Fig. 2 | Nature Communications

Fig. 2

From: Direct RNA sequencing on nanopore arrays redefines the transcriptional complexity of a viral pathogen

Fig. 2

Comparison of direct RNA nanopore sequencing to Illumina sequencing. a HSV-1 genome-wide sliding-window (100 nt) coverage plots of poly(A) RNA sequenced by nanopore (black) and Illumina (red) technologies. Nanopore reads represent a single polyadenylated RNA, directly sequenced, while Illumina reads are derived from highly fragmented poly(A)-selected RNAs. Illumina data (red dotted line) were normalized (red solid line) to produce the same overall coverage as the nanopore data. The HSV-1 genome is annotated with all canonical open-reading frames (ORFs) and colored according to kinetic class (green—immediate early, yellow—early, red—late, and gray—undefined). Multiple ORFs are grouped in polycistronic units and these are indicated by black hatched boxes. The y-axis represents absolute read-depth counts. Inset windows (blue hatched boxes) exemplify the 3′ bias inherent to direct RNA-seq (due to sequence reads being generated 3′ − > 5′) that is less prevalent in Illumina data. b Correlation analyses of HSV-1 genome coverage were generated using nanopore and Illumina sequence data. The sliding-window analysis was determined by calculating and plotting mean read-depth values per 100 nucleotide windows across canonically defined genic regions in both a strand-specific and strand-combined manner. c Dot plots denoting read- depth values (100-nt intervals) in genic and intergenic regions for both direct RNA-seq and normalized Illumina datasets. Read depths between genic and intergenic regions differ by a mean fold difference of 12.08 (nanopore) and 6.82 (Illumina). The y-axis is log-10 scaled. d, e Transcript abundances were counted for nanopore and Illumina datasets by aligning against two versions of the HSV-1 transcriptome. The simplified version (left) collapses polycistronic units into simple transcription units, while the standard version (right) comprises all individual coding units, whether mono- or polycistronic. The impact on comparative transcript abundance estimates is greater in the latter

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