Co-expression networks reveal the tissue-specific regulation of transcription and splicing
Co-expression networks reveal the tissue-specific regulation of transcription and splicing
复制标题
DOI：
10.1101/gr.216721.116
复制
发表时间：
2017-11-01
期刊：
影响因子：
7
通讯作者：
Battle, Alexis
中科院分区：
文献类型：
--
作者：
Saha, Ashis;Kim, Yungil;Battle, Alexis
基因共表达网络捕捉了基因表达数据中重要的生物学模式，使基因的功能分析、生物标记物的发现和遗传变异的解释成为可能。到目前为止，大多数网络分析仅限于评估单个组织或小组组织中总基因表达水平之间的相关性。在这里，我们建立了额外的网络，捕捉相对异构体丰度和剪接的调节，以及一组不同组织中每个组织特有的组织特异性连接。我们使用了基因类型-组织表达(GTEx)项目V6 RNA测序数据，数据涉及50个组织和449个个体。首先，我们开发了一个名为Transcriptome-wide Networks(TWNS)的框架，用于将总表达和相对异构体水平结合到一个稀疏网络中，捕获剪接和转录调节之间的相互作用。我们为16个组织建立了TWN，发现这些网络中的枢纽强烈富含剪接和RNA结合基因，证明了它们在解开人类转录组中剪接调控的有效性。接下来，我们使用贝叶斯双聚类模型来识别单个组织特有的网络边缘，以重建26个不同组织和10组相关组织的组织特异性网络(TSN)。最后，我们发现了与我们网络中的相邻节点对相关的遗传变异，支持估计的网络结构，并识别了20个对转录和剪接具有远程调控影响的遗传变异。我们的网络提供了对人类转录组跨组织复杂关系的更好理解。
Gene co-expression networks capture biologically important patterns in gene expression data, enabling functional analyses of genes, discovery of biomarkers, and interpretation of genetic variants. Most network analyses to date have been limited to assessing correlation between total gene expression levels in a single tissue or small sets of tissues. Here, we built networks that additionally capture the regulation of relative isoform abundance and splicing, along with tissue-specific connections unique to each of a diverse set of tissues. We used the Genotype-Tissue Expression (GTEx) project v6 RNA sequencing data across 50 tissues and 449 individuals. First, we developed a framework called Transcriptome-Wide Networks (TWNs) for combining total expression and relative isoform levels into a single sparse network, capturing the interplay between the regulation of splicing and transcription. We built TWNs for 16 tissues and found that hubs in these networks were strongly enriched for splicing and RNA binding genes, demonstrating their utility in unraveling regulation of splicing in the human transcriptome. Next, we used a Bayesian biclustering model that identifies network edges unique to a single tissue to reconstruct Tissue-Specific Networks (TSNs) for 26 distinct tissues and 10 groups of related tissues. Finally, we found genetic variants associated with pairs of adjacent nodes in our networks, supporting the estimated network structures and identifying 20 genetic variants with distant regulatory impact on transcription and splicing. Our networks provide an improved understanding of the complex relationships of the human transcriptome across tissues.