Jumper enables discontinuous transcript assembly in coronaviruses

Palash Sashittal, Chuanyi Zhang, Jian Peng and Mohammed El-Kebir ()
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Palash Sashittal: University of Illinois at Urbana-Champaign
Chuanyi Zhang: University of Illinois at Urbana-Champaign
Jian Peng: University of Illinois at Urbana-Champaign
Mohammed El-Kebir: University of Illinois at Urbana-Champaign

Nature Communications, 2021, vol. 12, issue 1, 1-12

Abstract: Abstract 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.

Date: 2021
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DOI: 10.1038/s41467-021-26944-y

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