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ENCODE has some Hi-C with domains/boundaries in BED format: https://www.encodeproject.org/search/?type=Experiment&assay_title=Hi-C&files.file_type=bed+bed3%2B
Yes I was.
Click the "bed bed3+" button on your link (else the "file.txt" is blank). Then, click the “Download” button to download a “files.txt” file that contains a list of URLs to a file containing all the experimental metadata and links to download the file.
Then, keep only the *.bed URLs in your “files.txt”.
Then use the following command to download all the BED files in the list:
xargs -n 1 curl -O -L < files.txt
The boundary size should be equal to the bin size.
The genome is split into bins. Each bin is assigned a boundary score. The bins with local maximum boundary scores become the boundaries and separate the neighboring TADs. Thus, each bin is either in a specific TAD or is a boundary.
There should be a description somewhere, but I am not sure where. That would be a question for ENCODE.
Regarding bins, most operations in Hi-C are on a bin level since it's not possible to get single-base resolution. This means the genome is broken into bins/regions/windows (usually 10-40 kb).
A good review is here: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4347522/ . Specifically regarding boundary calculations:
An approach by Dixon et al. uses the following statistic: for each bin, we calculate the difference between its average upstream interactions and its average downstream interactions (within some genomic range). This difference is then transformed into a chi-squared statistic and the resulting value is referred to as the directionality index. At the boundaries of TADs, we expect to see a sharp change in the directionality index. Boundaries are then associated with each other using a Hidden Markov Model. Alternatively, others have simply used the ratio between average upstream and average downstream interactions.
An alternative approach is to calculate for each bin the average of interaction frequencies crossing over it (within some genomic range). This is referred to as the insulation score and can be thought of as the average of a square sliding along the matrix diagonal. We expect that this value will be lower at TAD boundaries. Then one can use standard techniques to find local minima and use those as boundaries, and define regions between consecutive boundaries to be TADs.
So I have another question about these TAD BED files and I hope that someone can help. So whats the actual diffrence between these files. If I am not wrong tthe most of them are (H19) genome files so I would assume that the start and stop locations in these files would be the match. Are these files generated from different experiments/predictions? I am a bit confused, sorry.
Hi everyone
I really enjoy the conversation above from TADs and bin size and hope you could help me. I download from 3D Geneome Browser some TADs annotation from different types of cell in bed file format. Some of these files were defined in 40 kb windown, but others in 25 kb. So I asked if someone knows a way to convert 25 kb to 40 kb by using bedtools or some linux commmand as awk. Still, is it a good way to get position in 40 kb windown?
BED file from IMR90_Lieberman-raw_TADs.txt
chr1 700000 1575000
chr1 1675000 1850000
chr1 1850000 2325000
chr1 2325000 3725000
chr1 3975000 6250000
chr1 6300000 6500000
chr1 6725000 8025000
chr1 8025000 8425000
chr1 8425000 8925000
chr1 8925000 9650000
chr1 9650000 9925000
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A search led to this paper. Look at supplementary data 1 and 2.
Which genome ?
Sorry, in human genome
For human genome, the supplementary data 1 looks good. Many thanks. (supplementary data 2 is for Drosophila)
If it may interest someone else, I extracted following informations from the supplementary data 1
genome version: hg19
816 genomic regulatory blocks (GRBs), predicting the boundaries of TAD
on chromosomes 1-22,X
display a range of sizes from 10 kb to 7.2 Mb
Your question is about TADs and now you are looking at GRB. Both are different.
Ok, I'm certainly wrong but I'm not sure to understand.
In the above paper, authors said that clusters of CNE (described previously as GRBs) strongly coincide with topological organisation, predicting the boundaries of hundreds of topologically associating domains (TADs) in human.
So, according to you, is it better to use other sources? As you suggest, I can download human ES cell and fibroblasts topological domains from:
http://chromosome.sdsc.edu/mouse/hi-c/download.html
Many thanks for any help you can provide me.
Dear Igor, as you seem to be a TAD specialist, I have a last question.
We know that disruption of TAD boundaries with structural variation can affect the expression of nearby genes, and this can cause disease.
Do you have any idea of if it can affect all the genes of the TAD? Or only those located at a certaine distance (if yes which one?) of the boundarie?
Many thanks for any help you can provide me
Not a TAD specialist, but I work with people who work with TADs.
The classic theory is that all the genes within a single TAD are correlated (of course, the correlation is generally fairly poor). For example, see Fig 1E: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4831574/figure/F1/
Dear Igor ,
Is there any readme file availible for these files? I am also intrested in these TAD's. I don't really understand what you mean with the bin size and boundary score.
I hope you can help