Bioinformatics Group, Department of Computer Science and Interdisciplinary Center for Bioinformatics, University of Leipzig, Leipzig, Germany.
Bioinformatics. 2012 Jun 1;28(11):1471-9. doi: 10.1093/bioinformatics/bts142. Epub 2012 Apr 6.
Tiling arrays have been a mainstay of unbiased genome-wide transcriptomics over the last decade. Currently available approaches to identify expressed or differentially expressed segments in tiling array data are limited in the recovery of the underlying gene structures and require several parameters that are intensity-related or partly dataset-specific.
We have developed TileShuffle, a statistical approach that identifies transcribed and differentially expressed segments as significant differences from the background distribution while considering sequence-specific affinity biases and cross-hybridization. It avoids dataset-specific parameters in order to provide better comparability of different tiling array datasets, based on different technologies or array designs. TileShuffle detects highly and differentially expressed segments in biological data with significantly lower false discovery rates under equal sensitivities than commonly used methods. Also, it is clearly superior in the recovery of exon-intron structures. It further provides window z-scores as a normalized and robust measure for visual inspection.
The R package including documentation and examples is freely available at http://www.bioinf.uni-leipzig.de/Software/TileShuffle/
在过去十年中，平铺阵列一直是无偏全基因组转录组学的主要方法。目前，用于识别平铺阵列数据中表达或差异表达片段的方法在恢复潜在基因结构方面存在局限性，并且需要与强度相关或部分与数据集特定的几个参数。
我们开发了 TileShuffle，这是一种统计方法，它通过考虑序列特异性亲和力偏差和交叉杂交，将转录和差异表达片段识别为与背景分布的显著差异。它避免了数据集特定的参数，以便基于不同的技术或阵列设计，提供不同的平铺阵列数据集之间更好的可比性。TileShuffle 在生物数据中以显著更低的假发现率检测到高度和差异表达的片段，而在相同灵敏度下比常用方法具有更好的检测效果。此外，它在恢复外显子-内含子结构方面明显具有优势。它还提供窗口 z 分数作为一种归一化和稳健的视觉检查措施。
包括文档和示例的 R 包可在 http://www.bioinf.uni-leipzig.de/Software/TileShuffle/ 上免费获得。
