Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, USA.
Genome Med. 2010 Sep 20;2(9):67. doi: 10.1186/gm188.
In the path towards personalized medicine, the integrative bioinformatics infrastructure is a critical enabling resource. Until large-scale reference data became available, the attributes of the computational infrastructure were postulated by many, but have mostly remained unverified. Now that large-scale initiatives such as The Cancer Genome Atlas (TCGA) are in full swing, the opportunity is at hand to find out what analytical approaches and computational architectures are really effective. A recent report did just that: first a software development environment was assembled as part of an informatics research program, and only then was the analysis of TCGA's glioblastoma multiforme multi-omic data pursued at the multi-omic scale. The results of this complex analysis are the focus of the report highlighted here. However, what is reported in the analysis is also the validating corollary for an infrastructure development effort guided by the iterative identification of sound design criteria for the architecture of the integrative computational infrastructure. The work is at least as valuable as the data analysis results themselves: computational ecosystems with their own high-level abstractions rather than rigid pipelines with prescriptive recipes appear to be the critical feature of an effective infrastructure. Only then can analytical workflows benefit from experimentation just like any other component of the biomedical research program.
在迈向个性化医学的道路上，整合生物信息学基础设施是一个关键的使能资源。在大规模参考数据可用之前，许多人推测了计算基础设施的属性，但大多数仍未经证实。现在，像癌症基因组图谱（TCGA）这样的大型计划正在全面展开，现在有机会了解哪些分析方法和计算架构真正有效。最近的一份报告就是这样做的：首先，作为信息学研究计划的一部分，组装了一个软件开发环境，然后才在多组学规模上追求 TCGA 的胶质母细胞瘤多组学数据的分析。这里突出显示的报告重点是这一复杂分析的结果。然而，在分析中报告的内容也是以迭代方式确定整合计算基础设施架构的合理设计标准为指导的基础设施开发工作的验证推论。这项工作至少与数据分析结果本身一样有价值：具有自身高级抽象的计算生态系统，而不是具有规定性配方的刚性管道，似乎是有效基础设施的关键特征。只有这样，分析工作流才能像生物医学研究计划的任何其他组成部分一样受益于实验。
