<p>Without being terribly familiar with your data or the tool or the tutorial, I think that there is a simple technical explanation: your statistical power to detect TADs will be much higher than your power to detect loops. I'm not sure how many reads you're working with, but in general, TADs are very big strong features and loops are smaller features. Bigger features will have more reads due to just being bigger pieces linear chromosome, and stronger features with more robust chromatin structure will show up better in Hi-C data than the more labile loop structures.</p>
<p>See for example this Figure from a <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6028237/" rel="nofollow noreferrer">review</a> showing qualitatively the expected strength of each feature. You just have a lot more data supporting TADs than you do loops!</p>
<p>That said, I have not been able to track down a citation explicitly saying so- but intuitively it makes sense to me that TADs would be easier to define than loops.</p>
<p><a href="https://i.sstatic.net/FNt7S.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/FNt7S.jpg" alt="Illustration of TAD/loop architecture in Hi-C data" /></a></p>
<p>You can evaluate the power of your dataset <a href="https://academic.oup.com/bioinformaticsadvances/article/3/1/vbad152/7319106" rel="nofollow noreferrer">here</a> for detecting loops. If you find that it has low power, that is probably the source of your small number of loops detected.</p>
<p>Note also that different tools may have very different power thresholds! Trying a few other loop detection tools may lead you to the conclusion that Homer just isn't very sensitive. There is indeed some reason to believe that Homer is not sensitive in <a href="https://academic.oup.com/gigascience/article/doi/10.1093/gigascience/giac061/6636891" rel="nofollow noreferrer">comparisons of different loop detection tools</a>.</p>
<p>You don't mention how much data you're working with or what kind of hi-c it is, so knowing that may partially answer your question.</p>

CC BY-SA 4.0
2024-04-02T05:23:37.837Z
2024-04-02T05:23:37.837Z
Maximilian Press
<p>Generally, loops are harder to identify than TADs because of the length scales and data involved.
TADs are often identified by detecting changepoints in Hi-C contact matrices averaged over Mbp along the diagonal, whereas loops are kbp scale increases in contact matrix values at some unknown distance away from the diagonal.</p>
<p>In practice, this makes TAD detection often more sensitive than loop detection, regardless of the underlying biology.
Theoretically, there should be multiple loops within each TAD, but Hi-C data isn't exactly optimized for detecting loops.</p>
<p><a href="https://bioinformatics.stackexchange.com/a/22338/240">Maximilian's answer</a> also raises a good point that HOMER isn't that sensitive of a loop caller.
Other methods like Fit-Hi-C (1 or 2), Peakachu, and <a href="https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02167-0" rel="nofollow noreferrer">Mustache</a> are largely better at loop calling, so you could try those and see if you get better results.</p>
<p>Loop detection is also very dependent on the library size for each sample, whereas TAD detection seems to be a bit more robust.
I'm not sure how many reads you have per sample, but that may be something to consider.</p>
<p>Overall, loop detection is pretty unreliable for representing the underlying biology of <em>cis</em>-regulatory interactions.
It's one of the best genome-wide methods we've got, but is still unreliable.
So if you want to draw inferences from the number of loops called in different experimental conditions, I would be careful, here.
There are lots of caveats and uncertainties that just come from the detection / measurement part of your experiment.</p>

