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bioRxiv · 10.1101/2020.12.07.413559

VEHiCLE: a Variationally Encoded Hi-C Loss Enhancement algorithm

Highsmith, M. R.·Cheng, J. R.
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Abstract

Chromatin conformation plays an important role in a variety of genomic processes. Hi-C is one of the most popular assays for inspecting chromatin conformation. However, the utility of Hi-C contact maps is bottlenecked by resolution. Here we present VEHiCLE, a deep learning algorithm for resolution enhancement of Hi-C contact data. VEHiCLE utilises a variational autoencoder and adversarial training strategy to enhance contact maps, making them more viable for downstream analysis. VEHiCLE expands previous efforts at Hi-C super resolution by providing novel insight into the biologically meaningful and human interpretable feature extraction. Using a variational autoencoder VEHiCLE provides a user tunable, full generative model for generating synthetic Hi-C data while also providing state-of-the-art results in enhancement of Hi-C data across multiple metrics.

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Highsmith, M. R., Cheng, J. R.. 2020-12-09. VEHiCLE: a Variationally Encoded Hi-C Loss Enhancement algorithm. https://doi.org/10.1101/2020.12.07.413559

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