BCRANK

BCRANK predicts transcription factor binding consensus sequences and identifies regulatory single nucleotide polymorphisms (rSNPs) in non-coding genomic regions using heuristic motif-finding applied to chromatin immunoprecipitation microarray (ChIP-chip) data.

Key Features:

• Motif Finding Algorithm: Employs a heuristic motif-finding algorithm to analyze whole-genome chromatin immunoprecipitation combined with microarray (ChIP-chip) data and identify transcription factor binding sites at base-pair resolution.
• Integration of Computational and Experimental Approaches: Combines computational predictions with sequence analysis techniques such as haploChIP to support identification and characterization of rSNPs.
• Identification of Candidate rSNPs: Has been applied to USF1 ChIP-chip data to detect 1,754 binding sites and nominate 140 candidate regulatory SNPs within those regions.
• Validation of Regulatory Function: Predicts SNPs affecting transcription factor binding that have been validated by preferential binding assays.
• In Vivo Data Utilization: Bases predictions and validations on in vivo data to reflect actual cellular conditions.
• Application to Complex Disorders: Identifies rSNPs in regulatory regions to aid in pinpointing causative variants associated with complex disorders.

Scientific Applications:

• Transcriptional Regulation Studies: Mapping transcription factor binding sites and consensus motifs to study transcriptional regulation and the functional impact of non-coding variation.
• Disease Variant Prioritization: Prioritizing candidate regulatory variants (rSNPs) for functional follow-up in studies of complex genetic disorders.

Methodology:

Applies heuristic search motif-finding algorithms to whole-genome ChIP-chip datasets and sequence analysis approaches including haploChIP for prediction of transcription factor binding sites and candidate rSNPs.