Binless normalization of Hi-C data provides significant interaction and difference detection independent of resolution
Chromosome conformation capture techniques, such as Hi-C, are fundamental in characterizing genome organization. These methods have revealed several genomic features, such as chromatin loops, whose disruption can have dramatic effects in gene regulation. Unfortunately, their detection is difficult;...
| Autores: | , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2019 |
| País: | España |
| Institución: | Universitat Pompeu Fabra |
| Repositorio: | Repositorio Digital de la UPF |
| OAI Identifier: | oai:repositori.upf.edu:10230/42070 |
| Acceso en línea: | http://hdl.handle.net/10230/42070 http://dx.doi.org/10.1038/s41467-019-09907-2 |
| Access Level: | acceso abierto |
| Palabra clave: | Caulobacter crescentus Cromatina Gens -- Mapatge Biologia computacional ADN Genomes |
| Sumario: | Chromosome conformation capture techniques, such as Hi-C, are fundamental in characterizing genome organization. These methods have revealed several genomic features, such as chromatin loops, whose disruption can have dramatic effects in gene regulation. Unfortunately, their detection is difficult; current methods require that the users choose the resolution of interaction maps based on dataset quality and sequencing depth. Here, we introduce Binless, a resolution-agnostic method that adapts to the quality and quantity of available data, to detect both interactions and differences. Binless relies on an alternate representation of Hi-C data, which leads to a more detailed classification of paired-end reads. Using a large-scale benchmark, we demonstrate that Binless is able to call interactions with higher reproducibility than other existing methods. Binless, which is freely available, can thus reliably be used to identify chromatin loops as well as for differential analysis of chromatin interaction maps. |
|---|