With the improvement of sequencing techniques, chromatin immunoprecipitation followed by high throughput sequencing (ChIP-Seq) is getting popular to study genome-wide protein-DNA interactions. To address the lack of powerful ChIP-Seq analysis method, we presented the Model-based Analysis of ChIP-Seq (MACS), for identifying transcript factor binding sites. MACS captures the influence of genome complexity to evaluate the significance of enriched ChIP regions and MACS improves the spatial resolution of binding sites through combining the information of both sequencing tag position and orientation. MACS can be easily used for ChIP-Seq data alone, or with a control sample with the increase of specificity. Moreover, as a general peak-caller, MACS can also be applied to any “DNA enrichment assays” if the question to be asked is simply: where we can find significant reads coverage than the random background.
This package is a wrapper of the MACS toolkit based on basilisk.
The package is built on basilisk. The dependent python library macs3 will be installed automatically inside its conda environment.
library(MACSr)There are 13 functions imported from MACS3. Details of each function can be checked from its manual.
| Functions | Description |
|---|---|
callpeak |
Main MACS3 Function to call peaks from alignment results. |
bdgpeakcall |
Call peaks from bedGraph output. |
bdgbroadcall |
Call broad peaks from bedGraph output. |
bdgcmp |
Comparing two signal tracks in bedGraph format. |
bdgopt |
Operate the score column of bedGraph file. |
cmbreps |
Combine BEDGraphs of scores from replicates. |
bdgdiff |
Differential peak detection based on paired four bedGraph files. |
filterdup |
Remove duplicate reads, then save in BED/BEDPE format. |
predictd |
Predict d or fragment size from alignment results. |
pileup |
Pileup aligned reads (single-end) or fragments (paired-end) |
randsample |
Randomly choose a number/percentage of total reads. |
refinepeak |
Take raw reads alignment, refine peak summits. |
callvar |
Call variants in given peak regions from the alignment BAM files. |
callpeakWe have uploaded multipe test datasets from MACS to a data package
MACSdata in the ExperimentHub. For example, Here we download a
pair of single-end bed files to run the callpeak function.
eh <- ExperimentHub::ExperimentHub()
#> snapshotDate(): 2022-10-24
eh <- AnnotationHub::query(eh, "MACSdata")
CHIP <- eh[["EH4558"]]
#> see ?MACSdata and browseVignettes('MACSdata') for documentation
#> loading from cache
CTRL <- eh[["EH4563"]]
#> see ?MACSdata and browseVignettes('MACSdata') for documentation
#> loading from cacheHere is an example to call narrow and broad peaks on the SE bed files.
cp1 <- callpeak(CHIP, CTRL, gsize = 5.2e7, store_bdg = TRUE,
name = "run_callpeak_narrow0", outdir = tempdir(),
cutoff_analysis = TRUE)
#> + '/home/biocbuild/.cache/R/basilisk/1.10.0/0/bin/conda' 'create' '--yes' '--prefix' '/home/biocbuild/.cache/R/basilisk/1.10.0/MACSr/1.6.0/env_macs' 'python=3.10' '--quiet' '-c' 'conda-forge'
#> + '/home/biocbuild/.cache/R/basilisk/1.10.0/0/bin/conda' 'install' '--yes' '--prefix' '/home/biocbuild/.cache/R/basilisk/1.10.0/MACSr/1.6.0/env_macs' 'python=3.10'
#> + '/home/biocbuild/.cache/R/basilisk/1.10.0/0/bin/conda' 'install' '--yes' '--prefix' '/home/biocbuild/.cache/R/basilisk/1.10.0/MACSr/1.6.0/env_macs' '-c' 'conda-forge' 'python=3.10' 'python=3.10'
#> INFO @ Tue, 01 Nov 2022 17:55:35:
#> # Command line:
#> # ARGUMENTS LIST:
#> # name = run_callpeak_narrow0
#> # format = AUTO
#> # ChIP-seq file = ['/home/biocbuild/.cache/R/ExperimentHub/3d73832332a552_4601']
#> # control file = ['/home/biocbuild/.cache/R/ExperimentHub/3d73831c5d77a3_4606']
#> # effective genome size = 5.20e+07
#> # band width = 300
#> # model fold = [5.0, 50.0]
#> # qvalue cutoff = 5.00e-02
#> # The maximum gap between significant sites is assigned as the read length/tag size.
