Abstract
Histone modifications play important roles in gene regulation, heredity, imprinting, and many human diseases. The histone code is complex, consisting of about 100 marks. Biologists need computational tools for characterizing general signatures representing the distributions of tens of chromatin marks around thousands of regions. To this end, we developed a software tool called HebbPlot, which utilizes a Hebbian neural network to learn such signatures. HebbPlot presents a signature as a digitized image, which can be easily interpreted. We validated HebbPlot in six case studies. HebbPlot is applicable to a wide array of studies, facilitating the deciphering of the histone code.
Copyright
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