ChIP sequencing

ChIP-sequencing, also known as ChIP-seq, is a method used to analyze protein interactions with DNA. ChIP-seq combines chromatin immunoprecipitation (ChIP) with massively parallel DNA sequencing to identify the binding sites of DNA-associated proteins. It can be used to map global binding sites precisely for any protein of interest. Previously, ChIP-on-chip was the most common technique utilized to study these protein–DNA relations.

Uses

ChIP-seq is primarily used to determine how

ChIP

After size selection, all the resulting ChIP-DNA fragments are sequenced simultaneously using a genome sequencer. A single sequencing run can scan for genome-wide associations with high resolution, meaning that features can be located precisely on the chromosomes. ChIP-chip, by contrast, requires large sets of tiling arrays for lower resolution.^[10]

There are many new sequencing methods used in this sequencing step. Some technologies that analyze the sequences can use cluster amplification of adapter-ligated ChIP DNA fragments on a solid flow cell substrate to create clusters of approximately 1000 clonal copies each. The resulting high density array of template clusters on the flow cell surface is sequenced by a genome analyzing program. Each template cluster undergoes sequencing-by-synthesis in parallel using novel fluorescently labelled reversible terminator nucleotides. Templates are sequenced base-by-base during each read. Then, the data collection and analysis software aligns sample sequences to a known genomic sequence to identify the ChIP-DNA fragments.^{[citation needed]}

Quality control

ChIP-seq offers us a fast analysis, however, a quality control must be performed to make sure that the results obtained are reliable:

• Non-redundant fraction: low-complexity regions should be removed as they are not informative and may interfere with mapping in the reference genome.^[11]
• Fragments in peaks: ratio of reads that are located in peaks over reads that are located where there isn't a peak.^[11]

Sensitivity

Sensitivity of this technology depends on the depth of the sequencing run (i.e. the number of mapped sequence tags), the size of the genome and the distribution of the target factor. The sequencing depth is directly correlated with cost. If abundant binders in large genomes have to be mapped with high sensitivity, costs are high as an enormously high number of sequence tags will be required. This is in contrast to ChIP-chip in which the costs are not correlated with sensitivity.^[12][13]

Unlike microarray-based ChIP methods, the precision of the ChIP-seq assay is not limited by the spacing of predetermined probes. By integrating a large number of short reads, highly precise binding site localization is obtained. Compared to ChIP-chip, ChIP-seq data can be used to locate the binding site within few tens of base pairs of the actual protein binding site. Tag densities at the binding sites are a good indicator of protein–DNA binding affinity,^[14] which makes it easier to quantify and compare binding affinities of a protein to different DNA sites.^[15]

Current research

STAT1 DNA association: ChIP-seq was used to study STAT1 targets in HeLa S3 cells which are clones of the HeLa line that are used for analysis of cell populations.^[16] The performance of ChIP-seq was then compared to the alternative protein–DNA interaction methods of ChIP-PCR and ChIP-chip.^[17]

Nucleosome Architecture of Promoters: Using ChIP-seq, it was determined that Yeast genes seem to have a minimal nucleosome-free promoter region of 150bp in which RNA polymerase can initiate transcription.^[18]

Transcription factor conservation: ChIP-seq was used to compare conservation of TFs in the forebrain and heart tissue in embryonic mice. The authors identified and validated the heart functionality of transcription enhancers, and determined that transcription enhancers for the heart are less conserved than those for the forebrain during the same developmental stage.^[19]

Genome-wide ChIP-seq: ChIP-sequencing was completed on the worm C. elegans to explore genome-wide binding sites of 22 transcription factors. Up to 20% of the annotated candidate genes were assigned to transcription factors. Several transcription factors were assigned to non-coding RNA regions and may be subject to developmental or environmental variables. The functions of some of the transcription factors were also identified. Some of the transcription factors regulate genes that control other transcription factors. These genes are not regulated by other factors. Most transcription factors serve as both targets and regulators of other factors, demonstrating a network of regulation.^[20]

Inferring regulatory network: ChIP-seq signal of

As with many high-throughput sequencing approaches, ChIP-seq generates extremely large data sets, for which appropriate computational analysis methods are required. To predict DNA-binding sites from ChIP-seq read count data, peak calling methods have been developed. One of the most popular methods^{[citation needed]} is MACS which empirically models the shift size of ChIP-Seq tags, and uses it to improve the spatial resolution of predicted binding sites.^[22] MACS is optimized for higher resolution peaks, while another popular algorithm, SICER is programmed to call for broader peaks, spanning over kilobases to megabases in order to search for broader chromatin domains. SICER is more useful for histone marks spanning gene bodies. A mathematical more rigorous method BCP (Bayesian Change Point) can be used for both sharp and broad peaks with faster computational speed,^[23] see benchmark comparison of ChIP-seq peak-calling tools by Thomas et al. (2017).^[24]

Another relevant computational problem is differential peak calling, which identifies significant differences in two ChIP-seq signals from distinct biological conditions. Differential peak callers segment two ChIP-seq signals and identify differential peaks using

To reduce spurious sites from ChIP-seq, multiple experimental controls can be used to detect binding sites from an IP experiment. Bay2Ctrls adopts a Bayesian model to integrate the DNA input control for the IP, the mock IP and its corresponding DNA input control to predict binding sites from the IP.[27] This approach is particularly effective for complex samples such as whole model organisms. In addition, the analysis indicates that for complex samples mock IP controls substantially outperform DNA input controls probably due to the active genomes of the samples.^[27]

See also

• ChIP-on-chip
• ChIP-PCR
• ChIP-PET
• Mammalian promoter database

Similar methods

• CUT&RUN sequencing, antibody-targeted controlled cleavage by micrococcal nuclease instead of ChIP, allowing for enhanced signal-to-noise ratio during sequencing.
• CUT&Tag sequencing, antibody-targeted controlled cleavage by transposase Tn5 instead of ChIP, allowing for enhanced signal-to-noise ratio during sequencing.
• Sono-Seq, identical to ChIP-Seq but skipping the immunoprecipitation step.
• CLIP-Seq
  ), for finding interactions with RNA rather than DNA.
• PAR-CLIP, another method for identifying the binding sites of cellular RNA-binding proteins (RBPs).
• RIP-Chip
  , same goal and first steps, but does not use cross linking methods and uses microarray instead of sequencing
• SELEX
  , a method for finding a consensus binding sequence
• Competition-ChIP, to measure relative replacement dynamics on DNA.
• ChiRP-Seq to measure RNA-bound DNA and proteins.
• ChIP-exo uses exonuclease treatment to achieve up to single base-pair resolution
• ChIP-nexus improved version of ChIP-exo to achieve up to single base-pair resolution.
• DRIP-seq uses S9.6 antibody to precipitate three-stranded DND:RNA hybrids called R-loops.
• TCP-seq, principally similar method to measure mRNA translation dynamics.
• Calling Cards, uses a transposase to mark the sequence where a transcription factor binds.^[30]

References