Jumper Enables Discontinuous Transcript Assembly in Coronaviruses

Genes in SARS-CoV-2 and, more generally, in viruses in the order of Nidovirales are expressed by a process of discontinuous transcription mediated by the viral RNA-dependent RNA polymerase. This process is distinct from alternative splicing in eukaryotes, rendering current transcript assembly methods unsuitable to Nidovirales sequencing samples. Here, we introduce the D <sc>iscontinuous</sc> T <sc>ranscript</sc> A <sc>ssembly</sc> problem of finding transcripts <inline-formula> <alternatives> <inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="431026v1_inline1.gif"/> </alternatives> </inline-formula> and their abundances c given an alignment <inline-formula> <alternatives> <inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="431026v1_inline2.gif"/> </alternatives> </inline-formula> under a maximum likelihood model that accounts for varying transcript lengths. Underpinning our approach is the concept of a segment graph, a directed acyclic graph that, distinct from the splice graph used to characterize alternative splicing, has a unique Hamiltonian path. We provide a compact characterization of solutions as subsets of non-overlapping edges in this graph, enabling the formulation of an efficient mixed integer linear program. We show using simulations that our method, J <sc>umper</sc> , drastically outperforms existing methods for classical transcript assembly. On short-read data of SARS-CoV-1 and SARS-CoV-2 samples, we find that J <sc>umper</sc> not only identifies canonical transcripts that are part of the reference transcriptome, but also predicts expression of non-canonical transcripts that are well supported by direct evidence from long-read data, presence in multiple, independent samples or a conserved core sequence. J <sc>umper</sc> enables detailed analyses of Nidovirales transcriptomes.