Table 2.

List of high-throughput techniques, their functions and corresponding pros and caveats.

High-throughput Technology	Function	Pros	Caveats	Ref
WGS	Identify mutations genome wide	The ability to identify NCMs in all regions (not only regulatory regions). The potential identification of novel mutations implicated in cancer.	Accuracy relies on sequencing depth The alignment of short reads across repetitive regions. Large volume of data to process.	[63,70]
WES	Identify mutations within exon regions.	Cheaper method of sequencing the protein-coding regions of the genome. Well optimised for the identification of SNVs. ∙	Can be limited to exonic regions. Coverage is not as uniform as WGS.	[70]
ChIP-seq	Targeted approach to identify NCMs in putative functional regulatory regions.	Can identify putative active and repressed regulatory regions. Can be used for the identification of TF binding. ∙	Only a snap shot in time of global chromatin accessibility, which continually changes. Requires large amounts of tissue to obtain purified cells. Technically challenging to carry out.	[71]
DNase-seq	The identification of DNase I hypersensitivity site, mapping open chromatic genome wide.	No prior knowledge of histone modifications or TFBS need to be known.	Requires a large number of cells. Requires further ChIP analysis or functional assay to determine the function of the regulatory region identified.	[72,73]
ATAC-seq	Mapping chromatin accessibility genome-wide using a Tn5 transposase which inserts adaptors into regions of open chromatin	Quick processing method	Results are sensitive to variations in cell numbers.	[74,75]
FAIRE-seq	Allows the identification of nucleosome depleted regions, mapping regions of open chromatin.	Able to detect chromatin accessibility in a relatively low number of cells. Cheap and easy method to perform. ∙	High background noise levels, making data interpretation computationally challenging. Results are dependent on fixation efficiency.	[75,76]
RNA-seq	Measure of gene expression.	Can be used to identify allele specific imbalance.	Requires high read coverage to detect AI. ∙	[77,78]
4C-seq	Identification of long-range DNA contacts with a single genomic locus of interest.	Highly reproducible data. Ideal for analysing a known loci of interest.	Local interactions will be missed from the region of interest. Unable to detect interactions on a global level. Requires a large number of cells.	[79]
Hi-C-seq	Identification of long-range chromatin interactions on a global level.	An unbiased method Ideal for looking at changes within TAD regions and supra-TAD chromatin organisation.	Low resolution can be prone to high levels of noise. Requires a large number of cells. Not ideal for the identification of individual loci.	[31]
ChIA-PET	A combination of ChIP and 3C techniques allowing the analysis of both protein-DNA complexes and long-range interactions, genome wide.	Identifies both the DNA and protein present at a given loci.	Limited by the specificity and purity of the antibodies used.	[31,61]

Note: Transcription factor binding sites (TBFS).