1 Introduction

This package contains several ChIP-seq datasets for use in differential binding (DB) analyses:

  • H3K9ac and H3K4me3 ChIP-seq in murine pro-B and mature B cells (Revilla-I-Domingo et al. 2012)
  • CREB binding protein (CBP) ChIP-seq in wild-type and CBP-knockout mouse embryonic fibroblasts (Kasper et al. 2014)
  • Nuclear transcription factor subunit gamma alpha (NF-YA) ChIP-seq in mouse terminal neurons and embryonic stem cells (Tiwari et al. 2011)
  • H3K27me3 ChIP-seq in mouse wild-type and Ezh2-knockout lung epithelium (Galvis et al. 2015)

These datasets are mainly used in the chipseqDB workflow (Lun and Smyth 2015) and the csaw user’s guide (Lun and Smyth 2016). This vignette will briefly demonstrate how to obtain each dataset and investigate some of the processing statistics.

2 Obtaining each dataset

We obtain the H3K9ac dataset from ExperimentHub using the H3K9acData() function. This downloads sorted and indexed BAM files to a local cache, along with the associated index files. The function returns a DataFrame of file paths and sample descriptions to further use in workflows.

library(chipseqDBData)
h3k9ac.paths <- H3K9acData()
h3k9ac.paths
## DataFrame with 4 rows and 3 columns
##                  Name            Description      Path
##           <character>            <character>    <List>
## 1    h3k9ac-proB-8113    pro-B H3K9ac (8113) <BamFile>
## 2    h3k9ac-proB-8108    pro-B H3K9ac (8108) <BamFile>
## 3 h3k9ac-matureB-8059 mature B H3K9ac (8059) <BamFile>
## 4 h3k9ac-matureB-8086 mature B H3K9ac (8086) <BamFile>

Note that the time-consuming download only occurs upon the first use of the function. Later uses will simply re-use the same files, thus avoiding the need to re-download these large files. (Some readers may notice that the paths point to the temporary directory, which is destroyed at the end of each R session. Here, the temporary directory contains only soft-links to the persistent BAM files in the local cache. This is a low-cost illusion to ensure that the index files have the same prefixes as the BAM files.)

The same approach is used for all of the other datasets, e.g., CBPData(), NFYAData(). Be aware that the initial download time will depend on the size and number of the BAM files in each dataset.

3 Investigating mapping statistics

We use functions from the Rsamtools package to examine the mapping statistics. This includes the number of mapped reads, the number of marked reads (i.e., potential PCR duplicates) and the number of high-quality alignments with high mapping scores.

library(Rsamtools)
diagnostics <- list()
for (i in seq_len(nrow(h3k9ac.paths))) {
    stats <- scanBam(h3k9ac.paths$Path[[i]], 
        param=ScanBamParam(what=c("mapq", "flag")))
    flag <- stats[[1]]$flag
    mapq <- stats[[1]]$mapq

    mapped <- bitwAnd(flag, 0x4)==0
    diagnostics[[h3k9ac.paths$Name[i]]] <- c(
        Total=length(flag), 
        Mapped=sum(mapped),
        HighQual=sum(mapq >= 10 & mapped),
        DupMarked=sum(bitwAnd(flag, 0x400)!=0)
    )
}
diag.stats <- data.frame(do.call(rbind, diagnostics))
diag.stats$Prop.mapped <- diag.stats$Mapped/diag.stats$Total*100
diag.stats$Prop.marked <- diag.stats$DupMarked/diag.stats$Mapped*100
diag.stats
##                        Total  Mapped HighQual DupMarked Prop.mapped Prop.marked
## h3k9ac-proB-8113    10724526 8832006  8815503    434884    82.35335    4.923955
## h3k9ac-proB-8108    10413135 7793913  7786335    252271    74.84694    3.236770
## h3k9ac-matureB-8059 16675372 4670364  4568908    396785    28.00756    8.495805
## h3k9ac-matureB-8086  6347683 4551692  4535587    141583    71.70635    3.110558

More comprehensive quality checks are beyond the scope of this document, but can be performed with other packages such as ChIPQC.

