SamSelect

SamSelect selects representative subsets of DNA sequences to accelerate quorum planted motif search (qPMS) for motif discovery in large-scale datasets such as ChIP-seq.

Key Features:

• Sample Sequence Selection: Selects a subset D' from a large input dataset D characterized by a reduced sequence count t and an increased quorum threshold q.
• Optimization for Performance: Analyzes the impact of sequence count (t) and quorum threshold (q) on computation time and adjusts selection to avoid large t or small q that increase runtime.
• Efficient Motif Discovery: Enables execution of existing qPMS algorithms on D' to identify l-length strings occurring in at least qt input sequences with up to d mismatches, facilitating discovery of motifs such as transcription factor binding sites.
• Experimental Validation: Validated on simulated and real datasets, demonstrating reduced computation time for motif discovery when using D' versus the full dataset D.
• Approximate Algorithm Approach: Employs an approximate algorithmic approach that focuses motif search on a representative subset rather than the entire dataset.

Scientific Applications:

• Transcription factor binding site identification: Facilitates discovery of transcription factor binding sites within large DNA sequence collections.
• ChIP-seq analysis scalability: Improves scalability of qPMS-based motif searches for high-throughput datasets such as ChIP-seq.
• Gene regulation studies: Supports analyses that inform gene regulation mechanisms through efficient motif discovery.

Methodology:

Selects a subset D' from D by reducing t and increasing q, analyzes the effects of t and q on computation time, and applies existing qPMS algorithms to D' to identify l-length strings present in at least qt sequences with up to d mismatches using an approximate algorithmic approach.