Electronic PCR

Electronic PCR identifies sequence-tagged sites (STSs) in DNA sequences by searching for subsequences that match PCR primers in order, orientation, and spacing to support genomic mapping and integration of genetic and physical maps.

Key Features:

• STS identification: Detects STSs by locating primer-matching subsequences with specified order, orientation, and inter-primer spacing.
• Mismatch and gap tolerance: Handles mismatches and gaps between primer sequences and target DNA to improve sensitivity while maintaining specificity.
• Discontinuous-word indexing: Extracts overlapping discontinuous words from the 3' ends of primers and stores them in a sorted hash table for rapid access during searches.
• Search modes: Supports querying an STS against a sequence database and querying a sequence against an STS database.
• Scalable search strategy: Implements an efficient search strategy for large-scale searches across extensive genomic databases.
• Map integration: Facilitates construction and integration of genetic and physical maps and correlation of genetic and physical distances using STS placements.
• Database linkage: Operates with resources such as UniSTS and MapViewer for STS and mapping data.

Scientific Applications:

• Genomic mapping: Locating STSs to construct and refine genetic and physical maps of genomes, including the human genome.
• Marker integration: Integrating new sequence data with existing maps and revealing relationships among previously separately mapped markers.
• Distance correlation: Correlating genetic distances with physical distances through STS placement.
• Large-scale genome analysis: Enabling high-throughput searches of genomic databases for marker localization and map construction.

Methodology:

Searches for subsequences matching PCR primers by order, orientation, and spacing; extracts overlapping discontinuous words from the primers' 3' ends and indexes them in a sorted hash table; tolerates mismatches and gaps; provides two search modes (STS→sequence and sequence→STS) and employs an efficient search strategy for large genomic databases.