CC BY-SA 4.0
2024-04-04T15:58:56.077Z
2024-04-04T15:58:56.077Z
James Hawley
<p>I have HiC reads from an HiC experiment and I am using HOMER to call loops and TADs. The numbers I am getting are like:</p>
<p>Total TADs: 5024; Total Loops : 254</p>
<p>As multiple loops are expected in a TAD so numbers of loops should be higher than the number of TADs. That's why I am not sure how to justify the numbers I am getting.</p>
<p>I made sure I am not doing any mistake in the HOMER pipeline of loop/TAD calling and I followed all the steps in the tutorial here: <a href="http://homer.ucsd.edu/homer/interactions2/index.html" rel="nofollow noreferrer">http://homer.ucsd.edu/homer/interactions2/index.html</a> Unfortunately I don't have any other loop calling program installed right now and HOMER is easy to install and run.</p>
<p>If someone working with HiC data analysis and TAD/loop calling from HiC reads can please comment on this?</p>
<p>Thanks</p>
<p>Following is the set of commands I used (based on HOMER tutorial here <a href="http://homer.ucsd.edu/homer/interactions2/index.html" rel="nofollow noreferrer">http://homer.ucsd.edu/homer/interactions2/index.html</a>) to get loop/TAD calls from HiC reads.
<strong>#Trimming/processing of HiC fastq files:</strong></p>
<p>homerTools trim -3 GATC -mis 0 -matchStart 20 -min 20 Hu_concat_1.fastq</p>
<p>homerTools trim -3 GATC -mis 0 -matchStart 20 -min 20 Hu_concat_2.fastq</p>
<p><strong>#Aligning the two paired-end fastq files to get sam:</strong></p>
<p>bowtie2 -p 20 -x hg38.fa -U Hu_concat_1.fastq.trimmed &gt; Hu_concat_1.sam</p>
<p>bowtie2 -p 20 -x hg38.fa -U Hu_concat_2.fastq.trimmed &gt; Hu_concat_2.sam</p>
<p><strong>#Making tag directory (the HOMER makeTagDirectory program is used to process the alignment file into a HOMER-style tag directory which is used for subsequent analysis steps)</strong></p>
<p>makeTagDirectory Hu_Hic_TagDir/ Hu_concat_1.sam,Hu_concat_2.sam -tbp 1</p>
<p><strong>#Calling loops and TADs</strong></p>
<p>findTADsAndLoops.pl find Hu_Hic_TagDir/ -cpu 10 -res 10000 -window 15000 -genome hg38.fa</p>
<p>The whole process ran smoothly without errors. It is just that in the output, the number of loops I am getting are far fewer than no. of TADs (Total TADs: 5024; Total Loops : 254) which I think is not biologically correct. If someone using HOMER for calling loops and TADs from HiC reads can justify this output, that would be really helpful.</p>

CC BY-SA 4.0
2024-04-29T17:20:51.967Z
I haven't looked into the scripts of Homer on what loop size they're setting to detect loops. Have you considered looking into loop size? Recently, I used Chromosight to detect loops and I was able to find more loops when I increased loop size.
miachom
<hi-c>
Loop and TAD calling from HiC reads using HOMER
# Loop and TAD calling from HiC reads using HOMER

(Asked by: username_0 on 2024-02-06)

I have HiC reads from an HiC experiment and I am using HOMER to call loops and TADs. The numbers I am getting are like:

Total TADs: 5024; Total Loops : 254

As multiple loops are expected in a TAD so numbers of loops should be higher than the number of TADs. That's why I am not sure how to justify the numbers I am getting.

I made sure I am not doing any mistake in the HOMER pipeline of loop/TAD calling and I followed all the steps in the tutorial here: <http://homer.ucsd.edu/homer/interactions2/index.html> Unfortunately I don't have any other loop calling program installed right now and HOMER is easy to install and run.

If someone working with HiC data analysis and TAD/loop calling from HiC reads can please comment on this?

Thanks

Following is the set of commands I used (based on HOMER tutorial here <http://homer.ucsd.edu/homer/interactions2/index.html>) to get loop/TAD calls from HiC reads.
**#Trimming/processing of HiC fastq files:**

homerTools trim -3 GATC -mis 0 -matchStart 20 -min 20 Hu\_concat\_1.fastq

homerTools trim -3 GATC -mis 0 -matchStart 20 -min 20 Hu\_concat\_2.fastq

**#Aligning the two paired-end fastq files to get sam:**

bowtie2 -p 20 -x hg38.fa -U Hu\_concat\_1.fastq.trimmed > Hu\_concat\_1.sam

bowtie2 -p 20 -x hg38.fa -U Hu\_concat\_2.fastq.trimmed > Hu\_concat\_2.sam

**#Making tag directory (the HOMER makeTagDirectory program is used to process the alignment file into a HOMER-style tag directory which is used for subsequent analysis steps)**

makeTagDirectory Hu\_Hic\_TagDir/ Hu\_concat\_1.sam,Hu\_concat\_2.sam -tbp 1

**#Calling loops and TADs**

findTADsAndLoops.pl find Hu\_Hic\_TagDir/ -cpu 10 -res 10000 -window 15000 -genome hg38.fa

