Wikipedia

ChIP sequencing

ChIP-sequencing,also known asChIP-seq,is a method used to analyzeproteininteractions withDNA.ChIP-seq combineschromatin immunoprecipitation(ChIP) withmassively parallelDNA sequencingto identify thebinding sitesof DNA-associated proteins. It can be used to map global binding sites precisely for any protein of interest. Previously,ChIP-on-chipwas the most common technique utilized to study these protein–DNA relations.

Uses

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ChIP-seq is primarily used to determine howtranscription factorsand other chromatin-associated proteins influencephenotype-affecting mechanisms. Determining how proteins interact with DNA to regulategene expressionis essential for fully understanding many biological processes and disease states. Thisepigeneticinformation is complementary togenotypeand expression analysis. ChIP-seq technology is currently seen primarily as an alternative toChIP-chipwhich requires ahybridization array.This introduces some bias, as an array is restricted to a fixed number of probes. Sequencing, by contrast, is thought to have less bias, although the sequencing bias of different sequencing technologies is not yet fully understood.[1]

Specific DNA sites in direct physical interaction with transcription factors and other proteins can be isolated bychromatin immunoprecipitation.ChIP produces a library of target DNA sites bound to a protein of interest. Massively parallel sequence analyses are used in conjunction with whole-genome sequence databases to analyze the interaction pattern of any protein with DNA,[2]or the pattern of any epigeneticchromatinmodifications. This can be applied to the set of ChIP-able proteins and modifications, such as transcription factors,polymerasesandtranscriptional machinery,structural proteins,protein modifications,andDNA modifications.[3]As an alternative to the dependence on specific antibodies, different methods have been developed to find the superset of allnucleosome-depleted or nucleosome-disrupted active regulatory regions in the genome, likeDNase-Seq[4]andFAIRE-Seq.[5][6]

Workflow of ChIP-sequencing

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ChIP-sequencing workflow

ChIP

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ChIPis a powerful method to selectively enrich for DNA sequences bound by a particular protein in livingcells.However, the widespread use of this method has been limited by the lack of a sufficiently robust method to identify all of the enriched DNA sequences. The ChIP wet lab protocol contains ChIP and hybridization. There are essentially five parts to the ChIP protocol[7]that aid in better understanding the overall process of ChIP. In order to carry out the ChIP, the first step iscross-linking[8]using formaldehyde and large batches of the DNA in order to obtain a useful amount. The cross-links are made between the protein and DNA, but also between RNA and other proteins. The second step is the process of chromatin fragmentation which breaks up the chromatin in order to get high quality DNA pieces for ChIP analysis in the end. These fragments should be cut to become under 500 base pairs[9]each to have the best outcome for genome mapping. The third step is called chromatin immunoprecipitation,[7]which is what ChIP is short for. The ChIP process enhances specific crosslinked DNA-protein complexes using anantibodyagainst the protein of interest followed by incubation and centrifugation to obtain the immunoprecipitation. The immunoprecipitation step also allows for the removal of non-specific binding sites. The fourth step is DNA recovery and purification,[7]taking place by the reversed effect on the cross-link between DNA and protein to separate them and cleaning DNA with an extraction. The fifth and final step is the analyzation step of the ChIP protocol by the process ofqPCR,ChIP-on-chip(hybrid array) or ChIP sequencing.Oligonucleotideadaptors are then added to the small stretches of DNA that were bound to the protein of interest to enablemassively parallel sequencing.Through the analysis, the sequences can then be identified and interpreted by the gene or region to where the protein was bound.[7]

Sequencing

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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 oftiling arraysfor lower resolution.[10]

There are manynew sequencing methodsused in this sequencing step. Some technologies that analyze the sequences can usecluster amplificationof adapter-ligated ChIP DNA fragments on a solidflow cellsubstrate 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 undergoessequencing-by-synthesisin 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

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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

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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]

Unlikemicroarray-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

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STAT1 DNA association:ChIP-seq was used to study STAT1 targets inHeLaS3 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 oftranscription 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 wormC. elegansto 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 ofHistone modificationwere shown to be more correlated with transcription factor motifs at promoters in comparison to RNA level.[21]Hence author proposed that using histone modification ChIP-seq would provide more reliable inference of gene-regulatory networks in comparison to other methods based on expression.

ChIP-seq offers an alternative to ChIP-chip. STAT1 experimental ChIP-seq data have a high degree of similarity to results obtained by ChIP-chip for the same type of experiment, with greater than 64% of peaks in shared genomic regions. Because the data are sequence reads, ChIP-seq offers a rapid analysis pipeline as long as a high-quality genome sequence is available for read mapping and the genome doesn't have repetitive content that confuses the mapping process. ChIP-seq also has the potential to detect mutations in binding-site sequences, which may directly support any observed changes in protein binding and gene regulation.

