BPPARAM does not exist in the current workspace

[mkgoita]

Hello, I would like to use VAriantFiltering for NGS analysis. I successfully installed VariantFiltering
I was following the instructions using the test data and then came across this issue blocking my progress.
BPPRAM seems causing the issue but I don't know how to deal with the error. Any help or explanation will be appreciated.
Find below the commands, errors, and session info.

CEUvcf <- file.path(system.file("extdata", package="VariantFiltering"), "CEUtrio.vcf.bgz")
CEUped <- file.path(system.file("extdata", package="VariantFiltering"), "CEUtrio.ped")
param <- VariantFilteringParam(vcfFilename=CEUvcf, pedFilename=CEUped)

reHet <- autosomalRecessiveHeterozygous(param )

or

reHet <- autosomalRecessiveHeterozygous(param, svparam=ScanVcfParam(), BPPARAM=bpparam("SerialParam") )

Error in .local(param, ...) : 
  Parallel back-end function bpparam given in argument 'BPPARAM' does not exist in the current workspace. Either you did not write correctly the function name or you did not load the package 'BiocParallel'.Parallel back-end function SerialParam given in argument 'BPPARAM' does not exist in the current workspace. Either you did not write correctly the function name or you did not load the package 'BiocParallel'.

sessionInfo()
R version 4.3.2 (2023-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.3 LTS

Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0

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

time zone: Africa/Bamako
tzcode source: system (glibc)

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

other attached packages:
[1] phastCons100way.UCSC.hg19_3.7.2 SIFT.Hsapiens.dbSNP137_1.0.0 PolyPhen.Hsapiens.dbSNP131_1.0.2
[4] RSQLite_2.3.3 MafDb.1Kgenomes.phase1.hs37d5_3.10.0 GenomicScores_2.14.1
[7] SNPlocs.Hsapiens.dbSNP144.GRCh37_0.99.20 TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2 GenomicFeatures_1.54.1
[10] org.Hs.eg.db_3.18.0 AnnotationDbi_1.64.1 BSgenome.Hsapiens.1000genomes.hs37d5_0.99.1
[13] BSgenome_1.70.1 rtracklayer_1.62.0 BiocIO_1.12.0
[16] VariantFiltering_1.38.0 VariantAnnotation_1.48.1 Rsamtools_2.18.0
[19] Biostrings_2.70.1 XVector_0.42.0 SummarizedExperiment_1.32.0
[22] GenomicRanges_1.54.1 GenomeInfoDb_1.38.1 IRanges_2.36.0
[25] S4Vectors_0.40.1 MatrixGenerics_1.14.0 matrixStats_1.1.0
[28] BiocParallel_1.36.0 Biobase_2.62.0 BiocGenerics_0.48.1

loaded via a namespace (and not attached):
[1] RColorBrewer_1.1-3 jsonlite_1.8.7 rstudioapi_0.15.0 magrittr_2.0.3
[5] rmarkdown_2.25 zlibbioc_1.48.0 vctrs_0.6.4 memoise_2.0.1
[9] RCurl_1.98-1.13 base64enc_0.1-3 htmltools_0.5.7 S4Arrays_1.2.0
[13] progress_1.2.2 AnnotationHub_3.10.0 curl_5.1.0 Rhdf5lib_1.24.0
[17] SparseArray_1.2.2 Formula_1.2-5 rhdf5_2.46.0 htmlwidgets_1.6.3
[21] Gviz_1.46.1 cachem_1.0.8 GenomicAlignments_1.38.0 mime_0.12
[25] lifecycle_1.0.4 pkgconfig_2.0.3 Matrix_1.6-3 R6_2.5.1
[29] fastmap_1.1.1 GenomeInfoDbData_1.2.11 shiny_1.8.0 digest_0.6.33
[33] colorspace_2.1-0 shinyTree_0.3.1 Hmisc_5.1-1 filelock_1.0.2
[37] fansi_1.0.5 httr_1.4.7 abind_1.4-5 compiler_4.3.2
[41] bit64_4.0.5 htmlTable_2.4.2 backports_1.4.1 DBI_1.1.3
[45] HDF5Array_1.30.0 biomaRt_2.58.0 rappdirs_0.3.3 DelayedArray_0.28.0
[49] rjson_0.2.21 tools_4.3.2 foreign_0.8-85 interactiveDisplayBase_1.40.0
[53] httpuv_1.6.12 nnet_7.3-19 glue_1.6.2 restfulr_0.0.15
[57] rhdf5filters_1.14.1 promises_1.2.1 grid_4.3.2 checkmate_2.3.0
[61] cluster_2.1.4 generics_0.1.3 gtable_0.3.4 ensembldb_2.26.0
[65] data.table_1.14.8 hms_1.1.3 xml2_1.3.5 utf8_1.2.4
[69] BiocVersion_3.18.1 pillar_1.9.0 stringr_1.5.1 later_1.3.1
[73] dplyr_1.1.4 BiocFileCache_2.10.1 lattice_0.22-5 deldir_2.0-2
[77] bit_4.0.5 biovizBase_1.50.0 tidyselect_1.2.0 RBGL_1.78.0
[81] knitr_1.45 gridExtra_2.3 ProtGenerics_1.34.0 xfun_0.41
[85] DT_0.30 stringi_1.8.2 lazyeval_0.2.2 yaml_2.3.7
[89] evaluate_0.23 codetools_0.2-19 interp_1.1-4 tibble_3.2.1
[93] BiocManager_1.30.22 graph_1.80.0 cli_3.6.1 rpart_4.1.21
[97] shinythemes_1.2.0 xtable_1.8-4 munsell_0.5.0 dichromat_2.0-0.1
[101] Rcpp_1.0.11 dbplyr_2.4.0 png_0.1-8 XML_3.99-0.15
[105] parallel_4.3.2 ellipsis_0.3.2 ggplot2_3.4.4 blob_1.2.4
[109] prettyunits_1.2.0 jpeg_0.1-10 latticeExtra_0.6-30 AnnotationFilter_1.26.0
[113] bitops_1.0-7 scales_1.2.1 crayon_1.5.2 rlang_1.1.2
[117] KEGGREST_1.42.0 shinyjs_2.1.0

[rcastelo]

hi,

Thanks for your detailed reporting. The parallelization is made possible through functions, such as bpparam(), from another Bioconductor package called BiocParallel. Please try to install the package `'BiocParallel' and load it before using the functionality of VariantFiltering.

[PetJana]

Hello,

Thank you for the nice package!

I have the same problem, also after installing and loading the BiocParallel package. The previous user seems to have done the same, as BiocParallel is listed in his sessionInfo.

I would really appreciate your help.

[rcastelo]

Hi, oh I see, I thought it was a problem about how it was being used, but there was really a tiny bug in the autosomalRecessiveHeterozygous() function, not affecting the rest of the functions. I have submitted a bugfix in the devel version of the package 1.41.1, unfortunately I cannot patch anymore the current release version because we are close to the next release, but that next release version 1.42.0 will include the bugfix and will be available in about ten days, on October 30th.

[rcastelo]

I discovered another bug in the autosomalRecessiveHeterozygous() function, this has been fixed in version 1.41.2, and will also become part of the forthcoming release version 1.42.0.