4See: A Flexible Browser to Explore 4C Data
4See: A Flexible Browser to Explore 4C Data
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DOI：
10.3389/fgene.2019.01372
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发表时间：
2020-01-21
影响因子：
3.7
通讯作者：
Sexton, Tom
中科院分区：
文献类型：
--
作者：
Ben Zouari, Yousra;Platania, Angeliki;Sexton, Tom
已经确定许多后生动物基因的转录受启动子以外的远端调控序列的调控。增强子已被鉴定为与其调节基因相距达百万碱基，和/或邻近未调节基因的内含子或在未调节基因的内含子内。因此，新鉴定的调控元件的靶基因的明确鉴定可能是具有挑战性的。研究充分的增强子已经被发现与受调控的基因直接物理接近，推测是通过形成染色质环。因此，评估不同遗传元件之间的邻近频率的染色体构象捕获（3C）衍生物是用于探索远端调控元件的基因调控的流行方法。对于染色质环和启动子-增强子通信的研究，4C（环状染色体构象捕获）是选择、优化成本（所需测序深度）、通量和分辨率的方法之一。为了便于对4C数据进行可视化检查，我们推出了4See，一个多功能和用户友好的浏览器。4See允许将来自相同诱饵的4C图谱灵活地绘制在一起，允许比较生物学重复，或合并用于不同细胞类型或实验条件之间的比较。4C图谱可以与基因轨迹、线性表观基因组图谱和注释的感兴趣区域（例如所谓的显著相互作用）整合，从而允许利用有限的计算资源或生物信息学专业知识进行快速数据探索。
It is established that transcription of many metazoan genes is regulated by distal regulatory sequences beyond the promoter. Enhancers have been identified at up to megabase distances from their regulated genes, and/or proximal to or within the introns of unregulated genes. The unambiguous identification of the target genes of newly identified regulatory elements can thus be challenging. Well-studied enhancers have been found to come into direct physical proximity with regulated genes, presumably by the formation of chromatin loops. Chromosome conformation capture (3C) derivatives that assess the frequency of proximity between different genetic elements is thus a popular method for exploring gene regulation by distal regulatory elements. For studies of chromatin loops and promoter-enhancer communication, 4C (circular chromosome conformation capture) is one of the methods of choice, optimizing cost (required sequencing depth), throughput, and resolution. For ease of visual inspection of 4C data we present 4See, a versatile and user-friendly browser. 4See allows 4C profiles from the same bait to be flexibly plotted together, allowing biological replicates to either be compared, or pooled for comparisons between different cell types or experimental conditions. 4C profiles can be integrated with gene tracks, linear epigenomic profiles, and annotated regions of interest, such as called significant interactions, allowing rapid data exploration with limited computational resources or bioinformatics expertise.