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The ChIP-seq technique enables genome-wide mapping of in vivo protein-DNA interactions and chromatin states. Current analytical approaches for ChIP-seq analysis are largely geared towards single-sample investigations, and have limited applicability in comparative settings that aim to identify combinatorial patterns of enrichment across multiple datasets. We describe a novel probabilistic method, jMOSAiCS, for jointly analyzing multiple ChIP-seq datasets. We demonstrate its usefulness with a wide range of data-driven computational experiments and with a case study of histone modifications on GATA1-occupied segments during erythroid differentiation. jMOSAiCS is open source software and can be downloaded from Bioconductor [1].  相似文献   

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We developed a computational procedure for optimizing the binding site detections in a given ChIP-seq experiment by maximizing their reproducibility under bootstrap sampling. We demonstrate how the procedure can improve the detection accuracies beyond those obtained with the default settings of popular peak calling software, or inform the user whether the peak detection results are compromised, circumventing the need for arbitrary re-iterative peak calling under varying parameter settings. The generic, open-source implementation is easily extendable to accommodate additional features and to promote its widespread application in future ChIP-seq studies. The peakROTS R-package and user guide are freely available at http://www.nic.funet.fi/pub/sci/molbio/peakROTS.  相似文献   

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ClusterDraw is a program aimed to identification of binding sites and binding-site clusters. Major difference of the ClusterDraw from existing tools is its ability to scan a wide range of parameter values and weigh statistical significance of all possible clusters, smaller than a selected size. The program produces graphs along with decorated FASTA files. ClusterDraw web server is available at the following URL: http://flydev.berkeley.edu/cgi-bin/cld/submit.cgi  相似文献   

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