Model-based probe set optimization for high-performance microarrays.
Model-based probe set optimization for high-performance microarrays.
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DOI：
10.1093/nar/gkn1001
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发表时间：
2009-02
影响因子：
14.9
通讯作者：
Kreil DP
中科院分区：
文献类型：
--
作者：
Leparc GG;Tüchler T;Striedner G;Bayer K;Sykacek P;Hofacker IL;Kreil DP
微阵列设计的一个主要挑战是为所有感兴趣的目标基因选择高度特异性的寡核苷酸探针，同时在杂交温度下保持热力学均匀性。我们引入了一种新的微阵列设计框架（基于热力学模型的Oligo设计优化器，TherMODO），该框架首次结合了许多先进的建模功能：(i)从实验中定量导出的位置相关标记效应模型。（ii）探针结合行为的多态热力学杂化模型，包括潜在的交叉杂化反应。（iii）一种快速校准的基于序列相似性的启发式交叉杂交预测方法，支持大规模设计。（iv）一种新的复合评分公式，用于综合评估多个探针设计目标。与贪婪搜索满足参数阈值的探针相比，这种方法允许在探针集级别上进行优化，并便于在保持探针集一致性的同时选择高度特定的候选探针。(v)最后，灵活的目标分组结构使流水线易于适应各种微阵列应用场景。讨论了该算法及其特点，并在实际设计运行中进行了验证。源代码可按要求提供。
A major challenge in microarray design is the selection of highly specific oligonucleotide probes for all targeted genes of interest, while maintaining thermodynamic uniformity at the hybridization temperature. We introduce a novel microarray design framework (Thermodynamic Model-based Oligo Design Optimizer, TherMODO) that for the first time incorporates a number of advanced modelling features: (i) A model of position-dependent labelling effects that is quantitatively derived from experiment. (ii) Multi-state thermodynamic hybridization models of probe binding behaviour, including potential cross-hybridization reactions. (iii) A fast calibrated sequence-similarity-based heuristic for cross-hybridization prediction supporting large-scale designs. (iv) A novel compound score formulation for the integrated assessment of multiple probe design objectives. In contrast to a greedy search for probes meeting parameter thresholds, this approach permits an optimization at the probe set level and facilitates the selection of highly specific probe candidates while maintaining probe set uniformity. (v) Lastly, a flexible target grouping structure allows easy adaptation of the pipeline to a variety of microarray application scenarios. The algorithm and features are discussed and demonstrated on actual design runs. Source code is available on request.
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影响因子：
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