#> # The minimum length of peaks is assigned as the predicted fragment length "d".
#> # Larger dataset will be scaled towards smaller dataset.
#> # Range for calculating regional lambda is: 1000 bps and 10000 bps
#> # Broad region calling is off
#> # Additional cutoff on fold-enrichment is: 0.10
#> # Paired-End mode is off
#>
#> INFO @ Tue, 01 Nov 2022 17:55:35: #1 read tag files...
#> INFO @ Tue, 01 Nov 2022 17:55:35: #1 read treatment tags...
#> INFO @ Tue, 01 Nov 2022 17:55:35: Detected format is: BED
#> INFO @ Tue, 01 Nov 2022 17:55:35: * Input file is gzipped.
#> INFO @ Tue, 01 Nov 2022 17:55:35: #1.2 read input tags...
#> INFO @ Tue, 01 Nov 2022 17:55:35: Detected format is: BED
#> INFO @ Tue, 01 Nov 2022 17:55:35: * Input file is gzipped.
#> INFO @ Tue, 01 Nov 2022 17:55:35: #1 tag size is determined as 101 bps
#> INFO @ Tue, 01 Nov 2022 17:55:35: #1 tag size = 101.0
#> INFO @ Tue, 01 Nov 2022 17:55:35: #1 total tags in treatment: 49622
#> INFO @ Tue, 01 Nov 2022 17:55:35: #1 user defined the maximum tags...
#> INFO @ Tue, 01 Nov 2022 17:55:35: #1 filter out redundant tags at the same location and the same strand by allowing at most 1 tag(s)
#> INFO @ Tue, 01 Nov 2022 17:55:35: #1 tags after filtering in treatment: 48047
#> INFO @ Tue, 01 Nov 2022 17:55:35: #1 Redundant rate of treatment: 0.03
#> INFO @ Tue, 01 Nov 2022 17:55:35: #1 total tags in control: 50837
#> INFO @ Tue, 01 Nov 2022 17:55:35: #1 user defined the maximum tags...
#> INFO @ Tue, 01 Nov 2022 17:55:35: #1 filter out redundant tags at the same location and the same strand by allowing at most 1 tag(s)
#> INFO @ Tue, 01 Nov 2022 17:55:35: #1 tags after filtering in control: 50783
#> INFO @ Tue, 01 Nov 2022 17:55:35: #1 Redundant rate of control: 0.00
#> INFO @ Tue, 01 Nov 2022 17:55:35: #1 finished!
#> INFO @ Tue, 01 Nov 2022 17:55:35: #2 Build Peak Model...
#> INFO @ Tue, 01 Nov 2022 17:55:35: #2 looking for paired plus/minus strand peaks...
#> INFO @ Tue, 01 Nov 2022 17:55:35: #2 Total number of paired peaks: 469
#> INFO @ Tue, 01 Nov 2022 17:55:35: #2 Model building with cross-correlation: Done
#> INFO @ Tue, 01 Nov 2022 17:55:35: #2 finished!
#> INFO @ Tue, 01 Nov 2022 17:55:35: #2 predicted fragment length is 228 bps
#> INFO @ Tue, 01 Nov 2022 17:55:35: #2 alternative fragment length(s) may be 228 bps
#> INFO @ Tue, 01 Nov 2022 17:55:35: #2.2 Generate R script for model : /tmp/RtmpcKwIto/run_callpeak_narrow0_model.r
#> INFO @ Tue, 01 Nov 2022 17:55:35: #3 Call peaks...
#> INFO @ Tue, 01 Nov 2022 17:55:35: #3 Pre-compute pvalue-qvalue table...
#> INFO @ Tue, 01 Nov 2022 17:55:35: #3 Cutoff vs peaks called will be analyzed!
