Tools and best practices for data processing in allelic expression analysis.
Tools and best practices for data processing in allelic expression analysis.
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DOI：
10.1186/s13059-015-0762-6
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发表时间：
2015-09-17
期刊：
影响因子：
12.3
通讯作者：
Lappalainen T
中科院分区：
文献类型：
--
作者：
Castel SE;Levy-Moonshine A;Mohammadi P;Banks E;Lappalainen T
等位基因表达分析已成为重要的整合基因组和转录组数据，以表征各种生物现象，如顺式调控变异和无义介导的衰变。我们分析了等位基因表达读段计数数据的特性和错误的技术来源，例如低质量或重复计数的RNA-seq读段、基因分型错误、等位基因作图偏倚和由于样品制备和测序引起的技术协变量，以及总读段深度的变化。我们提供了纠正这些错误的指导方针，表明我们的质量控制措施提高了相关等位基因表达的检测，并介绍了从RNA测序数据高通量生产等位基因表达数据的工具。本文的在线版本（doi：10.1186/s13059-015-0762-6）包含补充材料，可供授权用户使用。
Allelic expression analysis has become important for integrating genome and transcriptome data to characterize various biological phenomena such as cis-regulatory variation and nonsense-mediated decay. We analyze the properties of allelic expression read count data and technical sources of error, such as low-quality or double-counted RNA-seq reads, genotyping errors, allelic mapping bias, and technical covariates due to sample preparation and sequencing, and variation in total read depth. We provide guidelines for correcting such errors, show that our quality control measures improve the detection of relevant allelic expression, and introduce tools for the high-throughput production of allelic expression data from RNA-sequencing data. The online version of this article (doi:10.1186/s13059-015-0762-6) contains supplementary material, which is available to authorized users.