TFscope




Characterizing the binding preferences of transcription factors (TFs) in different cell types and conditions is key to understand how they orchestrate gene expression. TFscope is a machine learning approach that identifies sequence features explaining the binding differences observed between two ChIP-seq experiments targeting either the same TF in two conditions or two TFs with similar motifs (paralogous TFs). TFscope systematically investigates differences in the core motif, nucleotide environment and co-factor motifs, and provides the contribution of each key feature in the two experiments.


TFscope online execution

Input data
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BED file for the first class (Chip1).
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BED file for the second class (Chip2).
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Genome FASTA file.
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Motif collection in MEME format (.meme).
Parameters
Identifier of the targeted motif in the MEME file (ex. MA0007.1). Optional.
Base pairs added upstream and downstream in peaks to find the best occurrence.
Base pairs added upstream and downstream in the motif (DM).
Number of bins in lattice (DExTER and TFscope).
P-value threshold for FIMO when scanning motifs.
Sets of sequences to compare.
TFscope TFscope TFscope online

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Sequence analysis Software engineering Service discovery Service invocation Service management Job identifier Tool metadata HTML JSON