text: user1708: When I look up high-throughout (ChIP) evidence for binding of the ferric uptake regulator Fur to sites in the genome of E. coli, RegulonDB lists a whopping 4214 targets. (To repeat this search, go to the downloads page, click on "High-throughput datasets",  enter Fur in the right-most search box, and click on the fourth link, for HTRI00000827.) Some of these have the same gene (alternate promoters?), but I think over 2,000 unique target genes are given. When I click through to the supporting citation, Seo et al. say "Collectively, a total of 98 Fur-binding sites were newly identified in this study[.]" Clearly I'm missing something here. Why does RegulonDB list so many more target genes than the original paper? What's the evidence, if not ChIP? Thanks!
  Edit: I've tried to reach out to RegulonDB using the contact form on their website, but I am unable to pass their captcha test! I am uncertain what the problem could be. My protocols are completely up to date. I will update the question if I manage to contact them.
  Edit: the paper describing incorporation of ChIP data into RegulonDB is here. It is obviously well-informed, including senior authors from labs behind multiple ChIP datasets. Tables 4 and 5 describe the quantity of new interactions added and are closer to the original reference than to what I am seeing from the website.
  
  user1708: I reached out to the maintainers, and these results are derived from re-analysis of the raw reads obtained from GEO. This is described in the regulonDB 10.5 paper sections "HT dataset processing" and "Reconstructing regulatory interactions". The processing pipeline, described in fig S1, is fairly typical -- finding motif matches in MACS2 peaks. The cutoff used was simply lower than the source paper, yielding more hits.
  Edit: This pipeline description also explains why the tables you download contain duplicates (same regulator, same target). Duplicates can arise from different binding sites upstream of the same gene.
  
  
original_text: 1708: <p>When I look up high-throughout (ChIP) evidence for binding of the ferric uptake regulator Fur to sites in the genome of <em>E. coli</em>, <a href="http://regulondb.ccg.unam.mx/highthroughputdatasets?term=HTRI00000827" rel="nofollow noreferrer">RegulonDB lists</a> a whopping 4214 targets. (To repeat this search, go to the <a href="https://regulondb.ccg.unam.mx/central_panel_menu/downloads_menu.jsp" rel="nofollow noreferrer">downloads page</a>, click on &quot;High-throughput datasets&quot;,  enter Fur in the <em>right-most</em> search box, and click on the fourth link, for <code>HTRI00000827</code>.) Some of these have the same gene (alternate promoters?), but I think over 2,000 unique target genes are given. When I click through to the supporting citation, <a href="https://www.nature.com/articles/ncomms5910" rel="nofollow noreferrer">Seo et al.</a> say &quot;Collectively, a total of 98 Fur-binding sites were newly identified in this study[.]&quot; Clearly I'm missing something here. Why does RegulonDB list so many more target genes than the original paper? What's the evidence, if not ChIP? Thanks!</p>
  <p>Edit: I've tried to reach out to RegulonDB using the contact form on their website, but I am unable to pass their captcha test! <code>I am uncertain what the problem could be. My protocols are completely up to date.</code> I will update the question if I manage to contact them.</p>
  <p>Edit: the paper describing incorporation of ChIP data into RegulonDB is <a href="https://bmcbiol.biomedcentral.com/articles/10.1186/s12915-018-0555-y" rel="nofollow noreferrer">here</a>. It is obviously well-informed, including senior authors from labs behind multiple ChIP datasets. Tables 4 and 5 describe the quantity of new interactions added and are closer to the original reference than to what I am seeing from the website.</p>
   1708: <p>I reached out to the maintainers, and these results are derived from <em>re-analysis of the raw reads obtained from GEO.</em> This is described in the <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6324031/" rel="nofollow noreferrer">regulonDB 10.5 paper</a> sections &quot;HT dataset processing&quot; and &quot;Reconstructing regulatory interactions&quot;. The processing pipeline, described in fig S1, is fairly typical -- finding motif matches in MACS2 peaks. The cutoff used was simply lower than the source paper, yielding more hits.</p>
  <p>Edit: This pipeline description also explains why the tables you download contain duplicates (same regulator, same target). Duplicates can arise from different binding sites upstream of the same gene.</p>
  <p><a href="https://i.stack.imgur.com/bUY1a.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/bUY1a.png" alt="A bioinformatics pipeline for processing RNA-seq and ChIP data from GEO includes peak calling (MACS2), binding-site search (RSAT), and RNA differential expression (FDR-corrected t-test)." /></a></p>
  