Description
Hello
I would like to try out rCube to analyze some TT-seq data (done in mouse, with 4su-labelled drosophila RNA as labelled spike and ERCC spike-ins as unlabelled control RNAs).
I am blocked early on, at the step of indicating intron coordinates. I tried to use createJunctionGRangesFromBam with a single BAM file from a total RNA sample:
> single_bam_to_find_introns
[1] "/path/to/bam/LRBS83.mm9dm6ERCC.nodups.bam"
> granges_bam_introns_GM_A <-createJunctionGRangesFromBam(single_bam_to_find_introns, ncores=10)
|======================= | 33%Error in result[[njob]] <- value :
attempt to select less than one element in OneIndex
> Error in serialize(data, node$con, xdr = FALSE) : ignoring SIGPIPE signal
>
I was wondering if someone could help me troubleshoot this error.
I should specify the fastq files (paired-end) were aligned using STAR 2.7.0 on a hybrid genome composed of mm9 (mouse), dm6 (fly) and ERCC 'genes'.
I attempted to generate a GTF file of introns, but the only method I know to achieve this is using an exon GTF (ftp://ftp.ensembl.org/pub/release-67/gtf/mus_musculus/) and introducing introns using genomeTools (http://genometools.org/tools.html). However I am experiencing a problem with that program too (GTF to GFF3 conversion fails).
I could generate intron information generated using hisat2_extract_splice_sites.py, but I have the impression the bed file that this generates will not work with rCube.
Any help will be much appreciated.
Thanks
Alex
> sessionInfo()
R version 3.6.0 (2019-04-26)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS/LAPACK: /cvmfs/soft.computecanada.ca/easybuild/software/2017/Core/imkl/2018.3.222/compilers_and_libraries_2018.3.222/linux/mkl/lib/intel64_lin/libmkl_gf_lp64.so
locale:
[1] LC_CTYPE=en_CA.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_CA.UTF-8 LC_COLLATE=en_CA.UTF-8
[5] LC_MONETARY=en_CA.UTF-8 LC_MESSAGES=en_CA.UTF-8
[7] LC_PAPER=en_CA.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_CA.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] rtracklayer_1.44.4 rCube_1.1.1
[3] SummarizedExperiment_1.14.1 DelayedArray_0.10.0
[5] BiocParallel_1.18.1 matrixStats_0.55.0
[7] Biobase_2.44.0 GenomicRanges_1.36.1
[9] GenomeInfoDb_1.20.0 IRanges_2.18.2
[11] S4Vectors_0.22.0 BiocGenerics_0.30.0
loaded via a namespace (and not attached):
[1] bit64_0.9-7 splines_3.6.0 Formula_1.2-3
[4] assertthat_0.2.1 latticeExtra_0.6-28 blob_1.2.0
[7] Rsamtools_2.0.0 GenomeInfoDbData_1.2.1 pillar_1.4.2
[10] RSQLite_2.1.2 backports_1.1.4 lattice_0.20-38
[13] glue_1.3.1 digest_0.6.20 RColorBrewer_1.1-2
[16] XVector_0.24.0 checkmate_1.9.4 colorspace_1.4-1
[19] htmltools_0.3.6 Matrix_1.2-17 DESeq2_1.24.0
[22] XML_3.98-1.20 pkgconfig_2.0.2 genefilter_1.66.0
[25] zlibbioc_1.30.0 purrr_0.3.2 xtable_1.8-4
[28] scales_1.0.0 htmlTable_1.13.1 tibble_2.1.3
[31] annotate_1.62.0 ggplot2_3.2.1 nnet_7.3-12
[34] lazyeval_0.2.2 survival_2.44-1.1 magrittr_1.5
[37] crayon_1.3.4 memoise_1.1.0 MASS_7.3-51.4
[40] foreign_0.8-71 tools_3.6.0 data.table_1.12.2
[43] stringr_1.4.0 locfit_1.5-9.1 munsell_0.5.0
[46] cluster_2.0.8 AnnotationDbi_1.46.1 Biostrings_2.52.0
[49] compiler_3.6.0 rlang_0.4.0 grid_3.6.0
[52] RCurl_1.95-4.12 rstudioapi_0.10 htmlwidgets_1.3
[55] bitops_1.0-6 base64enc_0.1-3 gtable_0.3.0
[58] DBI_1.0.0 R6_2.4.0 GenomicAlignments_1.20.1
[61] gridExtra_2.3 knitr_1.24 dplyr_0.8.3
[64] zeallot_0.1.0 bit_1.1-14 Hmisc_4.2-0
[67] stringi_1.4.3 Rcpp_1.0.2 geneplotter_1.62.0
[70] vctrs_0.2.0 rpart_4.1-15 acepack_1.4.1
[73] tidyselect_0.2.5 xfun_0.9