I am following the ATACseqQC vignette (https://bioconductor.org/packages/release/bioc/vignettes/ATACseqQC/inst/doc/ATACseqQC.html) to analyze several ATAC seq samples, and I keep getting a segfault error when I run

outPath <- "splited"
dir.create(outPath)
## shift the coordinates of 5'ends of alignments in the bam file
library(TxDb.Hsapiens.UCSC.hg38.knownGene)
## if you don't have an available TxDb, please refer
## GenomicFeatures::makeTxDbFromGFF to create one from gff3 or gtf file.
seqlev <- c("chr1", "chr2", "chr3", "chr4", "chr5", 
             "chr6", "chr7", "chr8", "chr9", "chr10", 
             "chr11", "chr12", "chr13", "chr14", "chr15",
             "chr16", "chr17", "chr18", "chr19", "chr20",
             "chr21", "chr22")
seqinformation <- seqinfo(TxDb.Hsapiens.UCSC.hg.38knownGene)
which <- as(seqinformation[seqlev], "GRanges")
gal <- readBamFile(bamfile, tag=tags, which=which, asMates=TRUE, bigFile=TRUE)
shiftedBamfile <- file.path(outPath, "shifted.bam")
gal1 <- shiftGAlignmentsList(gal, outbam=shiftedBamfile)

I have changed from the original vignette to get all 'normal' chromosomes from just "chr1". I also am using TxDb.Hsapiens.UCSC.hg38.knownGene rather than TxDb.Hsapiens.UCSC.hg19.knownGene. The error comes when shifting the alignments using shiftGAlignmentsList, and the error I get is

 *** caught segfault ***
address 0xffffffff9a378e20, cause 'memory not mapped'

Traceback:
 1: .Call(.merge_bam, files, destination, overwrite, header, region,     byQname, addRG, compressLevel1)
 2: doTryCatch(return(expr), name, parentenv, handler)
 3: tryCatchOne(expr, names, parentenv, handlers[[1L]])
 4: tryCatchList(expr, classes, parentenv, handlers)
 5: tryCatch({    files <- sapply(files, .normalizePath)    destination <- .normalizePath(destination)    region <- local({        x <- as(region, "GRanges")        if (1L < length(x))             stop("'region' must specify one range")        sprintf("%s:%d-%d", as.character(seqnames(x)), start(x),             end(x))    })    if (!overwrite && file.exists(destination)) {        msg <- sprintf("'%s' exists, '%s' is FALSE\n  %s: %s",             "destination", "overwrite", "destination", destination)        stop(msg)    }    header <- .normalizePath(header)    destination <- .Call(.merge_bam, files, destination, overwrite,         header, region, byQname, addRG, compressLevel1)    if (indexDestination)         indexBam(destination)    destination}, error = function(err) {    msg <- sprintf("'mergeBam' %s", conditionMessage(err))    stop(msg)})
 6: .local(files, destination, ...)
 7: mergeBam(outfile, destination = tempfile(fileext = ".bam"), indexDestination = TRUE,     header = meta$file)
 8: mergeBam(outfile, destination = tempfile(fileext = ".bam"), indexDestination = TRUE,     header = meta$file)
 9: shiftGAlignmentsList(gal, outbam = shiftedBamfile)

I have referred to this stackoverflow post (https://stackoverflow.com/questions/49190251/caught-segfault-memory-not-mapped-error-in-r) that mentioned something similar, but reinstalling/updating my packages did not work. I would love any help fixing what should be a fairly straightforward analysis! Here is my sessionInfo:

sessionInfo()
R version 4.0.0 (2020-04-24)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Red Hat Enterprise Linux

Matrix products: default
BLAS/LAPACK: /zapps7/intel_parallel_studio_xe/2020/compilers_and_libraries_2020.0.166/linux/mkl/lib/intel64_lin/libmkl_gf_lp64.so

locale:
[1] C

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

other attached packages:
 [1] TxDb.Hsapiens.UCSC.hg38.knownGene_3.10.0
 [2] GenomicFeatures_1.42.3
 [3] AnnotationDbi_1.52.0
 [4] Biobase_2.50.0
 [5] Rsamtools_2.6.0
 [6] Biostrings_2.58.0
 [7] XVector_0.30.0
 [8] GenomicRanges_1.42.0
 [9] GenomeInfoDb_1.26.7
[10] IRanges_2.24.1
[11] ggplot2_3.3.3
[12] ATACseqQC_1.14.4
[13] S4Vectors_0.28.1
[14] BiocGenerics_0.36.1

