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Genome Biology

, 7:R36

First Online: 10 May 2006Received: 24 October 2005Revised: 13 February 2006Accepted: 30 March 2006

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.

Electronic supplementary materialThe online version of this article doi:10.1186-gb-2006-7-5-r36 contains supplementary material, which is available to authorized users.

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Autor: Richard Bonneau - David J Reiss - Paul Shannon - Marc Facciotti - Leroy Hood - Nitin S Baliga - Vesteinn Thorsson

Fuente: https://link.springer.com/







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