Chromatin accessibility profiling methods.
Liesbeth MinnoyeGeorgi K MarinovThomas KrausgruberLixia PanAlexandre P MarandStefano SecchiaWilliam J GreenleafEileen E M FurlongKeji ZhaoRobert J SchmitzChristoph BockStein AertsPublished in: Nature reviews. Methods primers (2021)
Chromatin accessibility, or the physical access to chromatinized DNA, is a widely studied characteristic of the eukaryotic genome. As active regulatory DNA elements are generally 'accessible', the genome-wide profiling of chromatin accessibility can be used to identify candidate regulatory genomic regions in a tissue or cell type. Multiple biochemical methods have been developed to profile chromatin accessibility, both in bulk and at the single-cell level. Depending on the method, enzymatic cleavage, transposition or DNA methyltransferases are used, followed by high-throughput sequencing, providing a view of genome-wide chromatin accessibility. In this Primer, we discuss these biochemical methods, as well as bioinformatics tools for analysing and interpreting the generated data, and insights into the key regulators underlying developmental, evolutionary and disease processes. We outline standards for data quality, reproducibility and deposition used by the genomics community. Although chromatin accessibility profiling is invaluable to study gene regulation, alone it provides only a partial view of this complex process. Orthogonal assays facilitate the interpretation of accessible regions with respect to enhancer-promoter proximity, functional transcription factor binding and regulatory function. We envision that technological improvements including single-molecule, multi-omics and spatial methods will bring further insight into the secrets of genome regulation.
Keyphrases
- transcription factor
- genome wide
- single molecule
- single cell
- dna methylation
- dna binding
- rna seq
- copy number
- circulating tumor
- high throughput
- gene expression
- mental health
- dna damage
- cell free
- genome wide identification
- electronic health record
- atomic force microscopy
- healthcare
- nitric oxide
- quality improvement
- oxidative stress
- machine learning
- deep learning
- nucleic acid