Hi,

I was wondering if anyone would be able to help. I was using the previous version of DiffBind v3.0.7 to successfully analyse ChIP-Rx (ChIP-seq with a 'spike in' genome) with the same sample sheet included below (minus the spikeIn column). However, when I saw that the new version (v3.0.7) included the potential to apply spike in normalisation, I tried to redo the analysis using the spike in normalisation methods included in the package but, could not get the dba.count() function to count the reads in my samples (code and error attached below). I have tried using the bUseSummarizeOverlaps=TRUE argument that was suggested as a fix in previous forum posts I came across but, this did not help fix the issue either. I have also tried my old sample sheet which worked with the previous version and I have tried my saved dba objects where the counts and analysis were already performed and they worked fine. I did double check my BAM files and try again with a copy of the BAM files, incase there was any corruption causing the error but, looking on SAMtools they all seem fine.

How would I go about fixing this error? Thank you in advance for the help.

After running the code below I received the following errors:

> m_k4_peaks<-dba(sampleSheet="/home/i/imb10/Desktop/k4_2.csv")
1_LSD1_D0_K4 Mouse H3K4Me2 ESC WT 1 raw
2_LSD1_D0_K4-R2 Mouse H3K4Me2 ESC WT 2 raw
5_LSD1_D2_K4 Mouse H3K4Me2 ESC WT 1 raw
6_LSD1_D2_K4-R2 Mouse H3K4Me2 ESC WT 2 raw
9_OHT_D0_K4 Mouse H3K4Me2 ESC KO 1 raw
10_OHT_D0_K4-R2 Mouse H3K4Me2 ESC KO 2 raw
13_OHT_D2_K4 Mouse H3K4Me2 ESC KO 1 raw
14_OHT_D2_K4-R2 Mouse H3K4Me2 ESC KO 2 raw
> m_k4_peaks <- dba.count(m_k4_peaks)
Computing summits...
Re-centering peaks...
Reads will be counted as Paired-end.
Error: Error processing one or more read files. Check warnings().
In addition: Warning messages:
1: In mclapply(arglist, fn, ..., mc.preschedule = TRUE, mc.allow.recursive = TRUE) :
  scheduled cores 2, 3, 4, 7 did not deliver results, all values of the jobs will be affected
2:  
3:  
4:  
5:

The sample sheet:

