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Fig. 1 | Genome Medicine

Fig. 1

From: Identification of cis-regulatory mutations generating de novo edges in personalized cancer gene regulatory networks

Fig. 1

Overview of the μ-cisTarget pipeline to predict cis-regulatory mutations. a As input μ-cisTarget takes a gene signature and a list of genomic variations. The gene signature can be derived from the matched transcriptome of the same cancer sample, or can be a general gene signature of the matching cancer subtype. b Motif discovery on the gene signature yields enriched motifs and candidate transcription factors. Motif discovery can be performed using i-cisTarget or iRegulon. c Variations are selected by their proximity (<1 Mb) from the genes in the input gene signature, and are scored with the motifs found under b. d Genes with gains of motifs for cancer type-related factors that are expressed in the sample are added to the inferred gene regulatory network (red edge). An optional filtering step selects only overexpressed cancer-related driver genes as targets (GRN gene regulatory network, TF transcription factor, WGS whole genome sequencing)

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