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ATAC-seq
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<h2 id="procedure">Procedure</h2> <p>ATAC-seq identifies accessible <a href="/facts/DNA/nB89Iauz">DNA</a> regions by probing open chromatin with hyperactive mutant <a href="/facts/Transposase/eYld61TN">Tn5 Transposase</a> that inserts sequencing adapters into open regions of the genome.<a class="footnote-ref" id="fnref:6" href="#fn:6"><sup>6</sup></a><a class="footnote-ref" id="fnref:7" href="#fn:7"><sup>7</sup></a> While naturally occurring transposases have a low level of activity, ATAC-seq employs the mutated hyperactive transposase.<a class="footnote-ref" id="fnref:8" href="#fn:8"><sup>8</sup></a> In a process called "tagmentation", Tn5 transposase cleaves and tags double-stranded DNA with sequencing adaptors in a single enzymatic step.<a class="footnote-ref" id="fnref:9" href="#fn:9"><sup>9</sup></a><a class="footnote-ref" id="fnref:10" href="#fn:10"><sup>10</sup></a> The tagged DNA fragments are then purified, <a href="/facts/Polymerase_chain_reaction/G0jWzJy1">PCR</a>-amplified, and sequenced using <a href="/facts/Massive_parallel_sequencing/FjLl8GGa">next-generation sequencing</a>.<a class="footnote-ref" id="fnref:11" href="#fn:11"><sup>11</sup></a> Sequencing reads can then be used to infer regions of increased accessibility as well as to map regions of <a href="/facts/Transcription_factor/wro0DPHe">transcription factor</a> binding sites and nucleosome positions.<a class="footnote-ref" id="fnref:12" href="#fn:12"><sup>12</sup></a> The number of reads for a region correlate with how open that chromatin is, at single nucleotide resolution.<a class="footnote-ref" id="fnref:13" href="#fn:13"><sup>13</sup></a> </p><p>ATAC-seq requires no <a href="/facts/Sonication/RfKqX7t4">sonication</a> or <a href="/facts/Phenol-chloroform_extraction/LnyQz6fd">phenol-chloroform extraction</a> like FAIRE-seq;<a class="footnote-ref" id="fnref:14" href="#fn:14"><sup>14</sup></a> no antibodies like <a href="/facts/ChIP-seq/bCxyF70g">ChIP-seq</a>;<a class="footnote-ref" id="fnref:15" href="#fn:15"><sup>15</sup></a> and no sensitive enzymatic digestion like MNase-seq or DNase-seq.<a class="footnote-ref" id="fnref:16" href="#fn:16"><sup>16</sup></a> ATAC-seq preparation can be completed in under three hours.<a class="footnote-ref" id="fnref:17" href="#fn:17"><sup>17</sup></a> </p> <h2 id="applications">Applications</h2> <p>ATAC-Seq analysis is used to investigate a number of chromatin-accessibility signatures. The most common use is <a href="/facts/Nucleosome/HX5DR9bn">nucleosome</a> mapping experiments,<a class="footnote-ref" id="fnref:18" href="#fn:18"><sup>18</sup></a> but it can be applied to mapping <a href="/facts/Transcription_factor-binding_site/q6u6VSBs">transcription factor binding sites</a>,<a class="footnote-ref" id="fnref:19" href="#fn:19"><sup>19</sup></a> adapted to map <a href="/facts/DNA_methylation/npoRhsnX">DNA methylation</a> sites,<a class="footnote-ref" id="fnref:20" href="#fn:20"><sup>20</sup></a> or combined with sequencing techniques.<a class="footnote-ref" id="fnref:21" href="#fn:21"><sup>21</sup></a> </p><p>The utility of high-resolution enhancer mapping ranges from studying the evolutionary divergence of enhancer usage (e.g. between chimps and humans) during development<a class="footnote-ref" id="fnref:22" href="#fn:22"><sup>22</sup></a> and uncovering a lineage-specific enhancer map used during blood cell differentiation.<a class="footnote-ref" id="fnref:23" href="#fn:23"><sup>23</sup></a> </p><p>ATAC-Seq has also been applied to defining the genome-wide chromatin accessibility landscape in human cancers,<a class="footnote-ref" id="fnref:24" href="#fn:24"><sup>24</sup></a> and revealing an overall decrease in chromatin accessibility in <a href="/facts/Macular_degeneration/9pFARaka">macular degeneration</a>.