SampleID,Tissue,Factor,Condition,Treatment,Replicate,bamReads,Peaks ,Peak Caller,SpikeIn 1_LSD1_D0_K4,Mouse,H3K4Me2,ESC,WT,1,/data/ngmeta/India/New_Workspace/10_dedup_mm10/LSD1_D0_K4_trimmed-mm10-dm6_filtered2_mm10_dedup.bam,/data/ngmeta/India/New_Workspace/16_peak_calling/1_LSD1_D0_K4_peaks.xls,macs,/data/ngmeta/India/New_Workspace/11_dedup_dm6/LSD1_D0_K4_trimmed-mm10-dm6_filtered2_dm6_dedup.bam 2_LSD1_D0_K4-R2,Mouse,H3K4Me2,ESC,WT,2,/data/ngmeta/India/New_Workspace/10_dedup_mm10/LSD1_D0_K4-R2_trimmed-mm10-dm6_filtered2_mm10_dedup.bam,/data/ngmeta/India/New_Workspace/16_peak_calling/2_LSD1_D0_K4-R2_peaks.xls,macs,/data/ngmeta/India/New_Workspace/11_dedup_dm6/LSD1_D0_K4-R2_trimmed-mm10-dm6_filtered2_dm6_dedup.bam 5_LSD1_D2_K4,Mouse,H3K4Me2,ESC,WT,1,/data/ngmeta/India/New_Workspace/10_dedup_mm10/LSD1_D2_K4_trimmed-mm10-dm6_filtered2_mm10_dedup.bam,/data/ngmeta/India/New_Workspace/16_peak_calling/5_LSD1_D2_K4_peaks.xls,macs,/data/ngmeta/India/New_Workspace/11_dedup_dm6/LSD1_D2_K4_trimmed-mm10-dm6_filtered2_dm6_dedup.bam 6_LSD1_D2_K4-R2,Mouse,H3K4Me2,ESC,WT,2,/data/ngmeta/India/New_Workspace/10_dedup_mm10/LSD1_D2_K4-R2_trimmed-mm10-dm6_filtered2_mm10_dedup.bam,/data/ngmeta/India/New_Workspace/16_peak_calling/6_LSD1_D2_K4-R2_peaks.xls,macs,/data/ngmeta/India/New_Workspace/11_dedup_dm6/LSD1_D2_K4-R2_trimmed-mm10-dm6_filtered2_dm6_dedup.bam 9_OHT_D0_K4,Mouse,H3K4Me2,ESC,KO,1,/data/ngmeta/India/New_Workspace/10_dedup_mm10/OHT_D0_K4_trimmed-mm10-dm6_filtered2_mm10_dedup.bam,/data/ngmeta/India/New_Workspace/16_peak_calling/9_OHT_D0_K4_peaks.xls,macs,/data/ngmeta/India/New_Workspace/11_dedup_dm6/OHT_D0_K4_trimmed-mm10-dm6_filtered2_dm6_dedup.bam 10_OHT_D0_K4-R2,Mouse,H3K4Me2,ESC,KO,2,/data/ngmeta/India/New_Workspace/10_dedup_mm10/OHT_D0_K4-R2_trimmed-mm10-dm6_filtered2_mm10_dedup.bam,/data/ngmeta/India/New_Workspace/16_peak_calling/10_OHT_D0_K4-R2_peaks.xls,macs,/data/ngmeta/India/New_Workspace/11_dedup_dm6/OHT_D0_K4-R2_trimmed-mm10-dm6_filtered2_dm6_dedup.bam 13_OHT_D2_K4,Mouse,H3K4Me2,ESC,KO,1,/data/ngmeta/India/New_Workspace/10_dedup_mm10/OHT_D2_K4_trimmed-mm10-dm6_filtered2_mm10_dedup.bam,/data/ngmeta/India/New_Workspace/16_peak_calling/13_OHT_D2_K4_peaks.xls,macs,/data/ngmeta/India/New_Workspace/11_dedup_dm6/OHT_D2_K4_trimmed-mm10-dm6_filtered2_dm6_dedup.bam 14_OHT_D2_K4-R2,Mouse,H3K4Me2,ESC,KO,2,/data/ngmeta/India/New_Workspace/10_dedup_mm10/OHT_D2_K4-R2_trimmed-mm10-dm6_filtered2_mm10_dedup.bam,/data/ngmeta/India/New_Workspace/16_peak_calling/14_OHT_D2_K4-R2_peaks.xls,macs,/data/ngmeta/India/New_Workspace/11_dedup_dm6/OHT_D2_K4-R2_trimmed-mm10-dm6_filtered2_dm6_dedup.bam

>sessionInfo()

R version 4.0.0 (2020-04-24)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS:   /cm/shared/apps/R/4.0.0/lib64/R/lib/libRblas.so
LAPACK: /cm/shared/apps/R/4.0.0/lib64/R/lib/libRlapack.so

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

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

other attached packages:
 [1] DiffBind_3.0.7              SummarizedExperiment_1.20.0
 [3] Biobase_2.50.0              MatrixGenerics_1.2.0       
 [5] matrixStats_0.57.0          GenomicRanges_1.42.0       
 [7] GenomeInfoDb_1.26.1         IRanges_2.24.0             
 [9] S4Vectors_0.28.0            BiocGenerics_0.36.0        