#> INFO @ Tue, 01 Nov 2022 17:55:35: #3 Analysis of cutoff vs num of peaks or total length has been saved in b'/tmp/RtmpcKwIto/run_callpeak_narrow0_cutoff_analysis.txt'
#> INFO @ Tue, 01 Nov 2022 17:55:35: #3 In the peak calling step, the following will be performed simultaneously:
#> INFO @ Tue, 01 Nov 2022 17:55:35: #3 Write bedGraph files for treatment pileup (after scaling if necessary)... run_callpeak_narrow0_treat_pileup.bdg
#> INFO @ Tue, 01 Nov 2022 17:55:35: #3 Write bedGraph files for control lambda (after scaling if necessary)... run_callpeak_narrow0_control_lambda.bdg
#> INFO @ Tue, 01 Nov 2022 17:55:35: #3 Pileup will be based on sequencing depth in treatment.
#> INFO @ Tue, 01 Nov 2022 17:55:35: #3 Call peaks for each chromosome...
#> INFO @ Tue, 01 Nov 2022 17:55:36: #4 Write output xls file... /tmp/RtmpcKwIto/run_callpeak_narrow0_peaks.xls
#> INFO @ Tue, 01 Nov 2022 17:55:36: #4 Write peak in narrowPeak format file... /tmp/RtmpcKwIto/run_callpeak_narrow0_peaks.narrowPeak
#> INFO @ Tue, 01 Nov 2022 17:55:36: #4 Write summits bed file... /tmp/RtmpcKwIto/run_callpeak_narrow0_summits.bed
#> INFO @ Tue, 01 Nov 2022 17:55:36: Done!
cp2 <- callpeak(CHIP, CTRL, gsize = 5.2e7, store_bdg = TRUE,
name = "run_callpeak_broad", outdir = tempdir(),
broad = TRUE)
#> INFO @ Tue, 01 Nov 2022 17:55:36:
#> # Command line:
#> # ARGUMENTS LIST:
#> # name = run_callpeak_broad
#> # format = AUTO
#> # ChIP-seq file = ['/home/biocbuild/.cache/R/ExperimentHub/3d73832332a552_4601']
#> # control file = ['/home/biocbuild/.cache/R/ExperimentHub/3d73831c5d77a3_4606']
#> # effective genome size = 5.20e+07
#> # band width = 300
#> # model fold = [5.0, 50.0]
#> # qvalue cutoff for narrow/strong regions = 5.00e-02
#> # qvalue cutoff for broad/weak regions = 1.00e-01
#> # The maximum gap between significant sites is assigned as the read length/tag size.
#> # The minimum length of peaks is assigned as the predicted fragment length "d".
#> # Larger dataset will be scaled towards smaller dataset.
#> # Range for calculating regional lambda is: 1000 bps and 10000 bps
#> # Broad region calling is on
#> # Additional cutoff on fold-enrichment is: 0.10
#> # Paired-End mode is off
#>
#> INFO @ Tue, 01 Nov 2022 17:55:36: #1 read tag files...
#> INFO @ Tue, 01 Nov 2022 17:55:36: #1 read treatment tags...
#> INFO @ Tue, 01 Nov 2022 17:55:36: Detected format is: BED
#> INFO @ Tue, 01 Nov 2022 17:55:36: * Input file is gzipped.
#> INFO @ Tue, 01 Nov 2022 17:55:36: #1.2 read input tags...
#> INFO @ Tue, 01 Nov 2022 17:55:36: Detected format is: BED
#> INFO @ Tue, 01 Nov 2022 17:55:36: * Input file is gzipped.
#> INFO @ Tue, 01 Nov 2022 17:55:36: #1 tag size is determined as 101 bps
#> INFO @ Tue, 01 Nov 2022 17:55:36: #1 tag size = 101.0
#> INFO @ Tue, 01 Nov 2022 17:55:36: #1 total tags in treatment: 49622
#> INFO @ Tue, 01 Nov 2022 17:55:36: #1 user defined the maximum tags...
