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 DO_SCPLOWISCONTINUOUSC_SCPLOW TO_SCPLOWRANSCRIPTC_SCPLOW AO_SCPLOWSSEMBLYC_SCPLOW problem of finding transcripts [Formula] and their abundances c given an alignment [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, JO_SCPLOWUMPERC_SCPLOW, drastically outperforms existing methods for classical transcript assembly. On short-read data of SARS-CoV-1 and SARS-CoV-2 samples, we find that JO_SCPLOWUMPERC_SCPLOW 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. JO_SCPLOWUMPERC_SCPLOW enables detailed analyses of Nidovirales transcriptomes. Code availabilitySoftware is available at https://github.com/elkebir-group/Jumper