Parking Strategies for Genome Sequencing

Abstract

The parking strategy is an iterative approach to DNA sequencing. Each iteration consists of sequencing a novel portion of target DNA that does not overlap any previously sequenced region. Subject to the constraint of no overlap, each new region is chosen randomly. A parking strategy is often ideal in the early stages of a project for rapidly generating unique data. As a project progresses, parking becomes progressively more expensive and eventually prohibitive. We present a mathematical model with a generalization to allow for overlaps. This model predicts multiple parameters, including progress, costs, and the distribution of gap sizes left by a parking strategy. The highly fragmented nature of the gaps left after an initial parking strategy may make it difficult to finish a project efficiently. Therefore, in addition to our parking model, we model gap closing by walking. Our gap-closing model is generalizable to many other strategies. Our discussion includes modified parking strategies and hybrids with other strategies. A hybrid parking strategy has been employed for portions of the Human Genome Project.A large number and variety of strategies have been proposed and implemented for sequencing large genomes. Both mathematical and simulation models of these strategies are useful in conjunction with large-scale genome projects. These models serve three purposes. First, they allow projects to be planned efficiently, with appropriate allocation of resources, including estimates of project duration. Second, they allow the progress of projects to be monitored. Deviation of an observed parameter, such as target coverage, from its predicted value indicates a technical or biological problem, such as poor-quality data generation or the presence of unclonable regions on the target genome. Third, models allow for cost optimization. A mild increase in cost efficiency can result in tremendous absolute savings, given the overall high cost of large-scale genome sequencing. Costs can be optimized by choosing between alternative sequencing strategies, tuning controllable parameters such as clone-length distribution, and combing strategies to produce hybrid strategies.In this paper, we present a mathematical model for the parking strategy for genome sequencing. This strategy, in combination with other strategies, has been used for portions of the Human Genome Project and may prove popular for future genome projects for organisms with large genomes (Roach et al. 1999;Batzoglou et al. 1999). The name of the parking strategy derives from a mathematically equivalent scenario that has interested mathematicians for over 50 years (Solomon and Weiner 1986). The scenario consists of cars arriving sequentially to park along an infinite unmarked curb. Each car selects a spot along the curb to park with no regard for subsequent cars. As time proceeds, the curb fills. Any gap greater than a car length will eventually be occupied by a car, but if a gap between two cars is created that is less than the length of a car, it will remain forever em...

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