#> INFO @ Tue, 01 Nov 2022 17:55:36: #1 filter out redundant tags at the same location and the same strand by allowing at most 1 tag(s)
#> INFO @ Tue, 01 Nov 2022 17:55:36: #1 tags after filtering in treatment: 48047
#> INFO @ Tue, 01 Nov 2022 17:55:36: #1 Redundant rate of treatment: 0.03
#> INFO @ Tue, 01 Nov 2022 17:55:36: #1 total tags in control: 50837
#> INFO @ Tue, 01 Nov 2022 17:55:36: #1 user defined the maximum tags...
#> INFO @ Tue, 01 Nov 2022 17:55:36: #1 filter out redundant tags at the same location and the same strand by allowing at most 1 tag(s)
#> INFO @ Tue, 01 Nov 2022 17:55:36: #1 tags after filtering in control: 50783
#> INFO @ Tue, 01 Nov 2022 17:55:36: #1 Redundant rate of control: 0.00
#> INFO @ Tue, 01 Nov 2022 17:55:36: #1 finished!
#> INFO @ Tue, 01 Nov 2022 17:55:36: #2 Build Peak Model...
#> INFO @ Tue, 01 Nov 2022 17:55:36: #2 looking for paired plus/minus strand peaks...
#> INFO @ Tue, 01 Nov 2022 17:55:36: #2 Total number of paired peaks: 469
#> INFO @ Tue, 01 Nov 2022 17:55:36: #2 Model building with cross-correlation: Done
#> INFO @ Tue, 01 Nov 2022 17:55:36: #2 finished!
#> INFO @ Tue, 01 Nov 2022 17:55:36: #2 predicted fragment length is 228 bps
#> INFO @ Tue, 01 Nov 2022 17:55:36: #2 alternative fragment length(s) may be 228 bps
#> INFO @ Tue, 01 Nov 2022 17:55:36: #2.2 Generate R script for model : /tmp/RtmpcKwIto/run_callpeak_broad_model.r
#> INFO @ Tue, 01 Nov 2022 17:55:36: #3 Call peaks...
#> INFO @ Tue, 01 Nov 2022 17:55:36: #3 Call broad peaks with given level1 -log10qvalue cutoff and level2: 1.301030, 1.000000...
#> INFO @ Tue, 01 Nov 2022 17:55:36: #3 Pre-compute pvalue-qvalue table...
#> INFO @ Tue, 01 Nov 2022 17:55:36: #3 In the peak calling step, the following will be performed simultaneously:
#> INFO @ Tue, 01 Nov 2022 17:55:36: #3 Write bedGraph files for treatment pileup (after scaling if necessary)... run_callpeak_broad_treat_pileup.bdg
#> INFO @ Tue, 01 Nov 2022 17:55:36: #3 Write bedGraph files for control lambda (after scaling if necessary)... run_callpeak_broad_control_lambda.bdg
#> INFO @ Tue, 01 Nov 2022 17:55:36: #3 Call peaks for each chromosome...
#> INFO @ Tue, 01 Nov 2022 17:55:36: #4 Write output xls file... /tmp/RtmpcKwIto/run_callpeak_broad_peaks.xls
#> INFO @ Tue, 01 Nov 2022 17:55:36: #4 Write broad peak in broadPeak format file... /tmp/RtmpcKwIto/run_callpeak_broad_peaks.broadPeak
#> INFO @ Tue, 01 Nov 2022 17:55:36: #4 Write broad peak in bed12/gappedPeak format file... /tmp/RtmpcKwIto/run_callpeak_broad_peaks.gappedPeak
#> INFO @ Tue, 01 Nov 2022 17:55:36: Done!Here are the outputs.
