HiCool

Jacques Serizay

Contents

1 Processing sequencing Hi-C libraries with HiCool

The HiCool R/Bioconductor package provides an end-to-end interface to process and normalize Hi-C paired-end fastq reads into .(m)cool files.

1. The heavy lifting (fastq mapping, pairs parsing and pairs filtering) is performed by the underlying lightweight hicstuff python library (https://github.com/koszullab/hicstuff).
2. Pairs filering is done using the approach described in Cournac et al., 2012 and implemented in hicstuff.
3. cooler (https://github.com/open2c/cooler) library is used to parse pairs into a multi-resolution, balanced .mcool file. .(m)cool is a compact, indexed HDF5 file format specifically tailored for efficiently storing HiC-based data. The .(m)cool file format was developed by Abdennur and Mirny and published in 2019.
4. Internally, all these external dependencies are automatically installed and managed in R by a basilisk environment.

The main processing function offered in this package is HiCool(). To process .fastq reads into .pairs & .mcool files, one needs to provide:

• The path to each fastq file (r1 and r2);
• The genome reference, as a path to a .fasta sequence file, a path to a pre-computed bowtie2 index or a supported ID character (hg38, mm10, dm6, R64-1-1, WBcel235, GRCz10, Galgal4);
• The restriction enzyme(s) used for Hi-C.

x <- HiCool(
    r1 = '<PATH-TO-R1.fq.gz>', 
    r2 = '<PATH-TO-R2.fq.gz>', 
    restriction = '<RE1(,RE2)>', 
    binning = "<minimum resolution>", 
    genome = '<GENOME_ID>'
)

Here is a concrete example of Hi-C data processing.

• Example fastq files are retrieved using the HiContactsData package.
• Two restriction enzymes are used (these are the enzymes used in the Arima Kit).
• The final .mcool file will have three levels of resolutions, from 1000bp to 8000bp.
• The data will be mapped on R64-1-1, the yeast genome reference.
• All output files will be placed in output/ directory.

library(HiCool)
hcf <- HiCool(
    r1 = HiContactsData::HiContactsData(sample = 'yeast_wt', format = 'fastq_R1'), 
    r2 = HiContactsData::HiContactsData(sample = 'yeast_wt', format = 'fastq_R2'), 
    restriction = 'DpnII,HinfI', 
    binning = 1000, 
    genome = 'R64-1-1', 
    output = './HiCool/'
)
#> see ?HiContactsData and browseVignettes('HiContactsData') for documentation
#> loading from cache
#> see ?HiContactsData and browseVignettes('HiContactsData') for documentation
#> loading from cache
#> HiCool :: Recovering bowtie2 genome index from AWS iGenomes...
#> HiCool :: Initializing processing of fastq files [tmp folder: /tmp/Rtmp8JxNUb/03KUOG]...
#> HiCool :: Mapping fastq files...
#> HiCool :: Tidying up everything for you...
#> HiCool :: .fastq to .mcool processing done!
#> HiCool :: Check ./HiCool/folder to find the generated files
#> HiCool :: Generating HiCool report. This might take a while.
#> HiCool :: Report generated and available @ /tmp/RtmplcS2om/Rbuild35f77067ecccdb/HiCool/vignettes/HiCool/15c427129b3999_7833^mapped-R64-1-1^03KUOG.html
#> HiCool :: All processing successfully achieved. Congrats!

