Enter the body of text here

I have 26 Gb of RAM available on my laptop and I am trying to import a vcf file of 1.8GB using the readVcf function from the Variant Annotation package. So far the process was stopped every time. I guess it might be for some memory issue but I don't understand why because I shoud have enough memory I think. I am running this in a terminal on ubuntu.

Any ideas ?

Code should be placed in three backticks as shown below

# include your problematic code here with any corresponding output 
vcf <- readVcf('EA1EA2_all90_filt_map.recode.vcf')
Processus arrêté


# please also include the results of running the following in an R session 



sessionInfo( )

R version 3.6.3 (2020-02-29)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.3 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0

locale:
 [1] LC_CTYPE=fr_FR.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=fr_FR.UTF-8        LC_COLLATE=fr_FR.UTF-8    
 [5] LC_MONETARY=fr_FR.UTF-8    LC_MESSAGES=fr_FR.UTF-8   
 [7] LC_PAPER=fr_FR.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=fr_FR.UTF-8 LC_IDENTIFICATION=C       

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

other attached packages:
 [1] rtracklayer_1.46.0          VariantAnnotation_1.32.0   
 [3] GenomicAlignments_1.22.1    Rsamtools_2.2.3            
 [5] Biostrings_2.54.0           XVector_0.26.0             
 [7] SummarizedExperiment_1.16.1 DelayedArray_0.12.3        
 [9] BiocParallel_1.20.1         matrixStats_0.61.0         
[11] Biobase_2.46.0              GenomicRanges_1.38.0       
[13] GenomeInfoDb_1.22.1         IRanges_2.20.2             
[15] S4Vectors_0.24.4            BiocGenerics_0.32.0        

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.7             lattice_0.20-44        prettyunits_1.1.1     
 [4] assertthat_0.2.1       utf8_1.2.2             BiocFileCache_1.10.2  
 [7] R6_2.5.1               RSQLite_2.2.8          httr_1.4.2            
[10] pillar_1.6.2           zlibbioc_1.32.0        rlang_0.4.11          
[13] GenomicFeatures_1.38.2 progress_1.2.2         curl_4.3.2            
[16] blob_1.2.2             Matrix_1.3-4           stringr_1.4.0         
[19] RCurl_1.98-1.5         bit_4.0.4              biomaRt_2.42.1        
[22] compiler_3.6.3         pkgconfig_2.0.3        askpass_1.1           
[25] openssl_1.4.5          tidyselect_1.1.1       tibble_3.1.4          
[28] GenomeInfoDbData_1.2.2 XML_3.99-0.3           fansi_0.5.0           
[31] crayon_1.4.1           dplyr_1.0.7            dbplyr_2.1.1          
[34] bitops_1.0-7           rappdirs_0.3.3         grid_3.6.3            
[37] lifecycle_1.0.0        DBI_1.1.1              magrittr_2.0.1        
[40] stringi_1.7.4          cachem_1.0.6           ellipsis_0.3.2        
[43] generics_0.1.0         vctrs_0.3.8            tools_3.6.3           
[46] bit64_4.0.5            BSgenome_1.54.0        glue_1.4.2            
[49] purrr_0.3.4            hms_1.1.0              fastmap_1.1.0         
[52] AnnotationDbi_1.48.0   memoise_2.0.0

Usually when you run out of RAM, you usually get an error saying that 'R couldn't allocate a vector of size <some number>'. When it just says the process was stopped, that seems more like an error at the C level, where it hit something unexpected and just gave up. You could try a few things to try to track it down.

1. Update your R/Bioconductor. You are woefully out of date.
2. Use a VcfFile rather than a character file name
3. Read in each chromosome separately. To do this you have to use a VcfFile with an indexed VCF.

If you can read in each chromosome without problem, then maybe it is a memory issue. In which case if you are just reading in the VCF to process the data, maybe you could do it in chunks?