Hi all, I am trying to visualize mapped reads from nanopore sequencing by Gviz. Unluckily, even if I am able to visualize short reads from illumina alignments that we have, Gviz seems to fail to show long reads alignment from nanopore.

Is there any option I should use in order to use a bam obtained from long read sequencing?

here my code:

library(Gviz)
library(BSgenome.Hsapiens.UCSC.hg19)
afrom <- 44905200
ato <- 44947200
bam <- "path/to/my/sorted_indexed_bam.bam"

alTrack <- AlignmentsTrack(bam, isPaired = F)
sTrack <- SequenceTrack(Hsapiens)
plotTracks(c(alTrack, sTrack), chromosome = "chr21", from = afrom, to = ato)

R version 3.6.1 (2019-07-05)
Platform: x86_64-conda_cos6-linux-gnu (64-bit)

Matrix products: default
BLAS/LAPACK: /home/gianlucam/tools/anaconda2/envs/R/lib/R/lib/libRblas.so

locale:
[1] en_US.UTF-8

attached base packages:
 [1] grid      stats4    parallel  stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] Gviz_1.28.3                       BSgenome.Hsapiens.UCSC.hg19_1.4.0 BSgenome_1.52.0                   rtracklayer_1.44.4                Biostrings_2.52.0                 XVector_0.24.0                   
 [7] GenomicRanges_1.36.1              GenomeInfoDb_1.20.0               IRanges_2.18.3                    S4Vectors_0.22.1                  BiocGenerics_0.30.0              

loaded via a namespace (and not attached):
 [1] ProtGenerics_1.16.0         bitops_1.0-6                matrixStats_0.58.0          bit64_4.0.5                 RColorBrewer_1.1-2          progress_1.2.2              httr_1.4.2                  tools_3.6.1                
 [9] backports_1.2.1             utf8_1.2.1                  R6_2.5.0                    rpart_4.1-15                Hmisc_4.5-0                 DBI_1.1.1                   lazyeval_0.2.2              colorspace_2.0-0           
[17] nnet_7.3-15                 tidyselect_1.1.0            gridExtra_2.3               prettyunits_1.1.1           curl_4.3                    bit_4.0.4                   compiler_3.6.1              Biobase_2.44.0             
[25] htmlTable_2.1.0             DelayedArray_0.10.0         scales_1.1.1                checkmate_2.0.0             stringr_1.4.0               digest_0.6.27               Rsamtools_2.0.3             foreign_0.8-76             
[33] base64enc_0.1-3             dichromat_2.0-0             jpeg_0.1-8.1                pkgconfig_2.0.3             htmltools_0.5.1.1           ensembldb_2.8.1             fastmap_1.1.0               htmlwidgets_1.5.3          
[41] rlang_0.4.10                rstudioapi_0.13             RSQLite_2.2.5               generics_0.1.0              BiocParallel_1.18.1         dplyr_1.0.5                 VariantAnnotation_1.30.1    RCurl_1.98-1.3             
[49] magrittr_2.0.1              GenomeInfoDbData_1.2.1      Formula_1.2-4               Matrix_1.3-2                Rcpp_1.0.6                  munsell_0.5.0               fansi_0.4.2                 lifecycle_1.0.0            
[57] stringi_1.5.3               SummarizedExperiment_1.14.1 zlibbioc_1.30.0             blob_1.2.1                  crayon_1.4.1                lattice_0.20-41             splines_3.6.1               GenomicFeatures_1.36.4     
[65] hms_1.0.0                   knitr_1.31                  pillar_1.5.1                biomaRt_2.40.5              XML_3.99-0.3                glue_1.4.2                  biovizBase_1.32.0           latticeExtra_0.6-29        
[73] data.table_1.14.0           png_0.1-7                   vctrs_0.3.7                 gtable_0.3.0                purrr_0.3.4                 assertthat_0.2.1            cachem_1.0.4                ggplot2_3.3.3              
[81] xfun_0.22                   AnnotationFilter_1.8.0      survival_3.2-10             tibble_3.1.0                GenomicAlignments_1.20.1    AnnotationDbi_1.46.1        memoise_2.0.0               cluster_2.1.1              
[89] ellipsis_0.3.1