The whole process ran smoothly without errors. It is just that in the output, the number of loops I am getting are far fewer than no. of TADs (Total TADs: 5024; Total Loops : 254) which I think is not biologically correct. If someone using HOMER for calling loops and TADs from HiC reads can justify this output, that would be really helpful.

**Tags:** hi-c
> **Comments:**
> **username_1**: I haven't looked into the scripts of Homer on what loop size they're setting to detect loops. Have you considered looking into loop size? Recently, I used Chromosight to detect loops and I was able to find more loops when I increased loop size.  

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## 2 answers

### Answer by username_2 on 2024-04-02. Score: 1

Without being terribly familiar with your data or the tool or the tutorial, I think that there is a simple technical explanation: your statistical power to detect TADs will be much higher than your power to detect loops. I'm not sure how many reads you're working with, but in general, TADs are very big strong features and loops are smaller features. Bigger features will have more reads due to just being bigger pieces linear chromosome, and stronger features with more robust chromatin structure will show up better in Hi-C data than the more labile loop structures.

See for example this Figure from a [review](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6028237/) showing qualitatively the expected strength of each feature. You just have a lot more data supporting TADs than you do loops!

That said, I have not been able to track down a citation explicitly saying so- but intuitively it makes sense to me that TADs would be easier to define than loops.

[![Illustration of TAD/loop architecture in Hi-C data](https://i.sstatic.net/FNt7S.jpg)](https://i.sstatic.net/FNt7S.jpg)

You can evaluate the power of your dataset [here](https://academic.oup.com/bioinformaticsadvances/article/3/1/vbad152/7319106) for detecting loops. If you find that it has low power, that is probably the source of your small number of loops detected.

Note also that different tools may have very different power thresholds! Trying a few other loop detection tools may lead you to the conclusion that Homer just isn't very sensitive. There is indeed some reason to believe that Homer is not sensitive in [comparisons of different loop detection tools](https://academic.oup.com/gigascience/article/doi/10.1093/gigascience/giac061/6636891).

You don't mention how much data you're working with or what kind of hi-c it is, so knowing that may partially answer your question.


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### Answer by username_3 on 2024-04-04. Score: 0

Generally, loops are harder to identify than TADs because of the length scales and data involved.
TADs are often identified by detecting changepoints in Hi-C contact matrices averaged over Mbp along the diagonal, whereas loops are kbp scale increases in contact matrix values at some unknown distance away from the diagonal.

In practice, this makes TAD detection often more sensitive than loop detection, regardless of the underlying biology.
Theoretically, there should be multiple loops within each TAD, but Hi-C data isn't exactly optimized for detecting loops.

[Maximilian's answer](https://bioinformatics.stackexchange.com/a/22338/240) also raises a good point that HOMER isn't that sensitive of a loop caller.
Other methods like Fit-Hi-C (1 or 2), Peakachu, and [Mustache](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02167-0) are largely better at loop calling, so you could try those and see if you get better results.

Loop detection is also very dependent on the library size for each sample, whereas TAD detection seems to be a bit more robust.
I'm not sure how many reads you have per sample, but that may be something to consider.

Overall, loop detection is pretty unreliable for representing the underlying biology of *cis*-regulatory interactions.
It's one of the best genome-wide methods we've got, but is still unreliable.
So if you want to draw inferences from the number of loops called in different experimental conditions, I would be careful, here.
There are lots of caveats and uncertainties that just come from the detection / measurement part of your experiment.


---

