Zhou Yingyao, Abagyan Ruben
Genomics Institute, Novartis Research Foundation, 10675 John Jay Hopkins Drive, San Diego, CA 92121, USA.
Curr Opin Drug Discov Devel. 2003 May;6(3):339-45.
As a global gene expression-monitoring tool, high-density oligonucleotide arrays (HDAs), accelerate biological and drug discovery processes. Computational considerations affect several key issues, from array design and accurate gene expression level determination to various aspects of the biological analysis of cell functions and gene networks. This review highlights recent progress in ideas and algorithms that are specific to HDAs. Replacing a large number of specific 'mismatch' oligonucleotides by a small number of general noise control oligonucleotides and using dynamic probe-to-gene mapping were some of the ideas proposed. Several novel mismatch-free probe-to-gene condensation algorithms have demonstrated the ability to take advantage of such designs, and provide higher density and equivalent or better calculation accuracy than previous algorithms. Algorithms to be developed in the future should significantly increase both the quality and quantity of information revealed by a single array; therefore, a single array containing the whole mammalian genome or even multiple genomes could soon become a standard tool.
作为一种全球基因表达监测工具，高密度寡核苷酸阵列（HDAs）加速了生物学和药物发现进程。计算方面的考量影响着几个关键问题，从阵列设计、准确的基因表达水平测定到细胞功能和基因网络生物学分析的各个方面。本综述重点介绍了高密度寡核苷酸阵列特有的思想和算法方面的最新进展。提出了一些想法，比如用少量通用的噪声控制寡核苷酸取代大量特定的“错配”寡核苷酸，以及使用动态探针到基因的映射。几种新颖的无错配探针到基因浓缩算法已证明能够利用此类设计，并提供比以前的算法更高的密度和同等或更好的计算精度。未来要开发的算法应能显著提高单个阵列所揭示信息的质量和数量；因此，包含整个哺乳动物基因组甚至多个基因组的单个阵列可能很快会成为标准工具。
