Hi there,

I am trying to use my 42 BAM files (aligned with Subjunc from RSubread package) to prepare a DEXSeqDataSet according to point 2 in the manual (https://bioconductor.org/packages/release/bioc/vignettes/DEXSeq/inst/doc/DEXSeq.html#2_Preparations)

I changed BamFileList(bamFiles) from the manual to bamFiles, because we used this function already on the bam files. But also with the original code snippet Rstudio crashed.

Since I have seven condition with each six replicates, I would love to use all BAM files for more complex analyses.

Are there any recommendations how to approach without the need of too much RAM (I have approx 60GB left over)?

Best Thomas

txdb = makeTxDbFromGFF("/home/chuddy/bioinformatics/ref_genomes/mouse_38/annotation/ensembl/Mus_musculus.GRCm38.102.gtf.gz")


flattenedAnnotation = exonicParts(txdb, linked.to.single.gene.only = TRUE)
names(flattenedAnnotation) = sprintf("%s:E%0.3d", flattenedAnnotation$gene_id, flattenedAnnotation$exonic_part)

bamFiles <- list.files("../BAM/subjunc/", full.names = T, include.dirs = F, pattern = "\\.BAM$")
bamFiles <- normalizePath(bamFiles) 

bamFiles = BamFileList(bamFiles)

se = summarizeOverlaps(flattenedAnnotation, 
                       bamFiles, 
                       singleEnd=T,
                       ignore.strand=TRUE)

sessionInfo( )

R version 4.3.2 (2023-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.6 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=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=de_CH.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=de_CH.UTF-8    LC_MESSAGES=en_US.UTF-8    LC_PAPER=de_CH.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C             LC_MEASUREMENT=de_CH.UTF-8 LC_IDENTIFICATION=C       

time zone: Europe/Zurich
tzcode source: system (glibc)

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

other attached packages:
 [1] lubridate_1.9.3             forcats_1.0.0               stringr_1.5.1               dplyr_1.1.4                
 [5] purrr_1.0.2                 readr_2.1.4                 tidyr_1.3.0                 tibble_3.2.1               
 [9] ggplot2_3.4.4               tidyverse_2.0.0             GenomicAlignments_1.36.0    Rsamtools_2.16.0           
[13] Biostrings_2.68.1           XVector_0.40.0              GenomicFeatures_1.52.2      DEXSeq_1.46.0              
[17] RColorBrewer_1.1-3          AnnotationDbi_1.62.2        DESeq2_1.40.2               SummarizedExperiment_1.30.2
[21] GenomicRanges_1.52.1        GenomeInfoDb_1.36.4         IRanges_2.34.1              S4Vectors_0.38.2           
[25] MatrixGenerics_1.12.3       matrixStats_1.2.0           Biobase_2.60.0              BiocGenerics_0.46.0        
[29] BiocParallel_1.34.2        

loaded via a namespace (and not attached):
 [1] DBI_1.2.0               bitops_1.0-7            biomaRt_2.56.1          rlang_1.1.2             magrittr_2.0.3         
 [6] compiler_4.3.2          RSQLite_2.3.4           png_0.1-8               vctrs_0.6.5             pkgconfig_2.0.3        
[11] crayon_1.5.2            fastmap_1.1.1           dbplyr_2.4.0            utf8_1.2.4              tzdb_0.4.0             
[16] bit_4.0.5               xfun_0.41               zlibbioc_1.46.0         cachem_1.0.8            progress_1.2.3         
[21] blob_1.2.4              DelayedArray_0.26.7     parallel_4.3.2          prettyunits_1.2.0       R6_2.5.1               
[26] stringi_1.8.3           rtracklayer_1.60.1      genefilter_1.82.1       Rcpp_1.0.11             knitr_1.45             
[31] timechange_0.2.0        Matrix_1.6-4            splines_4.3.2           tidyselect_1.2.0        rstudioapi_0.15.0      
[36] abind_1.4-5             yaml_2.3.8              codetools_0.2-19        hwriter_1.3.2.1         curl_5.2.0             
[41] lattice_0.22-5          withr_2.5.2             KEGGREST_1.40.1         survival_3.5-7          BiocFileCache_2.8.0    
[46] xml2_1.3.6              pillar_1.9.0            filelock_1.0.3          generics_0.1.3          RCurl_1.98-1.13        
[51] hms_1.1.3               munsell_0.5.0           scales_1.3.0            xtable_1.8-4            glue_1.6.2             
[56] tools_4.3.2             BiocIO_1.10.0           annotate_1.78.0         locfit_1.5-9.8          XML_3.99-0.16          
[61] grid_4.3.2              colorspace_2.1-0        GenomeInfoDbData_1.2.10 restfulr_0.0.15         cli_3.6.2              
[66] rappdirs_0.3.3          fansi_1.0.6             S4Arrays_1.0.6          gtable_0.3.4            digest_0.6.33          
[71] geneplotter_1.78.0      rjson_0.2.21            memoise_2.0.1           lifecycle_1.0.4         httr_1.4.7             
[76] statmod_1.5.0           bit64_4.0.5

You can read in chunks, using the 'yieldSize' argument, which may help with the memory situation. An alternative is to use Rsubread, which is likely faster, and might be more memory efficient.