Abstract
We present a method (the Inferelator) for deriving genome-wide transcriptional regulatory interactions, and apply the method to predict a large portion of the regulatory network of the archaeon Halobacterium NRC-1. The Inferelator uses regression and variable selection to identify transcriptional influences on genes based on the integration of genome annotation and expression data. The learned network successfully predicted Halobacterium's global expression under novel perturbations with predictive power similar to that seen over training data. Several specific regulatory predictions were experimentally tested and verified.
Publication types
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Evaluation Study
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Research Support, N.I.H., Extramural
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Research Support, U.S. Gov't, Non-P.H.S.
MeSH terms
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Adenosine Triphosphatases / metabolism
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Algorithms*
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Archaeal Proteins / metabolism
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Cation Transport Proteins / metabolism
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Cluster Analysis
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Gene Expression Profiling
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Gene Expression Regulation, Archaeal*
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Genomics / methods*
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Halobacterium / genetics*
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Halobacterium / metabolism
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Homeostasis
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Oligonucleotide Array Sequence Analysis
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Ribosomal Proteins / genetics
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Systems Biology / methods*
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Transcription Factors / metabolism
Substances
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Archaeal Proteins
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Cation Transport Proteins
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Ribosomal Proteins
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Transcription Factors
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Adenosine Triphosphatases