(Check CALDER2 with mutiple updates here: https://github.com/CSOgroup/CALDER2)

CALDER user manuel

CALDER is a Hi-C analysis tool that allows: (1) compute chromatin domains from whole chromosome contacts; (2) derive their non-linear hierarchical organization and obtain sub-compartments; (3) compute nested sub-domains within each chromatin domain from short-range contacts. CALDER is currently implemented in R.

(A note on the performance of Calder vs PC-based approach)

• PC1 of the correlation matrix was typically used to define A/B compartment. We found Calder demonstrates superior robustness over PC-based approach in identifying meaningful compartments, particularly when faced with complex chromosomal structural variations (figure on the left) and loose interaction between the p and q arms (figure on the right)

Installation

Make sure all dependencies have been installed:

• R.utils (>= 2.9.0),
• doParallel (>= 1.0.15),
• ape (>= 5.3),
• dendextend (>= 1.12.0),
• fitdistrplus (>= 1.0.14),
• igraph (>= 1.2.4.1),
• Matrix (>= 1.2.17),
• rARPACK (>= 0.11.0),
• factoextra (>= 1.0.5),
• maptools (>= 0.9.5),
• data.table (>= 1.12.2),
• fields (>= 9.8.3),
• GenomicRanges (>= 1.36.0)

Clone its repository and install it from source:

git clone https://github.com/CSOgroup/CALDER.git

install.packages(path_to_CALDER, repos = NULL, type="source") ## install from the cloned source file

Please contact [email protected] for any questions about installation.

install CALDER and dependencies automaticly:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("GenomicRanges")
install.packages("remotes")
remotes::install_github("CSOgroup/CALDER")

Usage

The input data of CALDER is a three-column text file storing the contact table of a full chromosome (zipped format is acceptable, as long as it can be read by data.table::fread). Each row represents a contact record pos_x, pos_y, contact_value, which is the same format as that generated by the dump command of juicer (https://github.com/aidenlab/juicer/wiki/Data-Extraction):

16050000	16050000	10106.306
16050000	16060000	2259.247
16060000	16060000	7748.551
16050000	16070000	1251.3663
16060000	16070000	4456.1245
16070000	16070000	4211.7393
16050000	16080000	522.0705
16060000	16080000	983.1761
16070000	16080000	1996.749
...

A demo dataset is included in the repository CALDER/inst/extdata/mat_chr22_10kb_ob.txt.gz and can be accessed by system.file("extdata", "mat_chr22_10kb_ob.txt.gz", package='CALDER') once CALDER is installed. This data contains contact values of GM12878 on chr22 binned at 10kb (Rao et al. 2014)

CALDER contains three modules: (1) compute chromatin domains; (2) derive their hierarchical organization and obtain sub-compartments; (3) compute nested sub-domains within each compartment domain.

To run three modules in a single step:

CALDER_main(contact_mat_file, 
			chr, 
			bin_size, 
			out_dir, 
			sub_domains=TRUE, 
			save_intermediate_data=FALSE,
			genome='hg19')

To run three modules in seperated steps:

# This will not compute sub-domains, but save the intermediate_data that can be used to compute sub-domains latter on
CALDER_main(contact_mat_file, 
			chr, 
			bin_size, 
			out_dir, 
			sub_domains=FALSE, 
			save_intermediate_data=TRUE,
			genome='hg19') 

# (optional depends on needs) Compute sub-domains using intermediate_data_file that was previous saved in the out_dir (named as chrxx_intermediate_data.Rds)
CALDER_sub_domains(intermediate_data_file, 
				   chr, 
				   out_dir, 
				   bin_size) 

Paramters:

• contact_mat_file: path to the contact table of a chromosome
• chr: chromosome number. Either numeric or character, will be pasted to the output name
• bin_size: numeric, the size of a bin in consistent with the contact table
• out_dir: the output directory
• sub_domains: logical, whether to compute nested sub-domains
• save_intermediate_data: logical. If TRUE, an intermediate_data will be saved. This file can be used for computing nested sub-domains later on
• genome: string. Specifies the genome assembly (Default="hg19").

Output:

chrxx_domain_hierachy.tsv

• information of compartment domain and their hierarchical organization. The hierarchical structure is fully represented by compartment_label, for example, B.2.2.2 and B.2.2.1 are two sub-branches of B.2.2. The pos_end column specifies all compartment domain borders, except when it is marked as gap, which indicates it is the border of a gap chromsome region that has too few contacts and was excluded from the analysis (e.g., due to low mappability, deletion, technique flaw)

chrxx_sub_compartments.bed

• a .bed file containing the sub-compartment information, that can be visualized in IGV. Different colors were used to distinguish compartments (at the resolution of 8 sub-compartments)

chrxx_domain_boundaries.bed

• a .bed file containing the chromatin domains boundaries, that can be visualized in IGV

chrxx_nested_boundaries.bed

• a .bed file containing the nested sub-domain boundaries, that can be visualized in IGV

chrxx_intermediate_data.Rds

• an Rds file storing the intermediate_data that can be used to compute nested sub-domains (if CALDER is run in two seperated steps)

chrxx_log.txt, chrxx_sub_domains_log.txt

• log file storing the status and running time of each step

Running time:

For the computational requirement, running CALDER on the GM12878 Hi-C dataset at bin size of 40kb took 36 minutes to derive the chromatin domains and their hierarchy for all chromosomes (i.e., CALDER Step1 and Step2); 13 minutes to derive the nested sub-domains (i.e., CALDER Step3). At the bin size of 10kb, it took 1 h 44 minutes and 55 minutes correspondingly (server information: 40 cores, 64GB Ram, Intel(R) Xeon(R) Silver 4210 CPU @ 2.20GHz). The evaluation was done using a single core although CALDER can be run in a parallel manner.

Demo run:

library(CALDER)

contact_mat_file = system.file("extdata", "mat_chr22_10kb_ob.txt.gz", package = 'CALDER')

CALDER_main(contact_mat_file, chr=22, bin_size=10E3, out_dir='./GM12878', sub_domains=TRUE, save_intermediate_data=FALSE)

The saved .bed files can be view directly through IGV:

Citation

If you use CALDER in your work, please cite: https://www.nature.com/articles/s41467-021-22666-3

Contact information

• Author: Yuanlong LIU
• Affiliation: Computational Systems Oncology group, Department of Computational Biology, University of Lausanne, Switzerland
• Email: [email protected]