cp1
#> macsList class
#> $outputs:
#> /tmp/RtmpcKwIto/run_callpeak_narrow0_control_lambda.bdg
#> /tmp/RtmpcKwIto/run_callpeak_narrow0_cutoff_analysis.txt
#> /tmp/RtmpcKwIto/run_callpeak_narrow0_model.r
#> /tmp/RtmpcKwIto/run_callpeak_narrow0_peaks.narrowPeak
#> /tmp/RtmpcKwIto/run_callpeak_narrow0_peaks.xls
#> /tmp/RtmpcKwIto/run_callpeak_narrow0_summits.bed
#> /tmp/RtmpcKwIto/run_callpeak_narrow0_treat_pileup.bdg
#> $arguments: tfile, cfile, gsize, outdir, name, store_bdg, cutoff_analysis
#> $log:
#> INFO @ Tue, 01 Nov 2022 17:55:35:
#> # Command line:
#> # ARGUMENTS LIST:
#> # name = run_callpeak_narrow0
#> # format = AUTO
#> ...
cp2
#> macsList class
#> $outputs:
#> /tmp/RtmpcKwIto/run_callpeak_broad_control_lambda.bdg
#> /tmp/RtmpcKwIto/run_callpeak_broad_model.r
#> /tmp/RtmpcKwIto/run_callpeak_broad_peaks.broadPeak
#> /tmp/RtmpcKwIto/run_callpeak_broad_peaks.gappedPeak
#> /tmp/RtmpcKwIto/run_callpeak_broad_peaks.xls
#> /tmp/RtmpcKwIto/run_callpeak_broad_treat_pileup.bdg
#> $arguments: tfile, cfile, gsize, outdir, name, store_bdg, broad
#> $log:
#> INFO @ Tue, 01 Nov 2022 17:55:36:
#> # Command line:
#> # ARGUMENTS LIST:
#> # name = run_callpeak_broad
#> # format = AUTO
#> ...macsList classThe macsList is designed to contain everything of an execution,
including function, inputs, outputs and logs, for the purpose of
reproducibility.
For example, we can the function and input arguments.
cp1$arguments
#> [[1]]
#> callpeak
#>
#> $tfile
#> CHIP
#>
#> $cfile
#> CTRL
#>
#> $gsize
#> [1] 5.2e+07
#>
#> $outdir
#> tempdir()
#>
#> $name
#> [1] "run_callpeak_narrow0"
#>
#> $store_bdg
#> [1] TRUE
#>
#> $cutoff_analysis
#> [1] TRUEThe files of all the outputs are collected.
cp1$outputs
#> [1] "/tmp/RtmpcKwIto/run_callpeak_narrow0_control_lambda.bdg"
#> [2] "/tmp/RtmpcKwIto/run_callpeak_narrow0_cutoff_analysis.txt"
#> [3] "/tmp/RtmpcKwIto/run_callpeak_narrow0_model.r"
#> [4] "/tmp/RtmpcKwIto/run_callpeak_narrow0_peaks.narrowPeak"
#> [5] "/tmp/RtmpcKwIto/run_callpeak_narrow0_peaks.xls"
#> [6] "/tmp/RtmpcKwIto/run_callpeak_narrow0_summits.bed"
#> [7] "/tmp/RtmpcKwIto/run_callpeak_narrow0_treat_pileup.bdg"The log is especially important for MACS to check. Detailed
information was given in the log when running.
