Binless normalization of Hi-C data provides significant interaction and difference detection independent of resolution
Analysis of Hi-C datasets is limited by the current existing methods for data normalization, with detection of features such as TADs and chromatin loops being inconsistent amongst different approaches. Here the authors develop Binless, a method that allows for reproducible normalization of Hi-C data...
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| Auteurs principaux: | , , , |
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| Format: | article |
| Langue: | EN |
| Publié: |
Nature Portfolio
2019
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| Sujets: | |
| Accès en ligne: | https://doaj.org/article/68f2d8d52dec41c0a48e547768b92e81 |
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| Résumé: | Analysis of Hi-C datasets is limited by the current existing methods for data normalization, with detection of features such as TADs and chromatin loops being inconsistent amongst different approaches. Here the authors develop Binless, a method that allows for reproducible normalization of Hi-C data independent of its resolution and compare how Binless performs in comparison with other methods. |
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