Computational analysis

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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 callingmethods 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 Thomaset 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 usingHidden Markov Models.Examples for two-stage differential peak callers are ChIPDiff[25]and ODIN.[26]

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

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Similar methods

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  • 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.
  • HITS-CLIP[28][29](also calledCLIP-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-Seqto measure RNA-bound DNA and proteins.
  • ChIP-exouses exonuclease treatment to achieve up to single base-pair resolution
  • ChIP-nexusimproved version ofChIP-exoto achieve up to single base-pair resolution.
  • DRIP-sequses 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

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  1. ^Muhammad, Isiaka Ibrahim; Kong, Sze Ling; Akmar Abdullah, Siti Nor; Munusamy, Umaiyal (25 December 2019)."RNA-seq and ChIP-seq as Complementary Approaches for Comprehension of Plant Transcriptional Regulatory Mechanism".International Journal of Molecular Sciences.21(1): 167.doi:10.3390/ijms21010167.ISSN1422-0067.PMC6981605.PMID31881735.
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  4. ^Song, Lingyun; Crawford, Gregory E. (February 2010)."DNase-seq: a high-resolution technique for mapping active gene regulatory elements across the genome from mammalian cells".Cold Spring Harbor Protocols.2010(2): pdb.prot5384.doi:10.1101/pdb.prot5384.ISSN1559-6095.PMC3627383.PMID20150147.
  5. ^Giresi, Paul G.; Kim, Jonghwan; McDaniell, Ryan M.; Iyer, Vishwanath R.; Lieb, Jason D. (June 2007)."FAIRE (Formaldehyde-Assisted Isolation of Regulatory Elements) isolates active regulatory elements from human chromatin".Genome Research.17(6): 877–885.doi:10.1101/gr.5533506.ISSN1088-9051.PMC1891346.PMID17179217.
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  23. ^Xing H, Mo Y, Liao W, Zhang MQ (2012)."Genome-wide localization of protein-DNA binding and histone modification by a Bayesian change-point method with ChIP-seq data".PLOS Comput Biol.8(7): e1002613.Bibcode:2012PLSCB...8E2613X.doi:10.1371/journal.pcbi.1002613.PMC5429005.PMID22844240.
  24. ^Thomas R, Thomas S, Holloway AK, Pollard KS (2017)."Features that define the best ChIP-seq peak calling algorithms".Brief Bioinform.18(3): 441–450.doi:10.1093/bib/bbw035.PMC5429005.PMID27169896.
  25. ^Xu H, Wei CL, Lin F, Sung WK (October 2008)."An HMM approach to genome-wide identification of differential histone modification sites from ChIP-seq data".Bioinformatics.24(20): 2344–9.doi:10.1093/bioinformatics/btn402.PMID18667444.
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  27. ^abXu, Jinrui; Kudron, Michelle M; Victorsen, Alec; Gao, Jiahao; Ammouri, Haneen N; Navarro, Fabio C P; Gevirtzman, Louis; Waterston, Robert H; White, Kevin P; Reinke, Valerie; Gerstein, Mark (21 December 2020)."To mock or not: a comprehensive comparison of mock IP and DNA input for ChIP-seq".Nucleic Acids Research.49(3): gkaa1155.doi:10.1093/nar/gkaa1155.ISSN0305-1048.PMC7897498.PMID33347581.
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  • ReMap catalogue:An integrative and uniformChIP-Seqanalysis of regulatory elements from +2800 ChIP-seq datasets, giving a catalogue of 80 million peaks from 485 transcription regulators.[1]
  • ChIPBase database:a database for exploring transcription factor binding maps fromChIP-Seqdata. It provides the most comprehensive ChIP-Seq data set for various cell/tissue types and conditions.
  • GeneProf database and analysis tool:GeneProf is a freely accessible, easy-to-use analysis environment for ChIP-seq and RNA-seq data and comes with a large database of ready-analysed public experiments, e.g. for transcription factor binding and histone modifications.
  • Differential Peak CallingArchived15 May 2021 at theWayback Machine:Tutorial for differential peak calling with ODIN.
  • Bioinformatic analysis of ChIP-seq data:Comprehensive analysis of ChIP-seq data.[2]
  • KLTepigenome:Uncovering correlated variability in epigenomic datasets using the Karhunen-Loeve transform.
  • SignalSpider:a tool for probabilistic pattern discovery on multiple normalizedChIP-Seqsignal profiles
  • FullSignalRanker:a tool for regression and peak prediction on multiple normalizedChIP-Seqsignal profiles

  1. ^Chèneby J, Gheorghe M, Artufel M, Mathelier A, Ballester B (January 2018)."ReMap 2018: an updated atlas of regulatory regions from an integrative analysis of DNA-binding ChIP-seq experiments".Nucleic Acids Research.46(D1): D267–D275.doi:10.1093/nar/gkx1092.PMC5753247.PMID29126285.
  2. ^Bailey T, Krajewski P, Ladunga I, Lefebvre C, Li Q, Liu T, et al. (2013)."Practical guidelines for the comprehensive analysis of ChIP-seq data".PLOS Computational Biology.9(11): e1003326.Bibcode:2013PLSCB...9E3326B.doi:10.1371/journal.pcbi.1003326.PMC3828144.PMID24244136.
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