We aimed to analyze the strongest interchromosomal interactions and analyze their role and functionality.
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iterative_normalization.py is a script that performs normalization in each chrom_pair interchromosomal contact matrices we used all three normalizations:
- Knight-Ruiz (KR)
- Vanilla Coverage (VC)
- Square Root Vanilla Coverage (SQRTVC)
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concatenate_data.py is a script that look at the contact values frequences in the whole genome, calculate the threshold value for the one percent top-interactions (the most tight) and keep only those that bigger than this threshold
- works with raw and all normalized data
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jaccard_similarity.py is a script that calculates jaccard similarity coefficient between two top interactions datasets (raw and normalized)
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take_a_closer_look.py is a script that allows to get all interactions of higher resolution that are within top-interactions of 100 kb data
| metrics | raw | KRnormalized | VCnormalized | SQRTVCnormalized |
|---|---|---|---|---|
| initial number of contacts | 97719803 | 97719803 | 97719803 | 97719803 |
| threshold | 6.0 | 6.219 | 12.863(?) | 6.546 |
| 1% | 977198 | 977198 | 977198 | 977198 |
| sum bigger than 1% | 1236906 | 977222 | 977226 | 977270 |
| KRnormalized | VCnormalized | SQRTVCnormalized | |
|---|---|---|---|
| raw | 0.073 | 0.012 | 0.11 |
- Rao SS, Huntley MH, Durand NC, Stamenova EK et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 2014 Dec 18;159(7):1665-80. PMID: 25497547