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A Model-Based Approach to Identify Binding Sites in CLIP-Seq Data

机译：一种基于模型的方法来识别CLIP-Seq数据中的结合位点

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摘要

Cross-linking immunoprecipitation coupled with high-throughput sequencing (CLIP-Seq) has made it possible to identify the targeting sites of RNA-binding proteins in various cell culture systems and tissue types on a genome-wide scale. Here we present a novel model-based approach (MiClip) to identify high-confidence protein-RNA binding sites from CLIP-seq datasets. This approach assigns a probability score for each potential binding site to help prioritize subsequent validation experiments. The MiClip algorithm has been tested in both HITS-CLIP and PAR-CLIP datasets. In the HITS-CLIP dataset, the signaloise ratios of miRNA seed motif enrichment produced by the MiClip approach are between 17% and 301% higher than those by the ad hoc method for the top 10 most enriched miRNAs. In the PAR-CLIP dataset, the MiClip approach can identify ∼50% more validated binding targets than the original ad hoc method and two recently published methods. To facilitate the application of the algorithm, we have released an R package, MiClip ( ), and a public web-based graphical user interface software () for customized analysis.

机译：交联免疫沉淀与高通量测序（CLIP-Seq）结合在一起，使得在全基因组范围内鉴定各种细胞培养系统和组织类型中RNA结合蛋白的靶向位点成为可能。在这里，我们提出了一种新颖的基于模型的方法（MiClip），用于从CLIP-seq数据集中识别高可信度的蛋白质-RNA结合位点。该方法为每个潜在的结合位点分配概率得分，以帮助确定后续验证实验的优先级。 MiClip算法已在HITS-CLIP和PAR-CLIP数据集中进行了测试。在HITS-CLIP数据集中，通过MiClip方法产生的miRNA种子基序富集的信噪比比通过ad hoc方法产生的前10个最富集的miRNA高出17％至301％。在PAR-CLIP数据集中，与原始的ad hoc方法和两个最近发布的方法相比，MiClip方法可识别约50％的有效结合靶标。为了促进算法的应用，我们发布了R包，MiClip（）和基于Web的公共图形用户界面软件（），用于自定义分析。

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期刊名称 other
作者
Tao Wang; Beibei Chen; MinSoo Kim; Yang Xie; Guanghua Xiao;
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年(卷),期 -1(9),4
年度 -1
页码 e93248
总页数 11
原文格式 PDF
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专利

1. A deep boosting based approach for capturing the sequence binding preferences of RNA-binding proteins from high-throughput CLIP-seq data [J] . Tao Jiang, Fanghong Dong, Chen Zhang, Nucleic acids research . 2017,第14期

机译：基于深度增强的方法，可从高通量CLIP-seq数据中捕获RNA结合蛋白的序列结合偏好
2. A deep boosting based approach for capturing the sequence binding preferences of RNA-binding proteins from high-throughput CLIP-seq data [J] . Li Shuya, Dong Fanghong, Wu Yuexin, European Review of Agricultural Economics . 2017,第4期

机译：基于深度促进基于高吞吐量剪辑-SEQ数据的RNA结合蛋白序列结合偏好的方法
3. A mixture model-based discriminate analysis for identifying ordered transcription factor binding site pairs in gene promoters directly regulated by estrogen receptor-alpha [J] . Li L, Cheng ASL, Jin VX, Bioinformatics . 2006,第18期

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4. A deep neural network approach using distributed representations of RNA sequence and structure for identifying binding site of RNA-binding proteins [C] . Lei Deng, Youzhi Liu, Yechuan Shi, IEEE International Conference on Bioinformatics and Biomedicine . 2019

机译：使用RNA序列和结构的分布式表示的深层神经网络方法，用于识别RNA结合蛋白的结合位点
5. A Multidisciplinary Approach for Identifying Stage-specific Transcription Factor Binding Sites in the Irish Potato Famine Pathogen, Phytophthora Infestans [D] . Roy, Sourav 2011

机译：在爱尔兰马铃薯饥荒病原体疫霉疫中鉴定阶段特异性转录因子结合位点的多学科方法。
6. A deep boosting based approach for capturing the sequence binding preferences of RNA-binding proteins from high-throughput CLIP-seq data [O] . Shuya Li, Fanghong Dong, Yuexin Wu, 2017

机译：一种基于深度增强的方法可从高通量CLIP-seq数据中捕获RNA结合蛋白的序列结合偏好
7. A model-based approach to identify binding sites in CLIP-Seq data. [O] . Tao Wang, Beibei Chen, MinSoo Kim, 2014

机译：基于模型的方法，用于识别CLIp-seq数据中的绑定站点。

A Model-Based Approach to Identify Binding Sites in CLIP-Seq Data

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