Efficient and comprehensive representation of uniqueness for next-generation sequencing by minimum unique length analyses.
Efficient and comprehensive representation of uniqueness for next-generation sequencing by minimum unique length analyses.
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DOI：
10.1371/journal.pone.0053822
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发表时间：
2013
期刊：
影响因子：
3.7
通讯作者：
Sandberg R
中科院分区：
文献类型：
--
作者：
Storvall H;Ramsköld D;Sandberg R
随着下一代测序技术变得更加高效和便宜，RNA-Seq正在成为转录组研究中广泛使用的技术。RNA-Seq数据的计算分析通常始于将数百万个短读段映射回基因组或转录组，在这个过程中，发现一些读段同样很好地映射到多个基因组位置（多重映射读段）。我们已经开发了最小唯一长度工具（MULTo），这是一个有效和全面表示可映射性信息的框架，通过识别每个基因组坐标在基因组和转录组中变得独特所需的最短可能长度。使用最小的独特长度信息，我们比较了不同的独特性补偿方法的转录表达水平的定量和证明，最好的补偿是通过丢弃多映射读取和正确调整基因模型的长度。我们还探索了小鼠基因组特定区域内的独特性和增强子定位实验。最后，通过向社区提供MULTo，我们希望促进在RNA-Seq分析中使用独特性补偿，并消除制作额外可映射性文件的需要。
As next generation sequencing technologies are getting more efficient and less expensive, RNA-Seq is becoming a widely used technique for transcriptome studies. Computational analysis of RNA-Seq data often starts with the mapping of millions of short reads back to the genome or transcriptome, a process in which some reads are found to map equally well to multiple genomic locations (multimapping reads). We have developed the Minimum Unique Length Tool (MULTo), a framework for efficient and comprehensive representation of mappability information, through identification of the shortest possible length required for each genomic coordinate to become unique in the genome and transcriptome. Using the minimum unique length information, we have compared different uniqueness compensation approaches for transcript expression level quantification and demonstrate that the best compensation is achieved by discarding multimapping reads and correctly adjusting gene model lengths. We have also explored uniqueness within specific regions of the mouse genome and enhancer mapping experiments. Finally, by making MULTo available to the community we hope to facilitate the use of uniqueness compensation in RNA-Seq analysis and to eliminate the need to make additional mappability files.
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