Analysis of sequence-tagged-connector strategies for DNA sequencing

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• The sequence-tagged-connector (STC) approach, or BAC-end sequencing, uses genome-wide sequence tags to assemble contiguous DNA sequences.
• Identifying overlapping clones is crucial for efficient genome sequencing, but repeat regions and imperfect technology can complicate this process.

Purpose of the Study:

• To mathematically model STC-sequencing strategies to assess their efficiency and cost-effectiveness.
• To identify optimal parameters for minimizing 'problem clones' and overall sequencing expenses.

Main Methods:

• Development of a mathematical model simulating genome sequencing with homologous repeats and imperfect sequencing.
• Analysis of parameters influencing problem clone incidence and project costs.
• Estimation of clone numbers and optimization of the sequence similarity threshold for declaring overlaps.

Main Results:

• The study identifies optimal clone library redundancy and insert sizes for various genome scales.
• For a 3 Gb genome with significant repeats, fewer than 10 problem clones are expected with 15x coverage using 150-kb clones.
• Optimizing the overlap decision rule significantly impacts total cost and problem clone numbers.

Conclusions:

• STC resource generation constitutes a small fraction (1-3%) of total human genome sequencing costs.
• Increased STC resources generally reduce total sequencing costs up to a point of diminishing returns.
• The STC approach provides a cost-effective strategy for identifying minimally overlapping clones in large-scale genome sequencing projects.