hcf
#> CoolFile object
#> .mcool file: ./HiCool//matrices/15c427129b3999_7833^mapped-R64-1-1^03KUOG.mcool 
#> resolution: 1000 
#> pairs file: ./HiCool//pairs/15c427129b3999_7833^mapped-R64-1-1^03KUOG.pairs 
#> metadata(3): log args stats
S4Vectors::metadata(hcf)
#> $log
#> [1] "./HiCool//logs/15c427129b3999_7833^mapped-R64-1-1^03KUOG.log"
#> 
#> $args
#> $args$r1
#> [1] "/home/biocbuild/.cache/R/ExperimentHub/15c427129b3999_7833"
#> 
#> $args$r2
#> [1] "/home/biocbuild/.cache/R/ExperimentHub/15c427540af966_7834"
#> 
#> $args$genome
#> [1] "/tmp/Rtmp8JxNUb/R64-1-1"
#> 
#> $args$binning
#> [1] "1000"
#> 
#> $args$restriction
#> [1] "DpnII,HinfI"
#> 
#> $args$iterative
#> [1] TRUE
#> 
#> $args$balancing_args
#> [1] " --min-nnz 10 --mad-max 5 "
#> 
#> $args$threads
#> [1] 1
#> 
#> $args$output
#> [1] "./HiCool/"
#> 
#> $args$exclude_chr
#> [1] "Mito|chrM|MT"
#> 
#> $args$keep_bam
#> [1] FALSE
#> 
#> $args$scratch
#> [1] "/tmp/Rtmp8JxNUb"
#> 
#> $args$wd
#> [1] "/tmp/RtmplcS2om/Rbuild35f77067ecccdb/HiCool/vignettes"
#> 
#> 
#> $stats
#> $stats$nFragments
#> [1] 1e+05
#> 
#> $stats$nPairs
#> [1] 64761
#> 
#> $stats$nDangling
#> [1] 9266
#> 
#> $stats$nSelf
#> [1] 1910
#> 
#> $stats$nDumped
#> [1] 32
#> 
#> $stats$nFiltered
#> [1] 53553
#> 
#> $stats$nDups
#> [1] 613
#> 
#> $stats$nUnique
#> [1] 52940
#> 
#> $stats$threshold_uncut
#> [1] 7
#> 
#> $stats$threshold_self
#> [1] 7

2 Optional parameters

Extra optional arguments can be passed to the hicstuff workhorse library:

• iterative (default: TRUE): By default, hicstuff first truncates your set of reads to 20bp and attempts to align the truncated reads, then moves on to aligning 40bp-truncated reads for those which could not be mapped, etc. This procedure is longer than a traditional mapping but allows for more pairs to be rescued. Set to FALSE if you want to perform standard alignment of fastq files without iterative alignment;
• balancing_args (default: " --min-nnz 10 --mad-max 5 "): Specify here any balancing argument to be used by cooler when normalizing the binned contact matrices. Full list of options available at cooler documentation website;
• threads (default: 1L): Number of CPUs to use to process data;
• exclude_chr (default: 'Mito|chrM|MT'): List here any chromosome you wish to remove from the final contact matrix file;
• keep_bam (default: FALSE): Set to TRUE if you wish to keep the pair of .bam files;
• scratch (default: tempdir()): Points to a temporary directory to be used for processing.

3 Output files

The important files generated by HiCool are the following:

• A log file: <output_folder>/logs/<prefix>^mapped-<genome>^<hash>.log
• A multi-resolution, balanced contact matrix file: <output_folder>/matrices/<prefix>^mapped-<genome>^<hash>.mcool
• A .pairs file: <output_folder>/pairs/<prefix>^mapped-<genome>^<hash>.pairs
• Several diagnosis plots: <output_folder>/plots/<prefix>^mapped-<genome>^<hash>_*.pdf.

The diagnosis plots illustrate how pairs were filtered during the processing, using a strategy described in Cournac et al., BMC Genomics 2012. The event_distance chart represents the frequency of ++, +-, -+ and -- pairs in the library, as a function of the number of restriction sites between each end of the pairs, and shows the inferred filtering threshold. The event_distribution chart indicates the proportion of each type of pairs (e.g. dangling, uncut, abnormal, …) and the total number of pairs retained (3D intra + 3D inter).

Notes:

• .pairs file format is defined by the 4DN consortium;
• .(m)cool file format is defined by cooler authors in the supporting publication.

4 System dependencies

Processing Hi-C sequencing libraries into .pairs and .mcool files requires several dependencies, to (1) align reads to a reference genome, (2) manage alignment files (SAM), (3) filter pairs, (4) bin them to a specific resolution and (5)

All system dependencies are internally managed by basilisk.utils. HiCool maintains a conda environment containing:

• python 3.12.11
• numpy 1.26.4
• bowtie2 2.5.4
• chromosight 1.6.3
• cooler 0.10.3
• hicstuff 3.2.4
• pairtools 1.1.3
• samtools 1.22.1

The first time HiCool() is executed, a fresh conda environment will be created and required dependencies automatically installed. This ensures compatibility between the different system dependencies needed to process Hi-C fastq files.