<a class="footnote-ref" id="fnref:25" href="#fn:25"><sup>25</sup></a> Computational footprinting methods can be performed on ATAC-seq to find cell specific binding sites and transcription factors with cell specific activity.<a class="footnote-ref" id="fnref:26" href="#fn:26"><sup>26</sup></a> </p> <h2 id="single-cell-atac-seq">Single-cell ATAC-seq</h2> <p class="note">See also: <a href="/facts/Single-cell_sequencing/IfN72mpy">Single-cell sequencing</a></p> <p>Modifications to the ATAC-seq protocol have been made to accommodate <a href="/facts/Single-cell_analysis/SCcYWKXw">single-cell analysis</a>. <a href="/facts/Microfluidics/UW3LnTdE">Microfluidics</a> can be used to separate single nuclei and perform ATAC-seq reactions individually.<a class="footnote-ref" id="fnref:27" href="#fn:27"><sup>27</sup></a> With this approach, single cells are captured by either a microfluidic device or a liquid deposition system before tagmentation.<a class="footnote-ref" id="fnref:28" href="#fn:28"><sup>28</sup></a><a class="footnote-ref" id="fnref:29" href="#fn:29"><sup>29</sup></a> An alternative technique that does not require single cell isolation is combinatorial cellular indexing.<a class="footnote-ref" id="fnref:30" href="#fn:30"><sup>30</sup></a> This technique uses <a href="/facts/DNA_barcoding/gtkyL80c">barcoding</a> to measure chromatin accessibility in thousands of individual cells; it can generate epigenomic profiles from 10,000-100,000 cells per experiment.<a class="footnote-ref" id="fnref:31" href="#fn:31"><sup>31</sup></a> But combinatorial cellular indexing requires additional, custom-engineered equipment or a large quantity of custom, modified Tn5.<a class="footnote-ref" id="fnref:32" href="#fn:32"><sup>32</sup></a> Recently, a pooled barcode method called sci-CAR was developed, allowing joint profiling of chromatin accessibility and gene expression of single cells.<a class="footnote-ref" id="fnref:33" href="#fn:33"><sup>33</sup></a> </p><p>Computational analysis of scATAC-seq is based on construction of a count matrix with number of reads per open chromatin regions. Open chromatin regions can be defined, for example, by standard peak calling of pseudo bulk ATAC-seq data. Further steps include data reduction with PCA and clustering of cells.<a class="footnote-ref" id="fnref:34" href="#fn:34"><sup>34</sup></a> scATAC-seq matrices can be extremely large (hundreds of thousands of regions) and is extremely sparse, i.e. less than 3% of entries are non-zero.<a class="footnote-ref" id="fnref:35" href="#fn:35"><sup>35</sup></a> Therefore, imputation of count matrix is another crucial step performed by using various methods such as non-negative matrix factorization. As with bulk ATAC-seq, scATAC-seq allows finding regulators like transcription factors controlling gene expression of cells. This can be achieved by looking at the number of reads around TF motifs<a class="footnote-ref" id="fnref:36" href="#fn:36"><sup>36</sup></a> or footprinting analysis.<a class="footnote-ref" id="fnref:37" href="#fn:37"><sup>37</sup></a> </p> <h2 id="see-also">See also</h2> <ul><li>ScGET-seq</li> <li><a href="/facts/Single-cell_analysis/SCcYWKXw">Single-cell analysis</a></li></ul> <h2 id="external-links">External links</h2> <ul><li><a href="https://www.nature.com/nmeth/journal/v10/n12/fig_tab/nmeth.2688_F1.html">ATAC-seq probes open-chromatin state (figure)</a></li> <li><a href="https://web.archive.org/web/20150901062145/http://greenleaf.stanford.edu/portfolio_details_buenrostro_2013_nature_methods.html">ATAC-seq: Fast and sensitive epigenomic profiling</a></li> <li><a href="https://www.regulatory-genomics.org/hint/introduction/">HINT-ATAC: Identification of Transcription Factor Binding Sites using ATAC-seq</a></li></ul> <h2 id="references">References</h2> <ol> <li id="fn:1"><p>Buenrostro JD, Giresi PG, Zaba LC, Chang HY, Greenleaf WJ (December 2013). 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