Levitsky V G, Tsukanov A V, Merkulova T I
Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia Novosibirsk State University, Novosibirsk, Russia.
Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia.
Vavilovskii Zhurnal Genet Selektsii. 2024 Dec;28(8):822-833. doi: 10.18699/vjgb-24-90.
ChIP-seq technology, which is based on chromatin immunoprecipitation (ChIP), allows mapping a set of genomic loci (peaks) containing binding sites (BS) for the investigated (target) transcription factor (TF). A TF may recognize several structurally different BS motifs. The multiprotein complex mapped in a ChIP-seq experiment includes target and other "partner" TFs linked by protein-protein interactions. Not all these TFs bind to DNA directly. Therefore, both target and partner TFs recognize enriched BS motifs in peaks. A de novo search approach is used to search for enriched TF BS motifs in ChIP-seq data. For a pair of enriched BS motifs of TFs, the co-occurrence or mutually exclusive occurrence can be detected from a set of peaks: the co-occurrence reflects a more frequent occurrence of two motifs in the same peaks, while the mutually exclusive means their more frequent detection in different peaks. We propose the MetArea software package to identify pairs of TF BS motifs with the mutually exclusive occurrence in ChIP-seq data. MetArea was designed to predict the structural diversity of BS motifs of the same TFs, and the functional relation of BS motifs of different TFs. The functional relation of the motifs of the two distinct TFs presumes that they are interchangeable as part of a multiprotein complex that uses the BS of these TFs to bind directly to DNA in different peaks. MetArea calculates the estimates of recognition performance pAUPRC (partial area under the Precision-Recall curve) for each of the two input single motifs, identifies the "joint" motif, and computes the performance for it too. The goal of the analysis is to find pairs of single motifs A and B for which the accuracy of the joint A&B motif is higher than those of both single motifs.
染色质免疫沉淀测序（ChIP-seq）技术基于染色质免疫沉淀（ChIP），可用于绘制一组基因组位点（峰），这些位点包含所研究（靶标）转录因子（TF）的结合位点（BS）。一个转录因子可能识别几种结构不同的BS基序。在ChIP-seq实验中绘制的多蛋白复合物包括通过蛋白质-蛋白质相互作用连接的靶标和其他“伙伴”转录因子。并非所有这些转录因子都直接与DNA结合。因此，靶标转录因子和伙伴转录因子都能识别峰中富集的BS基序。一种从头搜索方法用于在ChIP-seq数据中搜索富集的TF BS基序。对于一对TF的富集BS基序，可以从一组峰中检测到共出现或互斥出现：共出现反映了两个基序在同一峰中更频繁出现，而互斥意味着它们在不同峰中更频繁被检测到。我们提出了MetArea软件包，用于识别ChIP-seq数据中互斥出现的TF BS基序对。MetArea旨在预测同一转录因子的BS基序的结构多样性，以及不同转录因子的BS基序的功能关系。两个不同转录因子基序的功能关系假定它们作为多蛋白复合物的一部分是可互换的，该复合物利用这些转录因子的BS直接结合不同峰中的DNA。MetArea计算两个输入单基序中每个基序的识别性能pAUPRC（精确召回曲线下的部分面积）估计值，识别“联合”基序，并计算其性能。分析的目标是找到单基序A和B的对，对于这些对，联合A&B基序的准确性高于两个单基序。
