A hidden Markov model approach for determining expression from genomic tiling micro arrays

TY - JOUR

T1 - A hidden Markov model approach for determining expression from genomic tiling micro arrays

AU - Terkelsen, Kasper Munch

AU - Gardner, P. P.

AU - Arctander, Peter

AU - Krogh, A.

PY - 2006

Y1 - 2006

N2 - BackgroundGenomic 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.ResultsWe 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 [1]. Results can be downloaded and viewed from our web site [2].ConclusionThe 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.

AB - BackgroundGenomic 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.ResultsWe 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 [1]. Results can be downloaded and viewed from our web site [2].ConclusionThe 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.

U2 - 10.1186/1471-2105-7-239

DO - 10.1186/1471-2105-7-239

M3 - Journal article

C2 - 16672042

SN - 1471-2105

VL - 7

JO - B M C Bioinformatics

JF - B M C Bioinformatics

IS - 239

ER -