GO-Diff: mining functional differentiation between EST-based transcriptomes.
GO-Diff: mining functional differentiation between EST-based transcriptomes.
复制标题
GO-Diff：挖掘基于 EST 的转录组之间的功能差异
DOI：
10.1186/1471-2105-7-72
复制
发表时间：
2006-02-16
影响因子：
3
通讯作者：
中科院分区：
文献类型：
--
作者：
背景大规模的测序工作产生了数以百万计的表达序列标签（EST），共同代表不同的生化和功能状态。对这些EST文库的分析揭示了差异基因表达，因此EST数据集构成了比较转录组学的宝贵资源。为了将差异表达的基因转化为对潜在生物现象的更好理解，现有的微阵列分析方法通常涉及将基因表达与基因本体（GO）数据库整合以获得可比较的功能谱。然而，方法是不可用的，但处理EST衍生的转录图，使GO为基础的全球功能分析比较transcriptomics在一个高通量mathematics.ResultsHere，我们提出了GO-Diff，GO为基础的功能分析方法对高通量EST为基础的基因表达分析和比较transcriptomics。该软件利用整体基因表达信息，将EST频率转换为GO术语的EST覆盖率。然后测试该比率的统计显著性以揭示比较的转录组之间的差异表示的GO项，并由此推断功能差异。我们证明了该软件的有效性和实用性，通过识别差异代表GO条款在三个应用案例：种内比较;荟萃分析，以测试一个特定的假设;种间比较。GO-Diff的发现与以前的知识一致，并为进一步的发现提供了新的线索。对GO-Diff结果进行的一系列人与鼠组织EST文库比较的综合检验显示，GO-Diff结果的一致性水平是可接受的：人与人之间为61%，鼠与鼠之间为69%;结论GO-Diff是第一个将EST图谱与GO知识库相结合挖掘生物系统间功能差异的软件，例如相同物种的组织或跨物种的相同组织。随着EST资源在公共领域的快速积累和各个实验室测序工作的扩大，GO-Diff作为进行认真的表达研究之前的筛选工具是有用的。
BackgroundLarge-scale sequencing efforts produced millions of Expressed Sequence Tags (ESTs) collectively representing differentiated biochemical and functional states. Analysis of these EST libraries reveals differential gene expressions, and therefore EST data sets constitute valuable resources for comparative transcriptomics. To translate differentially expressed genes into a better understanding of the underlying biological phenomena, existing microarray analysis approaches usually involve the integration of gene expression with Gene Ontology (GO) databases to derive comparable functional profiles. However, methods are not available yet to process EST-derived transcription maps to enable GO-based global functional profiling for comparative transcriptomics in a high throughput manner.ResultsHere we present GO-Diff, a GO-based functional profiling approach towards high throughput EST-based gene expression analysis and comparative transcriptomics. Utilizing holistic gene expression information, the software converts EST frequencies into EST Coverage Ratios of GO Terms. The ratios are then tested for statistical significances to uncover differentially represented GO terms between the compared transcriptomes, and functional differences are thus inferred. We demonstrated the validity and the utility of this software by identifying differentially represented GO terms in three application cases: intra-species comparison; meta-analysis to test a specific hypothesis; inter-species comparison. GO-Diff findings were consistent with previous knowledge and provided new clues for further discoveries. A comprehensive test on the GO-Diff results using series of comparisons between EST libraries of human and mouse tissues showed acceptable levels of consistency: 61% for human-human; 69% for mouse-mouse; 47% for human-mouse.ConclusionGO-Diff is the first software integrating EST profiles with GO knowledge databases to mine functional differentiation between biological systems, e.g. tissues of the same species or the same tissue cross species. With rapid accumulation of EST resources in the public domain and expanding sequencing effort in individual laboratories, GO-Diff is useful as a screening tool before undertaking serious expression studies.