4 Session information

sessionInfo()
## R Under development (unstable) (2021-01-19 r79848)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.2 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.13-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.13-bioc/R/lib/libRlapack.so
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=C              
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats4    parallel  stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] Rsamtools_2.7.1      Biostrings_2.59.2    XVector_0.31.1      
##  [4] GenomicRanges_1.43.3 GenomeInfoDb_1.27.5  IRanges_2.25.6      
##  [7] S4Vectors_0.29.7     BiocGenerics_0.37.1  chipseqDBData_1.7.0 
## [10] knitr_1.31           BiocStyle_2.19.1    
## 
## loaded via a namespace (and not attached):
##  [1] Rcpp_1.0.6                    assertthat_0.2.1             
##  [3] digest_0.6.27                 mime_0.9                     
##  [5] BiocFileCache_1.15.1          R6_2.5.0                     
##  [7] RSQLite_2.2.3                 evaluate_0.14                
##  [9] httr_1.4.2                    pillar_1.4.7                 
## [11] zlibbioc_1.37.0               rlang_0.4.10                 
## [13] curl_4.3                      blob_1.2.1                   
## [15] rmarkdown_2.6                 BiocParallel_1.25.3          
## [17] AnnotationHub_2.23.1          stringr_1.4.0                
## [19] RCurl_1.98-1.2                bit_4.0.4                    
## [21] shiny_1.6.0                   compiler_4.1.0               
## [23] httpuv_1.5.5                  xfun_0.20                    
## [25] pkgconfig_2.0.3               htmltools_0.5.1.1            
## [27] tidyselect_1.1.0              tibble_3.0.6                 
## [29] GenomeInfoDbData_1.2.4        interactiveDisplayBase_1.29.0
## [31] bookdown_0.21                 withr_2.4.1                  
## [33] crayon_1.4.0                  dplyr_1.0.4                  
## [35] dbplyr_2.0.0                  later_1.1.0.1                
## [37] bitops_1.0-6                  rappdirs_0.3.3               
## [39] xtable_1.8-4                  lifecycle_0.2.0              
## [41] DBI_1.1.1                     magrittr_2.0.1               
## [43] stringi_1.5.3                 cachem_1.0.2                 
## [45] promises_1.1.1                ellipsis_0.3.1               
## [47] filelock_1.0.2                generics_0.1.0               
## [49] vctrs_0.3.6                   tools_4.1.0                  
## [51] bit64_4.0.5                   Biobase_2.51.0               
## [53] glue_1.4.2                    purrr_0.3.4                  
## [55] BiocVersion_3.13.1            fastmap_1.1.0                
## [57] yaml_2.2.1                    AnnotationDbi_1.53.0         
## [59] BiocManager_1.30.10           ExperimentHub_1.17.0         
## [61] memoise_2.0.0

References

Galvis, L. A., A. Z. Holik, K. M. Short, J. Pasquet, A. T. Lun, M. E. Blewitt, I. M. Smyth, M. E. Ritchie, and M. L. Asselin-Labat. 2015. “Repression of Igf1 expression by Ezh2 prevents basal cell differentiation in the developing lung.” Development 142 (8): 1458–69.

Kasper, L. H., C. Qu, J. C. Obenauer, D. J. McGoldrick, and P. K. Brindle. 2014. “Genome-wide and single-cell analyses reveal a context dependent relationship between CBP recruitment and gene expression.” Nucleic Acids Res. 42 (18): 11363–82.

Lun, A. T., and G. K. Smyth. 2015. “From reads to regions: a Bioconductor workflow to detect differential binding in ChIP-seq data.” F1000Res. 4: 1080.

———. 2016. “csaw: a Bioconductor package for differential binding analysis of ChIP-seq data using sliding windows.” Nucleic Acids Res. 44 (5): e45.

Revilla-I-Domingo, R., I. Bilic, B. Vilagos, H. Tagoh, A. Ebert, I. M. Tamir, L. Smeenk, et al. 2012. “The B-cell identity factor Pax5 regulates distinct transcriptional programmes in early and late B lymphopoiesis.” EMBO J. 31 (14): 3130–46.

Tiwari, V. K., M. B. Stadler, C. Wirbelauer, R. Paro, D. Schubeler, and C. Beisel. 2011. “A chromatin-modifying function of JNK during stem cell differentiation.” Nat. Genet. 44 (1): 94–100.