PredTAD: A machine learning framework that models 3D chromatin organization alterations leading to oncogene dysregulation in breast cancer cell lines.
PredTAD: A machine learning framework that models 3D chromatin organization alterations leading to oncogene dysregulation in breast cancer cell lines.
复制标题
DOI：
10.1016/j.csbj.2021.05.013
复制
发表时间：
2021
影响因子：
6
通讯作者：
Zhou X
中科院分区：
文献类型：
--
作者：
Chyr J;Zhang Z;Chen X;Zhou X
拓扑关联结构域（TADs）在基因组组织和基因调控中起着重要作用;然而，它们经常在疾病中发生改变。高通量染色质构象捕获测定，如Hi-C，可以捕获增加相互作用的结构域，并且可以使用完善的分析工具鉴定TAD和边界。然而，生成Hi-C数据是昂贵的。在我们的研究中，我们使用一种新开发的名为Predix的机器学习模型来解决多组学数据与高阶染色质结构之间的关系。我们的工具使用已经可用且具有成本效益的数据库，如转录因子和组蛋白修饰ChIPseq数据。具体来说，Predogs利用表观遗传和遗传特征以及邻近信息将整个人类基因组分类为边界或非边界区域。我们的工具可以预测正常和乳腺癌基因组之间的边界变化。其中最重要的功能，预测边界的变化是CTCF，亚基的cohesin（RAD 21和SMC 3），和染色体数目，这表明他们的保守和动态边界的形成中的作用。在进一步分析后，我们观察到改变的乳腺癌边界附近的基因被发现参与了几个重要的乳腺癌信号通路，如Ras，Jak-STAT和雌激素信号通路。我们还发现了一个导致RET癌基因过度表达的边界改变。Predogs还可以成功地预测其他条件和疾病的边界变化。总之，我们新开发的机器学习工具可以更全面地了解乳腺癌细胞中信号通路激活、基因表达改变和疾病状态所涉及的动态3D染色质结构。
Topologically associating domains, or TADs, play important roles in genome organization and gene regulation; however, they are often altered in diseases. High-throughput chromatin conformation capturing assays, such as Hi-C, can capture domains of increased interactions, and TADs and boundaries can be identified using well-established analytical tools. However, generating Hi-C data is expensive. In our study, we addressed the relationship between multi-omics data and higher-order chromatin structures using a newly developed machine-learning model called PredTAD. Our tool uses already-available and cost-effective datatypes such as transcription factor and histone modification ChIPseq data. Specifically, PredTAD utilizes both epigenetic and genetic features as well as neighboring information to classify the entire human genome as boundary or non-boundary regions. Our tool can predict boundary changes between normal and breast cancer genomes. Among the most important features for predicting boundary alterations were CTCF, subunits of cohesin (RAD21 and SMC3), and chromosome number, suggesting their roles in conserved and dynamic boundaries formation. Upon further analysis, we observed that genes near altered TAD boundaries were found to be involved in several important breast cancer signaling pathways such as Ras, Jak-STAT, and estrogen signaling pathways. We also discovered a TAD boundary alteration that contributes to RET oncogene overexpression. PredTAD can also successfully predict TAD boundary changes in other conditions and diseases. In conclusion, our newly developed machine learning tool allowed for a more complete understanding of the dynamic 3D chromatin structures involved in signaling pathway activation, altered gene expression, and disease state in breast cancer cells.
登录
查看更多内容
影响因子：
14.9
作者：
Feng Y;Pauklin S
通讯作者：
Pauklin S
影响因子：
3.8
作者：
DeSantis, Carol E.;Bray, Freddie;Jemal, Ahmedin
通讯作者：
Jemal, Ahmedin
结合组蛋白标记和序列信息预测拓扑关联域边界的计算方法
影响因子：
4.4
作者：
Gan, Wei;Luo, Juan;Li, Meng Long
通讯作者：
Li, Meng Long
影响因子：
9.8
作者：
Deardorff, Matthew A.;Kaur, Maninder;Krantz, Ian D.
通讯作者：
Krantz, Ian D.
影响因子：
48
作者：
Danko CG;Hyland SL;Core LJ;Martins AL;Waters CT;Lee HW;Cheung VG;Kraus WL;Lis JT;Siepel A
通讯作者：
Siepel A