tailieunhanh - Báo cáo y học: "Prediction of effective genome size in metagenomic samples"

Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học Minireview cung cấp cho các bạn kiến thức về ngành y đề tài: Prediction of effective genome size in metagenomic samples. | Method Open Access Prediction of effective genome size in metagenomic samples Jeroen RaesH Jan O KorbelH Martin J Lercher Christian von Mering and Peer Bork Addresses European Molecular Biology Laboratory Meyerhofstrasse 1 D-69117 Heidelberg Germany. Molecular Biophysics Biochemistry Department Yale University Whitney Avenue New Haven Connecticut USA. Institute of Molecular Biology University of Zurich Winterthurerstrasse 190 8057 Zurich Switzerland. H These authors contributed equally to this work. Correspondence Peer Bork. Email bork@ Published 15 January 2007 Genome Biology 2007 8 R10 doi gb-2007-8-1-r10 The electronic version of this article is the complete one and can be found online at http 2007 8 1 R10 Received 29 August 2006 Revised 31 October 2006 Accepted 15 January 2007 2006 Raes 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 We introduce a novel computational approach to predict effective genome size EGS a measure that includes multiple plasmid copies inserted sequences and associated phages and viruses from short sequencing reads of environmental genomics or metagenomics projects. We observe considerable EGS differences between environments and link this with ecologic complexity as well as species composition for instance the presence of eukaryotes . For example we estimate EGS in a complex organism-dense farm soil sample at about megabases Mb whereas that of the bacteria therein is only Mb for bacteria in a nutrient-poor organism-sparse ocean surface water sample EGS is as low as Mb. The method also permits evaluation of completion status and assembly bias in single-genome sequencing projects. Background Because of its direct link with the .