Hi,

I'm using Gviz's IdeogramTrack from my institution's cluster (IdeogramTrack(genome="mm10",chromosome="chr1")). When I try this from the master node it works fine but when I try this from any other node in the cluster which IO's through the master node, it hangs and eventually I get the error message:

Error: Internal Server Error

I am able to access http://genome.ucsc.edu or any other UCSC mirror through these nodes (using traceroute http://genome.ucsc.edu), and can successfully download data from other repositories such as Ensembl, (e.g., using getBM). 

Any idea what's wrong?

BTW, which port is IdeogramTrack trying to use?

> sessionInfo()
R version 3.3.2 (2016-10-31)
Platform: x86_64-redhat-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

locale:
 [1] LC_CTYPE=en_US.UTF-8          LC_NUMERIC=C                  LC_TIME=en_US.UTF-8          
 [4] LC_COLLATE=en_US.UTF-8        LC_MONETARY=en_US.UTF-8       LC_MESSAGES=en_US.UTF-8      
 [7] LC_PAPER=en_US.UTF-8          LC_NAME=en_US.UTF-8           LC_ADDRESS=en_US.UTF-8       
[10] LC_TELEPHONE=en_US.UTF-8      LC_MEASUREMENT=en_US.UTF-8    LC_IDENTIFICATION=en_US.UTF-8

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

other attached packages:
 [1] cellrangerRkit_0.99.0 Rmisc_1.5             bit64_0.9-5           bit_1.1-12            Biobase_2.34.0       
 [6] BiocGenerics_0.20.0   Matrix_1.2-7.1        Seurat_1.4.0.8        cowplot_0.7.0         doParallel_1.0.10    
[11] iterators_1.0.8       foreach_1.4.3         snpEnrichment_1.7.0   fgsea_1.0.2           Rcpp_0.12.9          
[16] data.tree_0.6.2       zoo_1.7-13            gplots_3.0.1          ggdendro_0.1-20       RColorBrewer_1.1-2   
[21] venneuler_1.1-0       rJava_0.9-8           scales_0.4.1          reshape2_1.4.2        plotrix_3.6-3        
[26] outliers_0.14         Hmisc_3.17-4          Formula_1.2-1         survival_2.40-1       lattice_0.20-34      
[31] data.table_1.9.6      edgeR_3.16.1          limma_3.30.2          ggpmisc_0.2.12        dplyr_0.5.0          
[36] plyr_1.8.4            magrittr_1.5          gridExtra_2.2.1       ggplot2_2.2.1        

loaded via a namespace (and not attached):
 [1] Rtsne_0.11          VGAM_1.0-3          minqa_1.2.4         colorspace_1.2-7    class_7.3-14       
 [6] modeltools_0.2-21   mclust_5.2          rstudioapi_0.6      MatrixModels_0.4-1  flexmix_2.3-13     
[11] mvtnorm_1.0-5       ranger_0.6.0        codetools_0.2-15    splines_3.3.2       snpStats_1.24.0    
[16] mnormt_1.5-5        robustbase_0.92-6   tclust_1.2-3        jsonlite_1.1        nloptr_1.0.4       
[21] caret_6.0-73        pbkrtest_0.4-6      cluster_2.0.5       kernlab_0.9-25      pheatmap_1.0.8     
[26] DiagrammeR_0.9.0    assertthat_0.1      lazyeval_0.2.0      lars_1.2            acepack_1.4.1      
[31] visNetwork_1.0.3    htmltools_0.3.5     quantreg_5.29       tools_3.3.2         igraph_1.0.1       
[36] gtable_0.2.0        fastmatch_1.0-4     rgexf_0.15.3        trimcluster_0.1-2   gdata_2.17.0       
[41] ape_4.0             nlme_3.1-128        fpc_2.1-10          stringr_1.1.0       lme4_1.1-12        
[46] irlba_2.1.2         gtools_3.5.0        XML_3.98-1.4        DEoptimR_1.0-6      zlibbioc_1.20.0    
[51] MASS_7.3-45         rhdf5_2.16.0        SparseM_1.72        pbapply_1.3-1       segmented_0.5-1.4  
[56] rpart_4.1-10        fastICA_1.2-0       latticeExtra_0.6-28 stringi_1.1.2       Rook_1.1-1         
[61] caTools_1.17.1      boot_1.3-18         BiocParallel_1.8.1  chron_2.3-47        prabclus_2.2-6     
[66] bitops_1.0-6        ROCR_1.0-7          htmlwidgets_0.8     R6_2.2.0            DBI_0.5-1          
[71] sn_1.4-0            foreign_0.8-67      mgcv_1.8-15         mixtools_1.0.4      nnet_7.3-12        
[76] tibble_1.2          tsne_0.1-3          car_2.1-3           KernSmooth_2.23-15  viridis_0.3.4      
[81] locfit_1.5-9.1      FNN_1.1             influenceR_0.1.0    ModelMetrics_1.1.0  digest_0.6.11      
[86] diptest_0.75-7      numDeriv_2016.8-1   brew_1.0-6          stats4_3.3.2        munsell_0.4.3      

Gviz is using functionality from the rtracklayer package to fetch the ideogram data from UCSC. If you run in to connectivity errors you should try to create a reproducible example using their core functions and report to them.

That being said, you do not have to build your IdeogramTrack objects from live UCSC data. You could download the chromosome bands table and store it locally, as described in the class' help page. My experience is that once running stuff in batches on a cluster, the less external network dependencies, the better.