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Identification of C2H2-ZF binding preferences from ChIP-seq data using RCADE

机译:使用RCADE从ChIP-seq数据中识别C2H2-ZF结合偏好

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摘要

Current methods for motif discovery from chromatin immunoprecipitation followed by sequencing (ChIP-seq) data often identify non-targeted transcription factor (TF) motifs, and are even further limited when peak sequences are similar due to common ancestry rather than common binding factors. The latter aspect particularly affects a large number of proteins from the Cys(2)His(2) zinc finger (C2H2-ZF) class of TFs, as their binding sites are often dominated by endogenous retroelements that have highly similar sequences. Here, we present recognition code-assisted discovery of regulatory elements (RCADE) for motif discovery from C2H2-ZF ChIP-seq data. RCADE combines predictions from a DNA recognition code of C2H2-ZFs with ChIP-seq data to identify models that represent the genuine DNA binding preferences of C2H2-ZF proteins. We show that RCADE is able to identify generalizable binding models even from peaks that are exclusively located within the repeat regions of the genome, where state-of-the-art motif finding approaches largely fail.
机译:从染色质免疫沉淀后再测序(ChIP-seq)数据中发现基序的当前方法通常可识别非靶向转录因子(TF)基序,当峰序列由于共同祖先而不是共同结合因子而相似时,甚至会受到进一步限制。后一个方面特别影响来自Cys(2)His(2)锌指(C2H2-ZF)类TF的大量蛋白质,因为它们的结合位点通常由具有高度相似序列的内源性逆转录元件控制。在这里,我们介绍从C2H2-ZF ChIP-seq数据进行基序发现的识别代码辅助的调控元件发现(RCADE)。 RCADE将来自C2H2-ZFs的DNA识别码的预测与ChIP-seq数据相结合,以鉴定代表C2H2-ZF蛋白真正的DNA结合偏好的模型。我们表明RCADE甚至可以从仅位于基因组重复区域内的峰中识别出可概括的结合模型,在这些峰中,现有技术的基元发现方法大都失败了。

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