Hanauer David A, Rhodes Daniel R, Sinha-Kumar Chandan, Chinnaiyan Arul M
Room 6303 CCGC, 1500 E Medical Center Dr, Ann Arbor, MI 48109-0942, USA.
Curr Mol Med. 2007 Feb;7(1):133-41. doi: 10.2174/156652407779940431.
A revolution is underway in the approach to studying the genetic basis of cancer. Massive amounts of data are now being generated via high-throughput techniques such as DNA microarray technology and new computational algorithms have been developed to aid in analysis. At the same time, standards-based repositories, including the Stanford Microarray Database and the Gene Expression Omnibus have been developed to store and disseminate the results of microarray experiments. Bioinformatics, the convergence of biology, information science, and computation, has played a key role in these developments. Recently developed techniques include Module Maps, SLAMS (Stepwise Linkage Analysis of Microarray Signatures), and COPA (Cancer Outlier Profile Analysis). What these techniques have in common is the application of novel algorithms to find high-level gene expression patterns across heterogeneous microarray experiments. Large-scale initiatives are underway as well. The Cancer Genome Atlas (TCGA) project is a logical extension of the Human Genome Project and is meant to produce a comprehensive atlas of genetic changes associated with cancer. The Cancer Biomedical Informatics Grid (caBIG), led by the NCI, also represents a colossal initiative involving virtually all aspects of cancer research and may help to transform the way cancer research is conducted and data are shared.
癌症遗传基础的研究方法正在经历一场变革。现在，通过DNA微阵列技术等高通量技术正在生成海量数据，并且已经开发出了新的计算算法来辅助分析。与此同时，已经开发出了基于标准的储存库，包括斯坦福微阵列数据库和基因表达综合数据库，用于存储和传播微阵列实验的结果。生物信息学，即生物学、信息科学和计算的融合，在这些发展中发挥了关键作用。最近开发的技术包括模块图谱、SLAMS（微阵列特征逐步连锁分析）和COPA（癌症异常值分析）。这些技术的共同之处在于应用新颖的算法来在异质微阵列实验中寻找高水平的基因表达模式。大规模的计划也正在进行中。癌症基因组图谱（TCGA）项目是人类基因组计划的合理延伸，旨在生成与癌症相关的遗传变化的综合图谱。由美国国立癌症研究所牵头的癌症生物医学信息网格（caBIG），也代表了一项涉及癌症研究几乎所有方面的庞大计划，可能有助于改变癌症研究的开展方式和数据共享方式。
