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Fig. 1 | BMC Bioinformatics

Fig. 1

From: Self-organizing maps with variable neighborhoods facilitate learning of chromatin accessibility signal shapes associated with regulatory elements

Fig. 1

The SOM-VN workflow learns chromatin accessibility signal shapes and associates them with REs. a SOM-VN learns signal shapes from input DNase-seq signal which are then assigned to RE-associated chromatin states (e.g. promoter, enhancer, weak). b To learn shapes on each chromosome, SOM-VN uses an iterative training process which operates on a grid of nodes, where each node comprises one shape. Dotted circles represent the neighborhood of each node in an iteration. c Normalized DNase-seq signal segmented into regions is used as input to the SOM-VN training process. The learned shapes are then associated with ChromHMM annotations from Roadmap Epigenomics

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BMC Bioinformatics

ISSN: 1471-2105

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