bioRxiv · 10.1101/605576
An Integrative Approach for Fine-Mapping Chromatin Interactions
Abstract
Chromatin interactions play an important role in genome architecture and regulation. The Hi-C assay generates such interactions maps genome-wide, but at relatively low resolutions (e.g., 5-25kb), which is substantially larger than the resolution of transcription factor binding sites or open chromatin sites that are potential sources of such interactions. To predict the sources of Hi-C identified interactions at a high resolution (e.g., 100bp), we developed a computational method that integrates ChIP-seq data of transcription factors and histone marks and DNase-seq data. Our method,{chi} -SCNN, uses this data to first train a Siamese Convolutional Neural Network (SCNN) to discriminate between called Hi-C interactions and non-interactions.{chi} -SCNN then predicts the high-resolution source of each Hi-C interaction using a feature attribution method. We show these predictions recover original Hi-C peaks after extending them to be coarser. We also show{chi} -SCNN predictions enrich for evolutionarily conserved bases, eQTLs, and CTCF motifs, supporting their biological significance.{chi} -SCNN provides an approach for analyzing important aspects of genome architecture and regulation at a higher resolution than previously possible.\n\n{chi}-SCNN software is available on GitHub (https://github.com/ernstlab/X-SCNN).
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Jaroszewicz, A. T., Ernst, J.. 2019-04-11. An Integrative Approach for Fine-Mapping Chromatin Interactions. https://doi.org/10.1101/605576
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