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Combining Sequence and Time Series Expression Data to Learn Transcriptional Modules by A. Kundaje; M. Middendorf; Feng Gao; C. Wiggins; C. Leslie is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

What is Combining Sequence and Time Series Expression Data to Learn Transcriptional Modules about?

Our goal is to cluster genes into transcriptional modules-sets of genes where similarity in expression is explained by common regulatory mechanisms at the transcriptional level. We want to learn modules from both time series gene expression data and genome-wide motif data that are now readily available for organisms such as S. cereviseae as a result of prior computational studies or experimental results. We present a generative probabilistic model for combining regulatory sequence and time series expression data to cluster genes into coherent transcriptional modules. Starting with a set of motifs representing known or putative regulatory elements (transcription factor binding sites) and the counts of occurrences of these motifs in each gene's promoter region, together with a time series expression profile for each gene, the learning algorithm uses expectation maximization to learn module assignments based on both types of data. We also present a technique based on the Jensen-Shannon entropy contributions of motifs in the learned model for associating the most significant motifs to each module. Thus, the algorithm gives a global approach for associating sets of regulatory elements t

Who reads Combining Sequence and Time Series Expression Data to Learn Transcriptional Modules?

Author

A. Kundaje; M. Middendorf; Feng Gao; C. Wiggins; C. Leslie

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2005

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)