Modifying SNEEP to account for averaged differential TF Binding
Extending SNEEP's statistic to account for cumulative, not just maximal, TF binding effects at regulatory SNVs.
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Incorporating deep learning TF models into SNEEP
Adapt SNEEP with BPNet models for TF binding prediction
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Placing regulatory variants into tissue context
Map regulatory SNVs to spatial transcriptomics data
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Exploring the regulatory impact of non-coding genetic variants
Overview SNEEP is a computational method to identify regulatory Single Nucleotide Variants (rSNVs) that alter the binding sites of Transcription Factors (TFs). It can efficiently …
