Integrating and mining the chromatin landscape of cell-type specificity using self-organizing maps.
Integrating and mining the chromatin landscape of cell-type specificity using self-organizing maps.
复制标题
DOI：
10.1101/gr.158261.113
复制
发表时间：
2013-12
期刊：
影响因子：
7
通讯作者：
Wold BJ
中科院分区：
文献类型：
--
作者：
Mortazavi A;Pepke S;Jansen C;Marinov GK;Ernst J;Kellis M;Hardison RC;Myers RM;Wold BJ
我们测试了自组织映射（SOM）是否可以用于有效地整合，可视化和挖掘不同的基因组数据类型，包括复杂的染色质签名。一个细粒度的SOM是在72个ChIP-seq组蛋白修饰和DNase-seq数据集上训练的，这些数据集来自ENCODE项目联盟研究的六种生物多样性细胞系。我们挖掘由此产生的SOM，以确定染色质签名相关的序列特异性转录因子占用，序列基序富集，和生物功能。为了突出富集特定功能的簇，如转录启动子或增强子，我们将训练期间未使用的其他数据集覆盖在地图上，如ChIP-seq，RNA-seq，CAGE以及文献中关于顺式作用调控模块的信息。我们使用SOM根据细胞类型特异性染色质特征来解析已知的转录增强子，并且我们通过EP 300（也称为p300）占用率在地图上进一步证实了这种模式。以这种方式鉴定了多种ENCODE细胞类型的新的候选细胞类型特异性增强子，沿着有普遍存在的增强子活性的新候选物。开发了一个交互式Web界面，允许用户可视化和自定义挖掘ENCODE SOM。我们的结论是，在来自多种细胞类型的染色质数据上训练的大型SOM提供了一种强有力的方法，可以在用户选择的粒度级别上识别基因组数据中的复杂关系。
We tested whether self-organizing maps (SOMs) could be used to effectively integrate, visualize, and mine diverse genomics data types, including complex chromatin signatures. A fine-grained SOM was trained on 72 ChIP-seq histone modifications and DNase-seq data sets from six biologically diverse cell lines studied by The ENCODE Project Consortium. We mined the resulting SOM to identify chromatin signatures related to sequence-specific transcription factor occupancy, sequence motif enrichment, and biological functions. To highlight clusters enriched for specific functions such as transcriptional promoters or enhancers, we overlaid onto the map additional data sets not used during training, such as ChIP-seq, RNA-seq, CAGE, and information on cis-acting regulatory modules from the literature. We used the SOM to parse known transcriptional enhancers according to the cell-type-specific chromatin signature, and we further corroborated this pattern on the map by EP300 (also known as p300) occupancy. New candidate cell-type-specific enhancers were identified for multiple ENCODE cell types in this way, along with new candidates for ubiquitous enhancer activity. An interactive web interface was developed to allow users to visualize and custom-mine the ENCODE SOM. We conclude that large SOMs trained on chromatin data from multiple cell types provide a powerful way to identify complex relationships in genomic data at user-selected levels of granularity.
登录
查看更多内容
影响因子：
64.8
作者：
Ernst, Jason;Kheradpour, Pouya;Mikkelsen, Tarjei S.;Shoresh, Noam;Ward, Lucas D.;Epstein, Charles B.;Zhang, Xiaolan;Wang, Li;Issner, Robbyn;Coyne, Michael;Ku, Manching;Durham, Timothy;Kellis, Manolis;Bernstein, Bradley E.
通讯作者：
Bernstein, Bradley E.
影响因子：
7
作者：
Harrow J;Frankish A;Gonzalez JM;Tapanari E;Diekhans M;Kokocinski F;Aken BL;Barrell D;Zadissa A;Searle S;Barnes I;Bignell A;Boychenko V;Hunt T;Kay M;Mukherjee G;Rajan J;Despacio-Reyes G;Saunders G;Steward C;Harte R;Lin M;Howald C;Tanzer A;Derrien T;Chrast J;Walters N;Balasubramanian S;Pei B;Tress M;Rodriguez JM;Ezkurdia I;van Baren J;Brent M;Haussler D;Kellis M;Valencia A;Reymond A;Gerstein M;Guigó R;Hubbard TJ
通讯作者：
Hubbard TJ
影响因子：
64.5
作者：
Lee JS;Smith E;Shilatifard A
通讯作者：
Shilatifard A
影响因子：
56.9
作者：
Golub, TR;Slonim, DK;Lander, ES
通讯作者：
Lander, ES
影响因子：
7
作者：
Mortazavi, Ali;Thompson, Evonne Chen Leeper;Wold, Barbara
通讯作者：
Wold, Barbara