cat(cp1$log)
#> INFO @ Tue, 01 Nov 2022 17:55:35: # Command line: # ARGUMENTS LIST: # name = run_callpeak_narrow0 # format = AUTO # ChIP-seq file = ['/home/biocbuild/.cache/R/ExperimentHub/3d73832332a552_4601'] # control file = ['/home/biocbuild/.cache/R/ExperimentHub/3d73831c5d77a3_4606'] # effective genome size = 5.20e+07 # band width = 300 # model fold = [5.0, 50.0] # qvalue cutoff = 5.00e-02 # The maximum gap between significant sites is assigned as the read length/tag size. # The minimum length of peaks is assigned as the predicted fragment length "d". # Larger dataset will be scaled towards smaller dataset. # Range for calculating regional lambda is: 1000 bps and 10000 bps # Broad region calling is off # Additional cutoff on fold-enrichment is: 0.10 # Paired-End mode is off INFO @ Tue, 01 Nov 2022 17:55:35: #1 read tag files... INFO @ Tue, 01 Nov 2022 17:55:35: #1 read treatment tags... INFO @ Tue, 01 Nov 2022 17:55:35: Detected format is: BED INFO @ Tue, 01 Nov 2022 17:55:35: * Input file is gzipped. INFO @ Tue, 01 Nov 2022 17:55:35: #1.2 read input tags... INFO @ Tue, 01 Nov 2022 17:55:35: Detected format is: BED INFO @ Tue, 01 Nov 2022 17:55:35: * Input file is gzipped. INFO @ Tue, 01 Nov 2022 17:55:35: #1 tag size is determined as 101 bps INFO @ Tue, 01 Nov 2022 17:55:35: #1 tag size = 101.0 INFO @ Tue, 01 Nov 2022 17:55:35: #1 total tags in treatment: 49622 INFO @ Tue, 01 Nov 2022 17:55:35: #1 user defined the maximum tags... INFO @ Tue, 01 Nov 2022 17:55:35: #1 filter out redundant tags at the same location and the same strand by allowing at most 1 tag(s) INFO @ Tue, 01 Nov 2022 17:55:35: #1 tags after filtering in treatment: 48047 INFO @ Tue, 01 Nov 2022 17:55:35: #1 Redundant rate of treatment: 0.03 INFO @ Tue, 01 Nov 2022 17:55:35: #1 total tags in control: 50837 INFO @ Tue, 01 Nov 2022 17:55:35: #1 user defined the maximum tags... INFO @ Tue, 01 Nov 2022 17:55:35: #1 filter out redundant tags at the same location and the same strand by allowing at most 1 tag(s) INFO @ Tue, 01 Nov 2022 17:55:35: #1 tags after filtering in control: 50783 INFO @ Tue, 01 Nov 2022 17:55:35: #1 Redundant rate of control: 0.00 INFO @ Tue, 01 Nov 2022 17:55:35: #1 finished! INFO @ Tue, 01 Nov 2022 17:55:35: #2 Build Peak Model... INFO @ Tue, 01 Nov 2022 17:55:35: #2 looking for paired plus/minus strand peaks... INFO @ Tue, 01 Nov 2022 17:55:35: #2 Total number of paired peaks: 469 INFO @ Tue, 01 Nov 2022 17:55:35: #2 Model building with cross-correlation: Done INFO @ Tue, 01 Nov 2022 17:55:35: #2 finished! INFO @ Tue, 01 Nov 2022 17:55:35: #2 predicted fragment length is 228 bps INFO @ Tue, 01 Nov 2022 17:55:35: #2 alternative fragment length(s) may be 228 bps INFO @ Tue, 01 Nov 2022 17:55:35: #2.2 Generate R script for model : /tmp/RtmpcKwIto/run_callpeak_narrow0_model.r INFO @ Tue, 01 Nov 2022 17:55:35: #3 Call peaks... INFO @ Tue, 01 Nov 2022 17:55:35: #3 Pre-compute pvalue-qvalue table... INFO @ Tue, 01 Nov 2022 17:55:35: #3 Cutoff vs peaks called will be analyzed! INFO @ Tue, 01 Nov 2022 17:55:35: #3 Analysis of cutoff vs num of peaks or total length has been saved in b'/tmp/RtmpcKwIto/run_callpeak_narrow0_cutoff_analysis.txt' INFO @ Tue, 01 Nov 2022 17:55:35: #3 In the peak calling step, the following will be performed simultaneously: INFO @ Tue, 01 Nov 2022 17:55:35: #3 Write bedGraph files for treatment pileup (after scaling if necessary)... run_callpeak_narrow0_treat_pileup.bdg INFO @ Tue, 01 Nov 2022 17:55:35: #3 Write bedGraph files for control lambda (after scaling if necessary)... run_callpeak_narrow0_control_lambda.bdg INFO @ Tue, 01 Nov 2022 17:55:35: #3 Pileup will be based on sequencing depth in treatment. INFO @ Tue, 01 Nov 2022 17:55:35: #3 Call peaks for each chromosome... INFO @ Tue, 01 Nov 2022 17:55:36: #4 Write output xls file... /tmp/RtmpcKwIto/run_callpeak_narrow0_peaks.xls INFO @ Tue, 01 Nov 2022 17:55:36: #4 Write peak in narrowPeak format file... /tmp/RtmpcKwIto/run_callpeak_narrow0_peaks.narrowPeak INFO @ Tue, 01 Nov 2022 17:55:36: #4 Write summits bed file... /tmp/RtmpcKwIto/run_callpeak_narrow0_summits.bed INFO @ Tue, 01 Nov 2022 17:55:36: Done!More details about MACS3 can be found: https://macs3-project.github.io/MACS/.