loaded via a namespace (and not attached):
  [1] colorspace_2.0-1              ellipsis_0.3.2
  [3] futile.logger_1.4.3           rstudioapi_0.13
  [5] farver_2.1.0                  ChIPpeakAnno_3.24.2
  [7] bit64_4.0.5                   interactiveDisplayBase_1.28.0
  [9] fansi_0.5.0                   xml2_1.3.2
 [11] motifStack_1.34.0             splines_4.0.0
 [13] cachem_1.0.5                  ade4_1.7-16
 [15] polynom_1.4-0                 dbplyr_2.1.1
 [17] png_0.1-7                     graph_1.68.0
 [19] shiny_1.6.0                   HDF5Array_1.18.1
 [21] BiocManager_1.30.15           compiler_4.0.0
 [23] httr_1.4.2                    assertthat_0.2.1
 [25] Matrix_1.3-4                  fastmap_1.1.0
 [27] lazyeval_0.2.2                limma_3.46.0
 [29] later_1.2.0                   formatR_1.11
 [31] htmltools_0.5.1.1             prettyunits_1.1.1
 [33] tools_4.0.0                   gtable_0.3.0
 [35] glue_1.4.2                    GenomeInfoDbData_1.2.4
 [37] dplyr_1.0.6                   rappdirs_0.3.3
 [39] Rcpp_1.0.6                    vctrs_0.3.8
 [41] rhdf5filters_1.2.1            multtest_2.46.0
 [43] rtracklayer_1.50.0            stringr_1.4.0
 [45] mime_0.10                     lifecycle_1.0.0
 [47] ensembldb_2.14.1              XML_3.99-0.6
 [49] AnnotationHub_2.22.1          edgeR_3.32.1
 [51] zlibbioc_1.36.0               MASS_7.3-54
 [53] scales_1.1.1                  BSgenome_1.58.0
 [55] hms_1.1.0                     promises_1.2.0.1
 [57] MatrixGenerics_1.2.1          ProtGenerics_1.22.0
 [59] SummarizedExperiment_1.20.0   RBGL_1.66.0
 [61] rhdf5_2.34.0                  AnnotationFilter_1.14.0
 [63] lambda.r_1.2.4                yaml_2.2.1
 [65] curl_4.3.1                    memoise_2.0.0
 [67] biomaRt_2.46.3                stringi_1.6.2
 [69] RSQLite_2.2.7                 BiocVersion_3.12.0
 [71] randomForest_4.6-14           BiocParallel_1.24.1
 [73] rlang_0.4.11                  pkgconfig_2.0.3
 [75] matrixStats_0.59.0            bitops_1.0-7
 [77] lattice_0.20-41               purrr_0.3.4
 [79] Rhdf5lib_1.12.1               labeling_0.4.2
 [81] htmlwidgets_1.5.3             GenomicAlignments_1.26.0
 [83] bit_4.0.4                     tidyselect_1.1.1
 [85] magrittr_2.0.1                R6_2.5.0
 [87] generics_0.1.0                DelayedArray_0.16.3
 [89] DBI_1.1.1                     withr_2.4.2
 [91] preseqR_4.0.0                 pillar_1.6.1
 [93] survival_3.2-11               KEGGREST_1.30.1
 [95] RCurl_1.98-1.3                tibble_3.1.2
 [97] crayon_1.4.1                  futile.options_1.0.1
 [99] KernSmooth_2.23-20            utf8_1.2.1
[101] BiocFileCache_1.14.0          progress_1.2.2
[103] locfit_1.5-9.4                grid_4.0.0
[105] blob_1.2.1                    GenomicScores_2.2.0
[107] digest_0.6.27                 xtable_1.8-4
[109] VennDiagram_1.6.20            httpuv_1.6.1
[111] regioneR_1.22.0               openssl_1.4.4
[113] munsell_0.5.0                 askpass_1.1

Hello Nolan,

Based on the error you got, I think your computer RAM is not big enough for this analysis. Usually I use a high performance computer cluster to do analyze.

Haibo