GEO DataSets

(Submitter supplied) In the era of GWAS and personalized medicine systematically deciphering the impact of non-coding sequence variation on the regulatory genome is a critical bottleneck. Here we present Sasquatch a novel computational approach to estimate and visualize the effects of noncoding-variants on transcription factor (TF) binding using DNase I footprint data. Developed to provide an unbiased approach to prioritise non-coding variants for functional analysis, Sasquatch performs exhaustive, k-mer based analysis of average footprints to determine any k-mers potential for TF binding and quantifies how this is changed by sequence variants. more...

Organism:

Homo sapiens; Mus musculus

Type:

Genome binding/occupancy profiling by high throughput sequencing

Platforms:

GPL18573 GPL19057 GPL15520

6 Samples

Download data: BEDGRAPH, WIG