Roach J C, Thorsson V, Siegel A F
The Institute for Systems Biology, Seattle, Washington 98105 USA.
Genome Res. 2000 Jul;10(7):1020-30. doi: 10.1101/gr.10.7.1020.
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.
停车策略是一种用于DNA测序的迭代方法。每次迭代包括对目标DNA的一个新部分进行测序，该部分不与任何先前测序的区域重叠。在不重叠的约束条件下，每个新区域是随机选择的。停车策略在项目的早期阶段通常是理想的，用于快速生成独特的数据。随着项目的推进，停车变得越来越昂贵，最终变得令人望而却步。我们提出了一个具有泛化功能的数学模型，以允许重叠。该模型预测多个参数，包括进展、成本以及停车策略留下的间隙大小的分布。初始停车策略后留下的间隙高度碎片化的性质可能使得难以有效地完成一个项目。因此，除了我们的停车模型外，我们还对通过步移来封闭间隙进行建模。我们的间隙封闭模型可推广到许多其他策略。我们的讨论包括改进的停车策略以及与其他策略的混合策略。人类基因组计划的部分工作采用了混合停车策略。
