Institute for Bioinformatics and Evolutionary Studies, University of Idaho, Moscow, ID 83844-3051, USA.
Mol Ecol Resour. 2012 Nov;12(6):1079-89. doi: 10.1111/1755-0998.12006. Epub 2012 Sep 12.
High-throughput microarray experiments often generate far more biological information than is required to test the experimental hypotheses. Many microarray analyses are considered finished after differential expression and additional analyses are typically not performed, leaving untapped biological information left undiscovered. This is especially true if the microarray experiment is from an ecological study of multiple populations. Comparisons across populations may also contain important genomic polymorphisms, and a subset of these polymorphisms may be identified with microarrays using techniques for the detection of single feature polymorphisms (SFP). SFPs are differences in microarray probe level intensities caused by genetic polymorphisms such as single-nucleotide polymorphisms and small insertions/deletions and not expression differences. In this study, we provide a new algorithm for the detection of SFPs, evaluate the algorithm using existing data from two publicly available Affymetrix Barley (Hordeum vulgare) microarray data sets and compare them to two previously published SFP detection algorithms. Results show that our algorithm provides more consistent and sensitive calling of SFPs with a lower false discovery rate. Simultaneous analysis of SFPs and differential expression is a low-cost method for the enhanced analysis of microarray data, enabling additional biological inferences to be made.
高通量微阵列实验通常会产生远远超出测试实验假设所需的生物信息。许多微阵列分析在差异表达和其他分析完成后就被认为已经结束，未被发掘的生物信息仍然没有被发现。如果微阵列实验是对多个群体的生态研究，情况尤其如此。群体之间的比较也可能包含重要的基因组多态性，其中一部分多态性可以使用用于检测单特征多态性（SFP）的技术来确定。SFP 是由遗传多态性（如单核苷酸多态性和小插入/缺失）引起的微阵列探针水平强度的差异，而不是表达差异。在这项研究中，我们提供了一种用于检测 SFP 的新算法，使用来自两个公开可用的 Affymetrix 大麦（Hordeum vulgare）微阵列数据集的现有数据评估该算法，并将其与两种以前发表的 SFP 检测算法进行比较。结果表明，我们的算法在具有更低假阳性率的情况下提供了更一致和敏感的 SFP 调用。同时分析 SFP 和差异表达是一种低成本的微阵列数据分析增强方法，可以做出更多的生物学推断。
