Effect of de novo transcriptome assembly on transcript quantification
Effect of de novo transcriptome assembly on transcript quantification
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DOI：
10.1038/s41598-019-44499-3
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发表时间：
2019-06-05
影响因子：
4.6
通讯作者：
Chen, Chien-Yu
中科院分区：
文献类型：
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作者：
Hsieh, Ping-Han;Oyang, Yen-Jen;Chen, Chien-Yu
转录本表达的正确定量对于理解不同生理条件下的功能元件是必不可少的。对于没有参考转录组的微生物，必须在定量前进行从头转录组组装。然而，大量错误的重叠群产生的组装可能会导致不可靠的估计。在这方面，本研究探讨了组装质量如何影响基于从头转录组组装的定量性能。我们检查了过度延伸和不完整的重叠群，并证明组装完整性对重叠群丰度的估计有很大影响。然后，我们调查的行为的量词相对于序列的模糊性，这可能是最初在转录组或偶然产生的汇编。结果表明，数量词往往高估了家族折叠重叠群的表达，而低估了重复重叠群的表达。对于没有参考转录组的生物，检测家族折叠重叠群的不准确估计仍然具有挑战性。相反，我们观察到重复重叠群的低估情况可以通过分析由量词推断的连通分量中重叠群的估计丰度的读段比例（RPEA）来警告。此外，我们建议，估计的量化结果上的连接组件的水平有更好的准确性比序列水平的量化。本研究的分析结果为转录组组装和定量的未来发展提供了有价值的见解。
Correct quantification of transcript expression is essential to understand the functional elements in different physiological conditions. For the organisms without the reference transcriptome, de novo transcriptome assembly must be carried out prior to quantification. However, a large number of erroneous contigs produced by the assemblers might result in unreliable estimation. In this regard, this study investigates how assembly quality affects the performance of quantification based on de novo transcriptome assembly. We examined the over-extended and incomplete contigs, and demonstrated that assembly completeness has a strong impact on the estimation of contig abundance. Then we investigated the behavior of the quantifiers with respect to sequence ambiguity which might be originally presented in the transcriptome or accidentally produced by assemblers. The results suggested that the quantifiers often over-estimate the expression of family-collapse contigs and under-estimate the expression of duplicated contigs. For organisms without reference transcriptome, it remains challenging to detect the inaccurate estimation on family-collapse contigs. On the contrary, we observed that the situation of under-estimation on duplicated contigs can be warned through analyzing the read proportion of estimated abundance (RPEA) of contigs in the connected component inferenced by the quantifiers. In addition, we suggest that the estimated quantification results on the connected component level have better accuracy over sequence level quantification. The analytic results conducted in this study provides valuable insights for future development of transcriptome assembly and quantification.