Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.
Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.
Nat Commun. 2021 Nov 18;12(1):6728. doi: 10.1038/s41467-021-26944-y.
Genes in SARS-CoV-2 and other viruses in the order of Nidovirales are expressed by a process of discontinuous transcription which is distinct from alternative splicing in eukaryotes and is mediated by the viral RNA-dependent RNA polymerase. Here, we introduce the DISCONTINUOUS TRANSCRIPT ASSEMBLYproblem of finding transcripts and their abundances given an alignment of paired-end short reads under a maximum likelihood model that accounts for varying transcript lengths. We show, using simulations, that our method, JUMPER, outperforms existing methods for classical transcript assembly. On short-read data of SARS-CoV-1, SARS-CoV-2 and MERS-CoV samples, we find that JUMPER not only identifies canonical transcripts that are part of the reference transcriptome, but also predicts expression of non-canonical transcripts that are supported by subsequent orthogonal analyses. Moreover, application of JUMPER on samples with and without treatment reveals viral drug response at the transcript level. As such, JUMPER enables detailed analyses of Nidovirales transcriptomes under varying conditions.
冠状病毒科和套式病毒目其他病毒的基因通过一种不连续转录过程表达，该过程有别于真核生物的选择性剪接，由病毒 RNA 依赖性 RNA 聚合酶介导。在这里，我们提出了 DISCONTINUOUS TRANSCRIPT ASSEMBLY 问题，即在最大似然模型下，给定一对短读序列的比对，寻找转录本及其丰度，该模型考虑了转录本长度的变化。我们通过模拟表明，我们的方法 JUMPER 在经典转录本组装方面优于现有方法。在 SARS-CoV-1、SARS-CoV-2 和 MERS-CoV 样本的短读数据上，我们发现 JUMPER 不仅可以识别作为参考转录组一部分的规范转录本，还可以预测随后的正交分析支持的非规范转录本的表达。此外，在有和没有治疗的样本上应用 JUMPER 揭示了病毒药物在转录本水平上的反应。因此，JUMPER 能够在不同条件下对套式病毒目转录组进行详细分析。
