error in loadFun(x,seqname,ranges). BSgenome.Hsapiens.UCSC.hg19
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1
Entering edit mode
@sophialovechan-15987
Last seen 3.3 years ago

Hi, I am using BSgenome.Hsapiens.UCSC.hg19 trying to add GC bias into my ATAC sequencing data. But I kept having a problem when loading hg19 genome. Could anyone help me with this?

> counts_GC <- addGCBias(counts, genome = BSgenome.Hsapiens.UCSC.hg19)
Error in loadFUN(x, seqname, ranges) : 
  trying to load regions beyond the boundaries of non-circular sequence "chr17"

> sessionInfo()

R version 3.5.0 (2018-04-23)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS High Sierra 10.13.4

Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

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

other attached packages:
 [1] BiocInstaller_1.30.0              BSgenome.Hsapiens.UCSC.hg19_1.4.0
 [3] BSgenome_1.48.0                   rtracklayer_1.40.2               
 [5] Biostrings_2.48.0                 XVector_0.20.0                   
 [7] SummarizedExperiment_1.10.1       DelayedArray_0.6.0               
 [9] BiocParallel_1.14.1               matrixStats_0.53.1               
[11] Biobase_2.40.0                    GenomicRanges_1.32.3             
[13] GenomeInfoDb_1.16.0               IRanges_2.14.10                  
[15] S4Vectors_0.18.2                  BiocGenerics_0.26.0              
[17] Matrix_1.2-14                     motifmatchr_1.2.0                
[19] chromVAR_1.2.0                   

loaded via a namespace (and not attached):
 [1] bitops_1.0-6                DirichletMultinomial_1.22.0 TFBSTools_1.18.0           
 [4] bit64_0.9-7                 httr_1.3.1                  tools_3.5.0                
 [7] R6_2.2.2                    DT_0.4                      seqLogo_1.46.0             
[10] DBI_1.0.0                   lazyeval_0.2.1              colorspace_1.3-2           
[13] tidyselect_0.2.4            bit_1.1-14                  compiler_3.5.0             
[16] plotly_4.7.1                caTools_1.17.1              scales_0.5.0               
[19] readr_1.1.1                 stringr_1.3.1               digest_0.6.15              
[22] Rsamtools_1.32.0            R.utils_2.6.0               pkgconfig_2.0.1            
[25] htmltools_0.3.6             htmlwidgets_1.2             rlang_0.2.1                
[28] RSQLite_2.1.1               VGAM_1.0-5                  shiny_1.1.0                
[31] bindr_0.1.1                 jsonlite_1.5                gtools_3.5.0               
[34] dplyr_0.7.5                 R.oo_1.22.0                 RCurl_1.95-4.10            
[37] magrittr_1.5                GO.db_3.6.0                 GenomeInfoDbData_1.1.0     
[40] Rcpp_0.12.17                munsell_0.4.3               R.methodsS3_1.7.1          
[43] stringi_1.2.2               zlibbioc_1.26.0             plyr_1.8.4                 
[46] grid_3.5.0                  blob_1.1.1                  promises_1.0.1             
[49] miniUI_0.1.1.1              CNEr_1.16.0                 lattice_0.20-35            
[52] splines_3.5.0               annotate_1.58.0             hms_0.4.2                  
[55] KEGGREST_1.20.0             pillar_1.2.3                reshape2_1.4.3             
[58] TFMPvalue_0.0.8             XML_3.98-1.11               glue_1.2.0                 
[61] data.table_1.11.4           png_0.1-7                   httpuv_1.4.3               
[64] tidyr_0.8.1                 gtable_0.2.0                poweRlaw_0.70.1            
[67] purrr_0.2.5                 assertthat_0.2.0            ggplot2_2.2.1              
[70] mime_0.5                    xtable_1.8-2                later_0.7.2                
[73] viridisLite_0.3.0           tibble_1.4.2                GenomicAlignments_1.16.0   
[76] AnnotationDbi_1.42.1        memoise_1.1.0               bindrcpp_0.2.2  
bsgenome • 1.7k views
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@james-w-macdonald-5106
Last seen 1 day ago
United States

The error seems to imply that your counts object contains sequences that are off the end of chr17. This could either be due to a mismatch between genomes (most of the chromosomes in hg19 are longer than in hg38, with the notable exceptions of chrs17, 18, and 20, which are all much longer:

