANTIFYING ALTERNATIVE SPLICING FROM PAIRED-END RNA-SEQUENCING DATA.
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DOI：
10.1214/13-aoas687
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发表时间：
2014-03
期刊：
影响因子：
--
通讯作者：
Stöcker A
中科院分区：
文献类型：
--
作者：
Rossell D;Stephan-Otto Attolini C;Kroiss M;Stöcker A
RNA测序已经彻底改变了生物医学研究，特别是我们研究基因选择性剪接的能力。这个问题对人类健康具有重要影响，因为选择性剪接可能涉及细胞水平的功能障碍和多种疾病。然而，数据的高维性和实验偏差的存在对数据分析提出了严峻的挑战。我们发现，用于研究选择性剪接的标准数据摘要是非常有限的，因为它们忽略了大量有价值的信息。目前的数据分析方法是基于这样的总结，因此是次优的。此外，它们在考虑技术偏差方面的灵活性有限。我们提出了新的数据摘要和贝叶斯建模框架，克服了这些限制，并以非参数，高度灵活的方式确定偏见。这些总结自然适应测序技术的快速改进。我们提供有效的点估计和不确定性评估。该方法允许研究个体样品的选择性剪接模式，也可以作为下游分析的基础。我们发现了几倍的改进，估计均方误差比较流行的方法在模拟中，和实验数据中的重复之间的一致性大大提高。我们的研究结果表明，需要调整选择性剪接RNA-seq研究的常规总结和分析。我们在R软件包casper* 中提供软件实现
RNA-sequencing has revolutionized biomedical research and, in particular, our ability to study gene alternative splicing. The problem has important implications for human health, as alternative splicing may be involved in malfunctions at the cellular level and multiple diseases. However, the high-dimensional nature of the data and the existence of experimental biases pose serious data analysis challenges. We find that the standard data summaries used to study alternative splicing are severely limited, as they ignore a substantial amount of valuable information. Current data analysis methods are based on such summaries and are hence sub-optimal. Further, they have limited flexibility in accounting for technical biases. We propose novel data summaries and a Bayesian modeling framework that overcome these limitations and determine biases in a non-parametric, highly flexible manner. These summaries adapt naturally to the rapid improvements in sequencing technology. We provide efficient point estimates and uncertainty assessments. The approach allows to study alternative splicing patterns for individual samples and can also be the basis for downstream analyses. We found a several fold improvement in estimation mean square error compared popular approaches in simulations, and substantially higher consistency between replicates in experimental data. Our findings indicate the need for adjusting the routine summarization and analysis of alternative splicing RNA-seq studies. We provide a software implementation in the R package casper*
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