CRAC: an integrated approach to the analysis of RNA-seq reads
CRAC: an integrated approach to the analysis of RNA-seq reads
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DOI：
10.1186/gb-2013-14-3-r30
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发表时间：
2013-01-01
期刊：
影响因子：
12.3
通讯作者：
Rivals, Eric
中科院分区：
文献类型：
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作者：
Philippe, Nicolas;Salson, Mikael;Rivals, Eric
大量的rna测序研究开始预测突变、剪接或融合rna。我们提出了一种方法，CRAC，它整合了基因组位置和局部覆盖范围，使这种预测能够直接从RNA-seq读取分析中进行。k-mer分析方法检测候选突变，索引和剪接或嵌合连接在每个单读。与现有工具相比，CRAC提高了精度，在不损失灵敏度的情况下，拼接结的精度达到99:5%。重要的是，CRAC预测随着读取长度的增加而提高。在癌症文库中，CRAC恢复了74%的验证融合rna，并预测了新的复发嵌合连接。CRAC可在http://crac.gforge.inria.fr上获得。
A large number of RNA-sequencing studies set out to predict mutations, splice junctions or fusion RNAs. We propose a method, CRAC, that integrates genomic locations and local coverage to enable such predictions to be made directly from RNA-seq read analysis. A k-mer profiling approach detects candidate mutations, indels and splice or chimeric junctions in each single read. CRAC increases precision compared with existing tools, reaching 99:5% for splice junctions, without losing sensitivity. Importantly, CRAC predictions improve with read length. In cancer libraries, CRAC recovered 74% of validated fusion RNAs and predicted novel recurrent chimeric junctions. CRAC is available at http://crac.gforge.inria.fr.