(seqlengths(seqinfo(BSgenome.Hsapiens.UCSC.hg38)[paste0("chr", 1:22),]) - seqlengths(seqinfo(BSgenome.Hsapiens.UCSC.hg19)[paste0("chr", 1:22),]))/1e6
     chr1      chr2      chr3      chr4      chr5      chr6      chr7      chr8
-0.294199 -1.005844  0.273129 -0.939721  0.622999 -0.309088  0.207310 -1.225386
     chr9     chr10     chr11     chr12     chr13     chr14     chr15     chr16
-2.818714 -1.737325  0.080106 -0.576586 -0.805550 -0.305822 -0.540203 -0.016408
    chr17     chr18     chr19     chr20     chr21     chr22
 2.062231  2.296037 -0.511367  1.418647 -1.419912 -0.486098

So if  your data are based on hg38, but you are using hg19 to generate GC bias, chr17 is a likely one to give you a problem.

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I tried hg38 as well but it run into the same error with chr1.

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Well, you should check the rowRanges slot of your counts object, particularly for chr17, to see if there are sequences that are > 81195210. If so, you will need to figure out why.

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Thank you very much. I am a biologist but not a bioinformatician so I kind of have no clues about your suggestion. But thanks anyway.

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I looked at rowRanges slot of my counts objects and I didn't know where to find the information you mentioned. Here is an example of data I got from rowRanges.

seqnames start end width strand score qval
chr1 10358 10857 500 * 82 8.28541
chr1 11151 11650 500 * 53 5.3528
chr1 29063 29562 500 * 150 15.03696
chr1 32325 32824 500 * 244 24.4669
chr1 114730 115229 500 * 53 5.3528
chr1 236358 236857 500 * 32 3.22398
chr1 237521 238020 500 * 50 5.09896
chr1 348127 348626 500 * 95 9.54581

 

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Using the example data from chromVAR:

> rowRanges(example_counts)[seqnames(rowRanges(example_counts)) == "chr17",]
GRanges object with 1414 ranges and 3 metadata columns:
         seqnames            ranges strand |     score      qval           name
            <Rle>         <IRanges>  <Rle> | <integer> <numeric>    <character>
     [1]    chr17       65281-65780      * |        98   9.85266  H1_peak_21646
     [2]    chr17     259705-260204      * |       529  52.96062  GM_peak_32813
     [3]    chr17     266470-266969      * |       140  14.03065 GM_peak_32814b
     [4]    chr17     617827-618326      * |       115  11.54114  H1_peak_21654
     [5]    chr17     635078-635577      * |       150  15.05708  H1_peak_21657
     ...      ...               ...    ... .       ...       ...            ...
  [1410]    chr17 80710122-80710621      * |        82   8.21494  H1_peak_24325
  [1411]    chr17 80817847-80818346      * |       278  27.80532  GM_peak_36405
  [1412]    chr17 80821752-80822251      * |       509  50.93502  GM_peak_36407
  [1413]    chr17 80828979-80829478      * |       156  15.65456  GM_peak_36409
  [1414]    chr17 81009554-81010053      * |       869  86.91984 GM_peak_36410b
  -------
  seqinfo: 23 sequences from an unspecified genome

 

 

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Yes. I saw the last line with sequences > 81195210

 

chr17 81194811 81195310 500 * 148 14.89645
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OK, so that's a problem. You need to figure out why you have counts from a region of chr17 that don't exist in the hg19 genome that you said you used. This isn't a Bioconductor question, really, as the software is doing what it is supposed to do. You just have data that don't fulfill expectations, so you need to run that down.

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OK. Thanks for your help.

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Or more directly

(seqlengths(BSgenome.Hsapiens.UCSC.hg38)[1:22] - seqlengths(BSgenome.Hsapiens.UCSC.hg19)[1:22])/1e6
     chr1      chr2      chr3      chr4      chr5      chr6      chr7      chr8
-0.294199 -1.005844  0.273129 -0.939721  0.622999 -0.309088  0.207310 -1.225386
     chr9     chr10     chr11     chr12     chr13     chr14     chr15     chr16
-2.818714 -1.737325  0.080106 -0.576586 -0.805550 -0.305822 -0.540203 -0.016408
    chr17     chr18     chr19     chr20     chr21     chr22
 2.062231  2.296037 -0.511367  1.418647 -1.419912 -0.486098
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counts_GC <- addGCBias(counts, genome = BSgenome.Hsapiens.UCSC.hg38)
Error in loadFUN(x, seqname, ranges) : 
  trying to load regions beyond the boundaries of non-circular sequence "chr1"

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I checked my alignment and I used hg19 when I run bowtie.

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