Data from: Improving transcriptome assembly through error correction of high-throughput sequence reads

The study of functional genomics, particularly in non-model organisms, has been dramatically improved over the last few years by the use of transcriptomes and RNAseq. While these studies are potentially extremely powerful, a computationally intensive procedure, the de novo construction of a reference transcriptome must be completed as a prerequisite to further analyses. The accurate reference is critically important as all downstream steps, including estimating transcript abundance are critically dependent on the construction of an accurate reference. Though a substantial amount of research has been done on assembly, only recently have the pre-assembly procedures been studied in detail. Specifically, several stand-alone error correction modules have been reported on and, while they have shown to be effective in reducing errors at the level of sequencing reads, how error correction impacts assembly accuracy is largely unknown. Here, we show via use of a simulated and empiric dataset, that applying error correction to sequencing reads has significant positive effects on assembly accuracy, and should be applied to all datasets. A complete collection of commands which will allow for the production of Reptile corrected reads is available at https://github.com/macmanes/error_correction/tree/master/scripts

功能基因组学（functional genomics），尤其是针对非模式生物（non-model organisms）的研究，近年来借助转录组（transcriptome）与RNA测序（RNAseq）技术的应用得到了长足发展。尽管此类研究具备极强的应用潜力，但从头构建参考转录组（de novo construction of a reference transcriptome）属于计算密集型流程，必须先完成该步骤，方可开展后续分析。准确的参考转录组至关重要，因为包括转录本丰度估算在内的所有下游分析步骤，均高度依赖于高质量的参考转录组构建。尽管针对序列组装已开展了大量研究，但直至近年，组装前预处理流程才得到细致探究。具体而言，已有多款独立的错误校正模块被报道，尽管这些模块已被证实可有效降低测序读段（sequencing reads）层面的错误，但错误校正如何影响组装精度，目前仍鲜为人知。本研究通过模拟数据集与实测数据集证实，对测序读段应用错误校正可显著提升组装精度，因此所有数据集均应进行错误校正处理。可生成Reptile校正后读段的完整命令集，可通过https://github.com/macmanes/error_correction/tree/master/scripts 获取。