<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.sstatic.net/bUY1a.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/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>

CC BY-SA 4.0
2022-02-09T20:23:20.587Z
2022-03-07T21:18:29.980Z
2022-03-07T21:18:29.980Z
eric_kernfeld
<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>

CC BY-SA 4.0
2022-02-08T16:21:50.973Z
I've looked into this a little and I can't find an answer. If I had to guess, the 4214 targets represent sequences with *any* number of ChIP hits, and the final 98 Fur-binding sites are those with ChIP peaks above the described threshold with paired RNA-seq showing the effect of Fur on expression at those loci. It's a bit frustrating that the "Effect" column of the dataset is "not determined" for all positions.
acvill
<chip-seq>
Where do I find the primary evidence for regulatory relationships included in RegulonDB?
# Where do I find the primary evidence for regulatory relationships included in RegulonDB?

(Asked by: username_0 on 2022-02-07)

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](http://regulondb.ccg.unam.mx/highthroughputdatasets?term=HTRI00000827) a whopping 4214 targets. (To repeat this search, go to the [downloads page](https://regulondb.ccg.unam.mx/central_panel_menu/downloads_menu.jsp), 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.](https://www.nature.com/articles/ncomms5910) 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](https://bmcbiol.biomedcentral.com/articles/10.1186/s12915-018-0555-y). 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.

**Tags:** chip-seq
> **Comments:**
> **username_1**: I've looked into this a little and I can't find an answer. If I had to guess, the 4214 targets represent sequences with *any* number of ChIP hits, and the final 98 Fur-binding sites are those with ChIP peaks above the described threshold with paired RNA-seq showing the effect of Fur on expression at those loci. It's a bit frustrating that the "Effect" column of the dataset is "not determined" for all positions.  

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## 1 answers

### Answer by username_0 on 2022-02-09. Score: 2 | Accepted answer

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](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6324031/) 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.

[![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).](https://i.sstatic.net/bUY1a.png)](https://i.sstatic.net/bUY1a.png)


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