tailieunhanh - Báo cáo sinh học: "Transcriptional regulatory network discovery via multiple method integration: application to e. coli K12"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí y học Molecular Biology cung cấp cho các bạn kiến thức về ngành sinh học đề tài: Transcriptional regulatory network discovery via multiple method integration: application to e. coli K12. | Algorithms for Molecular Biology BioMed Central Research Transcriptional regulatory network discovery via multiple method integration application to e. coli K12 Jingjun Sunt Kagan Tuncay t Alaa Abi Haidar Lisa Ensman Frank Stanley Michael Trelinski and Peter Ortoleva Open Access Address Center for Cell and Virus Theory Chemistry Building Indiana University Bloomington IN 47405 USA Email Jingjun Sun - jinsun@ Kagan Tuncay - ktuncay@ Alaa Abi Haidar - alahay@ Lisa Ensman - lensman@ Frank Stanley - fstanley@ Michael Trelinski - mtrelins@ Peter Ortoleva - ortoleva@ Corresponding author fEqual contributors Published 30 March 2007 Received 2 August 2006 Algorithms for Molecular Biology 2007 2 2 doi 1748-7188-2-2 Accepted 30 March 2007 This article is available from http content 2 1 2 2007 Sun et al licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License http licenses by which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Abstract Transcriptional regulatory network TRN discovery from one method . microarray analysis gene ontology phylogenic similarity does not seem feasible due to lack of sufficient information resulting in the construction of spurious or incomplete TRNs. We develop a methodology TRND that integrates a preliminary TRN microarray data gene ontology and phylogenic similarity to accurately discover TRNs and apply the method to E. coli K12. The approach can easily be extended to include other methodologies. Although gene ontology and phylogenic similarity have been used in the context of gene-gene networks we show that more information can be extracted when gene-gene scores are transformed to gene-transcription factor TF scores using a preliminary TRN. This seems to be preferable over the .