Enhancer recognition and prediction during spermatogenesis based on deep convolutional neural networks

Sun, Chengzhang; Zhang, Ning; Yu, Peng; Wu, Xiaolong; Li, Qun; Li, Tongtong; Li, Hao; Xiao, Xia; Shalmani, Abdullah; Li, Leijie; Che, Dongxue; Wang, Xiaodan; Zhang, Peng; Chen, Ziyu; Liu, Tong; Zhao, Jun; Hua, Jinlian; Liao, Mingzhi

doi:10.1039/d0mo00031k

Cited by 2 publications

(1 citation statement)

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“…Similarly, there are increasing numbers of machine learning tools that are able to predict the role of a sequence, (e.g. whether a sequence is an enhancer) from DNA alone or a combination of DNA and epigenomic assays like ChIP-seq (Ghandi et al 2014;Min et al 2017;Chen et al 2018;Le et al 2019;Nguyen et al 2019;Oubounyt et al 2019;Ma et al 2020;Shujaat et al 2020;Sun et al 2020;Yang et al 2021). For example, iEnhancer-ECNN is able to predict whether a 200-bp sequence is a strong enhancer, weak enhancer or non-enhancer just from DNA sequence (Nguyen et al 2019).…”

Section: Accelerating Livestock 3d Genomicsmentioning

confidence: 99%

Opportunity to improve livestock traits using 3D genomics

et al. 2021

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The advent of high-throughput chromosome conformation capture and sequencing (Hi-C) has enabled researchers to probe the 3D architecture of the mammalian genome in a genome-wide manner. Simultaneously, advances in epigenomic assays, such as chromatin immunoprecipitation and sequencing (ChIP-seq) and DNase-seq, have enabled researchers to study cis-regulatory interactions and chromatin accessibility across the same genomewide scale. The use of these data has revealed many unique insights into gene regulation and disease pathomechanisms in several model organisms. With the advent of these highthroughput sequencing technologies, there has been an ever-increasing number of datasets available for study; however, this is often limited to model organisms. Livestock species play critical roles in the economies of developing and developed nations alike. Despite this, they are greatly underrepresented in the 3D genomics space; Hi-C and related technologies have the potential to revolutionise livestock breeding by enabling a more comprehensive understanding of how production traits are controlled. The growth in human and model organism Hi-C data has seen a surge in the availability of computational tools for use in 3D genomics, with some tools using machine learning techniques to predict features and improve dataset quality. In this review, we provide an overview of the 3D genome and discuss the status of 3D genomics in livestock before delving into advancing the field by drawing inspiration from research in human and mouse. We end by offering future directions for livestock research in the field of 3D genomics.

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