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Báo cáo sinh học: "PhyloScan: identification of transcription factor binding sites using cross-species evidence"

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: PhyloScan: identification of transcription factor binding sites using cross-species evidence. | Algorithms for Molecular Biology BioMed Central Research Open Access PhyloScan identification of transcription factor binding sites using cross-species evidence C Steven Carmack1 Lee Ann McCue1 2 Lee A Newberg 1 3 and Charles E Lawrence1 4 Address 1Frhe Wadsworth Center New York State Department of Health Albany NY 12201 USA 2Pacific Northwest National Laboratory Richland WA 99352 USA 3Departrnent of Computer Science Rensselaer Polytechnic Institute Troy NY 12180 USA and 4Division of Applied Mathematics Brown University Providence RI 02912 USA Email C Steven Carmack - steve.carmack@wadsworth.org Lee Ann McCue - leeann.mccue@pnl.gov Lee A Newberg - lee.newberg@wadsworth.org Charles E Lawrence - charles.lawrence@brown.edu Corresponding author Published 23 January 2007 Received 10 July 2006 Algorithms for Molecular Biology 2007 2 1 doi 10.1186 1748-7188-2-1 Accepted 23 January 2007 This article is available from http www.almob.Org content 2 1 1 2007 Carmack et al licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License http creativecommons.org licenses by 2.0 which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Abstract Background When transcription factor binding sites are known for a particular transcription factor it is possible to construct a motif model that can be used to scan sequences for additional sites. However few statistically significant sites are revealed when a transcription factor binding site motif model is used to scan a genome-scale database. Methods We have developed a scanning algorithm PhyloScan which combines evidence from matching sites found in orthologous data from several related species with evidence from multiple sites within an intergenic region to better detect regulons. The orthologous sequence data may be multiply aligned unaligned or a combination of aligned and unaligned. In aligned data .