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Combining Sequence and Time Series Expression Data to Learn Transcriptional Modules
July-September 2005 (vol. 2 no. 3)
pp. 194-202
ASCII Text
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Anshul Kundaje, Manuel Middendorf, Feng Gao, Chris Wiggins, Christina Leslie, "Combining Sequence and Time Series Expression Data to Learn Transcriptional Modules," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 2, no. 3, pp. 194-202, July-September, 2005.
 
 
BibTex
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@article{ 10.1109/TCBB.2005.34,
author = {Anshul Kundaje and Manuel Middendorf and Feng Gao and Chris Wiggins and Christina Leslie},
title = {Combining Sequence and Time Series Expression Data to Learn Transcriptional Modules},
journal ={IEEE/ACM Transactions on Computational Biology and Bioinformatics},
volume = {2},
number = {3},
issn = {1545-5963},
year = {2005},
pages = {194-202},
doi = {http://doi.ieeecomputersociety.org/10.1109/TCBB.2005.34},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE/ACM Transactions on Computational Biology and Bioinformatics
TI - Combining Sequence and Time Series Expression Data to Learn Transcriptional Modules
IS - 3
SN - 1545-5963
SP194
EP202
EPD - 194-202
A1 - Anshul Kundaje,
A1 - Manuel Middendorf,
A1 - Feng Gao,
A1 - Chris Wiggins,
A1 - Christina Leslie,
PY - 2005
KW - Index Terms- Gene regulation
KW - clustering
KW - heterogeneous data.
VL - 2
JA - IEEE/ACM Transactions on Computational Biology and Bioinformatics
ER -
 
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 to "modules” of genes with similar time series expression profiles. The model for expression data exploits our prior belief of smooth dependence on time by using statistical splines and is suitable for typical time course data sets with relatively few experiments. Moreover, the model is sufficiently interpretable that we can understand how both sequence data and expression data contribute to the cluster assignments, and how to interpolate between the two data sources. We present experimental results on the yeast cell cycle to validate our method and find that our combined expression and motif clustering algorithm discovers modules with both coherent expression and similar motif patterns, including binding motifs associated to known cell cycle transcription factors.

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Index Terms:
Index Terms- Gene regulation, clustering, heterogeneous data.
Citation:
Anshul Kundaje, Manuel Middendorf, Feng Gao, Chris Wiggins, Christina Leslie, "Combining Sequence and Time Series Expression Data to Learn Transcriptional Modules," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 2, no. 3, pp. 194-202, July-Sept. 2005, doi:10.1109/TCBB.2005.34
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