sessionInfo()
#> R version 4.2.1 (2022-06-23)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 20.04.5 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.16-bioc/R/lib/libRblas.so
#> LAPACK: /home/biocbuild/bbs-3.16-bioc/R/lib/libRlapack.so
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_GB 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] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] MACSdata_1.5.0 MACSr_1.6.0 BiocStyle_2.26.0
#>
#> loaded via a namespace (and not attached):
#> [1] Biobase_2.58.0 httr_1.4.4
#> [3] sass_0.4.2 bit64_4.0.5
#> [5] jsonlite_1.8.3 AnnotationHub_3.6.0
#> [7] here_1.0.1 bslib_0.4.0
#> [9] shiny_1.7.3 assertthat_0.2.1
#> [11] interactiveDisplayBase_1.36.0 BiocManager_1.30.19
#> [13] stats4_4.2.1 BiocFileCache_2.6.0
#> [15] blob_1.2.3 GenomeInfoDbData_1.2.9
#> [17] yaml_2.3.6 BiocVersion_3.16.0
#> [19] pillar_1.8.1 RSQLite_2.2.18
#> [21] lattice_0.20-45 glue_1.6.2
#> [23] reticulate_1.26 digest_0.6.30
#> [25] promises_1.2.0.1 XVector_0.38.0
#> [27] htmltools_0.5.3 httpuv_1.6.6
#> [29] Matrix_1.5-1 pkgconfig_2.0.3
#> [31] dir.expiry_1.6.0 bookdown_0.29
#> [33] zlibbioc_1.44.0 purrr_0.3.5
#> [35] xtable_1.8-4 later_1.3.0
#> [37] tibble_3.1.8 KEGGREST_1.38.0
#> [39] generics_0.1.3 IRanges_2.32.0
#> [41] ellipsis_0.3.2 withr_2.5.0
#> [43] cachem_1.0.6 BiocGenerics_0.44.0
#> [45] cli_3.4.1 magrittr_2.0.3
#> [47] crayon_1.5.2 mime_0.12
#> [49] memoise_2.0.1 evaluate_0.17
#> [51] fansi_1.0.3 tools_4.2.1
#> [53] lifecycle_1.0.3 basilisk.utils_1.10.0
#> [55] stringr_1.4.1 S4Vectors_0.36.0
#> [57] AnnotationDbi_1.60.0 Biostrings_2.66.0
#> [59] GenomeInfoDb_1.34.0 compiler_4.2.1
#> [61] jquerylib_0.1.4 rlang_1.0.6
#> [63] RCurl_1.98-1.9 grid_4.2.1
#> [65] rappdirs_0.3.3 bitops_1.0-7
#> [67] rmarkdown_2.17 basilisk_1.10.0
#> [69] ExperimentHub_2.6.0 DBI_1.1.3
#> [71] curl_4.3.3 R6_2.5.1
#> [73] knitr_1.40 dplyr_1.0.10
#> [75] fastmap_1.1.0 bit_4.0.4
#> [77] utf8_1.2.2 rprojroot_2.0.3
#> [79] filelock_1.0.2 stringi_1.7.8
#> [81] parallel_4.2.1 Rcpp_1.0.9
#> [83] vctrs_0.5.0 png_0.1-7
#> [85] dbplyr_2.2.1 tidyselect_1.2.0
#> [87] xfun_0.34