loaded via a namespace (and not attached):
  [1] amap_0.8-18              colorspace_2.0-0         rjson_0.2.20            
  [4] hwriter_1.3.2            ellipsis_0.3.1           XVector_0.30.0          
  [7] ggrepel_0.8.2            bit64_4.0.5              mvtnorm_1.1-1           
 [10] apeglm_1.12.0            AnnotationDbi_1.52.0     xml2_1.3.2              
 [13] splines_4.0.0            jsonlite_1.7.1           Rsamtools_2.6.0         
 [16] annotate_1.68.0          ashr_2.2-47              GO.db_3.12.1            
 [19] dbplyr_2.0.0             png_0.1-7                GreyListChIP_1.22.0     
 [22] pheatmap_1.0.12          graph_1.68.0             compiler_4.0.0          
 [25] httr_1.4.2               GOstats_2.56.0           backports_1.2.0         
 [28] assertthat_0.2.1         Matrix_1.2-18            limma_3.46.0            
 [31] prettyunits_1.1.1        tools_4.0.0              coda_0.19-4             
 [34] gtable_0.3.0             glue_1.4.2               GenomeInfoDbData_1.2.4  
 [37] Category_2.56.0          systemPipeR_1.24.2       dplyr_1.0.2             
 [40] rsvg_2.1                 batchtools_0.9.14        rappdirs_0.3.1          
 [43] V8_3.4.0                 ShortRead_1.48.0         Rcpp_1.0.5              
 [46] bbmle_1.0.23.1           vctrs_0.3.5              Biostrings_2.58.0       
 [49] rtracklayer_1.50.0       stringr_1.4.0            irlba_2.3.3             
 [52] lifecycle_0.2.0          gtools_3.8.2             XML_3.99-0.5            
 [55] edgeR_3.32.0             MASS_7.3-51.6            zlibbioc_1.36.0         
 [58] scales_1.1.1             BSgenome_1.58.0          VariantAnnotation_1.36.0
 [61] hms_0.5.3                RBGL_1.66.0              RColorBrewer_1.1-2      
 [64] yaml_2.2.1               curl_4.3                 memoise_1.1.0           
 [67] ggplot2_3.3.2            emdbook_1.3.12           bdsmatrix_1.3-4         
 [70] biomaRt_2.46.0           SQUAREM_2020.5           latticeExtra_0.6-29     
 [73] stringi_1.5.3            RSQLite_2.2.1            genefilter_1.72.0       
 [76] checkmate_2.0.0          GenomicFeatures_1.42.1   caTools_1.18.0          
 [79] BiocParallel_1.24.1      DOT_0.1                  truncnorm_1.0-8         
 [82] rlang_0.4.9              pkgconfig_2.0.3          bitops_1.0-6            
 [85] invgamma_1.1             lattice_0.20-41          purrr_0.3.4             
 [88] GenomicAlignments_1.26.0 bit_4.0.4                tidyselect_1.1.0        
 [91] GSEABase_1.52.0          AnnotationForge_1.32.0   plyr_1.8.6              
 [94] magrittr_2.0.1           R6_2.5.0                 gplots_3.1.1            
 [97] generics_0.1.0           base64url_1.4            DelayedArray_0.16.0     
[100] DBI_1.1.0                pillar_1.4.7             withr_2.3.0             
[103] mixsqp_0.3-43            survival_3.1-12          RCurl_1.98-1.2          
[106] tibble_3.0.4             crayon_1.3.4             KernSmooth_2.23-17      
[109] BiocFileCache_1.14.0     jpeg_0.1-8.1             progress_1.2.2          
[112] locfit_1.5-9.4           grid_4.0.0               data.table_1.13.2       
[115] blob_1.2.1               Rgraphviz_2.34.0         digest_0.6.27           
[118] xtable_1.8-4             numDeriv_2016.8-1.1      brew_1.0-6              
[121] openssl_1.4.3            munsell_0.5.0            askpass_1.1

Try running dba.count()with bParallel=FALSE, this will process each bam file in turn and report any errors directly. bUseSummarizeOverlaps=TRUE is the default now, so you could try bUseSummarizeOverlaps=FALSE to use the internal counting code.

I also notice that you are running R version 4.0.0, I believe you should be in R version 4.0.3 to ensure you have the current version of all the packages.