5 Session info

sessionInfo()
#> R version 4.5.1 (2025-06-13)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.2 LTS
#> 
#> Matrix products: default
#> BLAS:   /home/biocbuild/bbs-3.22-bioc/R/lib/libRblas.so 
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0  LAPACK version 3.12.0
#> 
#> 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       
#> 
#> time zone: America/New_York
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#>  [1] HiContactsData_1.11.0 ExperimentHub_2.99.5  AnnotationHub_3.99.6 
#>  [4] BiocFileCache_2.99.5  dbplyr_2.5.0          BiocGenerics_0.55.0  
#>  [7] generics_0.1.4        HiCool_1.9.1          HiCExperiment_1.9.1  
#> [10] BiocStyle_2.37.0     
#> 
#> loaded via a namespace (and not attached):
#>   [1] DBI_1.2.3                   httr2_1.2.0                
#>   [3] rlang_1.1.6                 magrittr_2.0.3             
#>   [5] matrixStats_1.5.0           compiler_4.5.1             
#>   [7] RSQLite_2.4.1               dir.expiry_1.17.0          
#>   [9] png_0.1-8                   vctrs_0.6.5                
#>  [11] stringr_1.5.1               pkgconfig_2.0.3            
#>  [13] crayon_1.5.3                fastmap_1.2.0              
#>  [15] XVector_0.49.0              rmdformats_1.0.4           
#>  [17] rmarkdown_2.29              sessioninfo_1.2.3          
#>  [19] tzdb_0.5.0                  UCSC.utils_1.5.0           
#>  [21] strawr_0.0.92               purrr_1.1.0                
#>  [23] bit_4.6.0                   xfun_0.52                  
#>  [25] cachem_1.1.0                GenomeInfoDb_1.45.7        
#>  [27] jsonlite_2.0.0              blob_1.2.4                 
#>  [29] rhdf5filters_1.21.0         DelayedArray_0.35.2        
#>  [31] Rhdf5lib_1.31.0             BiocParallel_1.43.4        
#>  [33] parallel_4.5.1              R6_2.6.1                   
#>  [35] bslib_0.9.0                 stringi_1.8.7              
#>  [37] RColorBrewer_1.1-3          reticulate_1.42.0          
#>  [39] GenomicRanges_1.61.1        jquerylib_0.1.4            
#>  [41] Rcpp_1.1.0                  Seqinfo_0.99.1             
#>  [43] bookdown_0.43               SummarizedExperiment_1.39.1
#>  [45] knitr_1.50                  IRanges_2.43.0             
#>  [47] Matrix_1.7-3                tidyselect_1.2.1           
#>  [49] dichromat_2.0-0.1           abind_1.4-8                
#>  [51] yaml_2.3.10                 codetools_0.2-20           
#>  [53] curl_6.4.0                  lattice_0.22-7             
#>  [55] tibble_3.3.0                withr_3.0.2                
#>  [57] InteractionSet_1.37.0       Biobase_2.69.0             
#>  [59] basilisk.utils_1.21.2       KEGGREST_1.49.1            
#>  [61] evaluate_1.0.4              Biostrings_2.77.2          
#>  [63] pillar_1.11.0               BiocManager_1.30.26        
#>  [65] filelock_1.0.3              MatrixGenerics_1.21.0      
#>  [67] stats4_4.5.1                plotly_4.11.0              
#>  [69] vroom_1.6.5                 BiocVersion_3.22.0         
#>  [71] S4Vectors_0.47.0            ggplot2_3.5.2              
#>  [73] scales_1.4.0                glue_1.8.0                 
#>  [75] lazyeval_0.2.2              tools_4.5.1                
#>  [77] BiocIO_1.19.0               data.table_1.17.8          
#>  [79] rhdf5_2.53.1                grid_4.5.1                 
#>  [81] tidyr_1.3.1                 crosstalk_1.2.1            
#>  [83] AnnotationDbi_1.71.0        basilisk_1.21.5            
#>  [85] cli_3.6.5                   rappdirs_0.3.3             
#>  [87] S4Arrays_1.9.1              viridisLite_0.4.2          
#>  [89] dplyr_1.1.4                 gtable_0.3.6               
#>  [91] sass_0.4.10                 digest_0.6.37              
#>  [93] SparseArray_1.9.0           htmlwidgets_1.6.4          
#>  [95] farver_2.1.2                memoise_2.0.1              
#>  [97] htmltools_0.5.8.1           lifecycle_1.0.4            
#>  [99] httr_1.4.7                  bit64_4.6.0-1