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  • Review Article
  • Published:

ChIP–seq and beyond: new and improved methodologies to detect and characterize protein–DNA interactions

Nature Reviews Genetics volume 13, pages 840–852 (2012)Cite this article

Subjects

Key Points

  • Chromatin immunoprecipitation followed by sequencing (ChIP–seq) detects protein–DNA binding events and chemical modifications of histone proteins.

  • Recent technological advances in the ChIP–seq protocol have enabled assaying samples with limited cells, increased precision of the genomic location of binding events, and assaying multiple binding events. However, technical and analytical challenges remain.

  • Open chromatin assays — such as DNase–seq, formaldehyde-assisted identification of regulatory elements (FAIRE–seq) and DNaseI footprinting — offer complementary methods to identify genomic regions bound by regulatory proteins.

  • Chromatin conformation capture (3C) and chromatin interaction analysis with paired-end tag sequencing (ChIA-PET) experiments detect three-dimensional chromatin interactions between bound proteins.

  • Protein binding efficiency varies across sites within a single genome due to differences in the underlying genomic sequences and chromatin state. These differences affect the functionality of transcription factors within cells.

  • SNPs in protein–DNA binding sites can affect binding efficiency across individuals and can be detected by allelic biases in sequences produced from high-throughput sequencing assays.

Abstract

Chromatin immunoprecipitation experiments followed by sequencing (ChIP–seq) detect protein–DNA binding events and chemical modifications of histone proteins. Challenges in the standard ChIP–seq protocol have motivated recent enhancements in this approach, such as reducing the number of cells that are required and increasing the resolution. Complementary experimental approaches — for example, DNaseI hypersensitive site mapping and analysis of chromatin interactions that are mediated by particular proteins — provide additional information about DNA-binding proteins and their function. These data are now being used to identify variability in the functions of DNA-binding proteins across genomes and individuals. In this Review, I describe the latest advances in methods to detect and functionally characterize DNA-bound proteins.

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Figure 1: Comparison of experimental protocols.
Figure 2: General analysis pipeline for sequence-tag experiments.
Figure 3: DNaseI footprints correspond to bound proteins.
Figure 4: Detecting chromatin interactions.
Figure 5: Allele-specific bias in a CTCF ChIP–seq experiment.

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Acknowledgements

I gratefully acknowledge support from the US National Institutes of Health grants U54-HG004563, R21-DA027040 and U01 CA157703, the Department of Defense grant W81XWH-10-1-0772, and the University Cancer Research Fund from the University of North Carolina at Chapel Hill.

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  1. Departments of Genetics and Biology, Carolina Center for Genome Sciences, Lineberger Comprehensive Cancer Center, The University of North Carolina at Chapel Hill, 120 Mason Farm Road, CB#7264, Chapel Hill, 27599, North Carolina, USA

    Terrence S. Furey

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Glossary

Sonication

The fragmenting of DNA sequence by exposing it to high-frequency sound waves.

Exonuclease

An enzyme that cleaves a single nucleotide from the end of a DNA molecule.

Crosslinked

The strong binding of DNA to interacting proteins through covalent bonds.

Mappability

The uniqueness of a stretch of DNA sequence compared with a whole-genome sequence. Short sequence reads can be confidently mapped to unique sequence, but less confidently mapped to sequence that occurs multiple times in a genome.

DNA binding motifs

A degenerate pattern of DNA sequences to which transcription factors prefer to bind. They are often represented as a probabilistic matrix.

Promoters

DNA sequences immediately upstream of transcription start sites at which RNA polymerases and transcription factors bind to initiate gene transcription.

Enhancers

DNA sequences at which transcription factors bind that increase the transcription rate of one or more target genes that can be at varying distances from the enhancer.

Silencers

DNA sequences at which transcription factors bind that decrease the transcription rate of one or more target genes that can be at varying distances from the silencer.

Insulators

DNA sequences that interfere with enhancer and/or silencer activity.

Locus control regions

Regulatory elements that generally control transcription of multiple genes in a single locus.

Hidden Markov model

(HMM). A statistical model consisting of states that represent an aspect of a sequence (such as in a footprint), which transitions between states; it is used to label bases in a sequence with the modelled property. HMMs are also used in many gene prediction programs.

Bayesian mixture model

A probabilistic model that is used to represent the presence of multiple subpopulations (such as DNaseI footprints) within the whole population (such as the whole genome sequence). Bayesian mixture models allow for the incorporation of prior knowledge about subpopulation frequencies.

Biotinylated

A protein or nucleic acid to which a small biotin molecule has been attached. Biotin binds to streptavidin, thus allowing for the isolation of biotinylated molecules.

Dissociation constant

A constant that reflects the amount of energy that is required to separate two interacting molecules, often referred to as Kd.

DNaseI-sensitivity quantitative trait loci

(dsQTL). A locus whose sensitivity to DNaseI digestion varies based on the presence of different alleles in that locus. An allelic difference may influence the binding of proteins at this locus, causing the variation in digestion.

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Furey, T. ChIP–seq and beyond: new and improved methodologies to detect and characterize protein–DNA interactions. Nat Rev Genet 13, 840–852 (2012). https://doi.org/10.1038/nrg3306

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