Gibbs Motif Sampler

Gibbs Motif Sampler identifies conserved sequence motifs, particularly transcription factor binding sites (TFBSs), within collections of unaligned DNA sequences to characterize regulatory elements such as cis-regulatory modules (CRMs).

Key Features:

• Gibbs Recursive Sampler: Implements the Gibbs Recursive Sampler variation to enable simultaneous identification of multiple TFBSs in unaligned DNA sequences.
• Iterative sampling algorithm: Employs an iterative Gibbs-sampling-based algorithm to detect subtle local residue patterns common across multiple sequences.
• Heterogeneous DNA composition handling: Accommodates heterogeneous DNA compositions during motif discovery.
• Phylogenetic footprinting: Incorporates phylogenetic footprinting to enhance prediction of transcription regulatory sites via cross-species comparison.
• Local multiple alignment (N-linear time): Performs local multiple alignment in N-linear time to detect and optimize multiple patterns and pattern repeats simultaneously.
• De novo CRM identification: Contains a Gibbs-sampling-based algorithm to locate cis-regulatory modules de novo and identify component TFBSs and their spatial distribution properties.
• Genome-scale application: Has been applied to genome-scale datasets and reported identification of novel candidate modules with low false discovery rates.
• Regulatory sequence discrimination: Demonstrates capability to discriminate regulatory sequences specific to muscle genes.

Scientific Applications:

• Transcriptional regulatory network analysis: Identifies TFBSs and motifs to support reconstruction of transcriptional regulatory networks across organisms.
• Cross-species regulatory site prediction: Uses phylogenetic footprinting to predict transcription regulatory sites in genomes such as gamma proteobacteria, including Escherichia coli.
• Genome-scale CRM discovery: Applied to whole-genome datasets to discover novel candidate cis-regulatory modules with low false discovery rates.
• CRM and TFBS characterization in multicellular organisms: Delineates regulatory regions and binding specificities in complex genomes, including humans.
• Muscle gene regulatory studies: Enables discrimination and analysis of regulatory sequences specific to muscle genes.
• Motif and repeat detection in large datasets: Detects and optimizes multiple motif patterns and repeats within large sequence collections.

Methodology:

Uses Gibbs sampling (including the Gibbs Recursive Sampler variation), an iterative sampling algorithm, local multiple alignment in N-linear time, and phylogenetic footprinting.