QuEST

QuEST identifies protein–DNA interaction sites in ChIP-seq data using kernel density estimation to map transcription factor binding with high resolution.

Key Features:

• Kernel Density Estimation: Employs a statistical framework based on kernel density estimation to analyze ChIP-seq tag distributions and determine positions of protein–DNA interactions.
• Quantitative Enrichment of Sequence Tags (QuEST): Implements the QuEST methodology to detect binding sites with high resolution (reported average resolution ~20 base pairs) and has identified several thousand binding sites for human transcription factors such as SRF, GABP, and NRSF.
• Integration with Motif Discovery Tools: Supports downstream analysis with motif-discovery tools such as MEME to reveal DNA-binding activities of cofactors and infer binding specificity from identified sites.

Scientific Applications:

• Transcription Factor Binding Site Identification: Maps binding sites for transcription factors from ChIP-seq data to characterize protein–DNA interactions across the genome.
• Cofactor Interaction Analysis: Identifies putative cofactors associated with transcription factors by integrating motif discovery results with ChIP-seq peaks.
• Functional Inference through GO Annotations: Facilitates inference of transcription factor functions by integrating QuEST results with Gene Ontology (GO) annotations and gene expression data.

Methodology:

Quantitative enrichment of sequence tags using kernel density estimation; integration of peak results with motif-discovery tools (e.g., MEME); and integration of QuEST analyses with GO annotations and gene expression data.