A hidden Markov model approach for determining expression from genomic tiling micro arrays.
A hidden Markov model approach for determining expression from genomic tiling micro arrays.
复制标题
一种隐藏的马尔可夫模型方法，用于确定基因组瓷砖微阵列的表达。
DOI：
10.1186/1471-2105-7-239
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发表时间：
2006-05-03
影响因子：
3
通讯作者：
Krogh A
中科院分区：
文献类型：
--
作者：
Munch K;Gardner PP;Arctander P;Krogh A
基因组镶嵌微阵列具有很大的潜力，以确定以前未发现的编码以及非编码转录。然而，迄今为止，对这些数据的分析是以特别的方式进行的。我们提出了一个概率的过程，HMM，自适应模型平铺数据之前，预测基因组序列的表达。隐马尔可夫模型（HMM）是用来模拟表达和非表达区域的平铺阵列探针得分的分布。HMM在映射到注释表达和非表达区域的探针集上训练。随后，对平铺的基因组序列进行转录片段的预测。预测伴随着表达概率曲线，用于支持证据的目视检查。我们在Cheng等人（2005）对10条人类染色体进行平铺阵列实验的数据上测试了HMM。结果可以从我们的网站下载和查看。在分类为表达和非表达探针之前，荧光评分的自适应建模的价值得到了证明。我们的研究结果表明，我们的自适应方法是上级的核苷酸敏感性和transfrag特异性方面的先前的分析。
Genomic tiling micro arrays have great potential for identifying previously undiscovered coding as well as non-coding transcription. To-date, however, analyses of these data have been performed in an ad hoc fashion. We present a probabilistic procedure, ExpressHMM, that adaptively models tiling data prior to predicting expression on genomic sequence. A hidden Markov model (HMM) is used to model the distributions of tiling array probe scores in expressed and non-expressed regions. The HMM is trained on sets of probes mapped to regions of annotated expression and non-expression. Subsequently, prediction of transcribed fragments is made on tiled genomic sequence. The prediction is accompanied by an expression probability curve for visual inspection of the supporting evidence. We test ExpressHMM on data from the Cheng et al. (2005) tiling array experiments on ten Human chromosomes. Results can be downloaded and viewed from our web site. The value of adaptive modelling of fluorescence scores prior to categorisation into expressed and non-expressed probes is demonstrated. Our results indicate that our adaptive approach is superior to the previous analysis in terms of nucleotide sensitivity and transfrag specificity.
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影响因子：
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Carninci, P;Kasukawa, T;Hayashizaki, Y
通讯作者：
Hayashizaki, Y
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通讯作者：
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通讯作者：
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