MLTreeMap - accurate Maximum Likelihood placement of environmental DNA sequences into taxonomic and functional reference phylogenies

BMC Genomics

Methodology Article

Christian von Mering¹  Manuel Stark²  Simon A Berger³  Alexandros Stamatakis³ 

[1] Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, Switzerland;Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, Switzerland;Ph.D. program in Molecular Life Sciences, University of Zurich and Federal Institute of Technology (ETH), Zurich, Switzerland;The Exelixis Lab, Department of Computer Science, Technische Universität München, Germany;

关键词: Maximum Parsimony;    Additional Data File;    Sequence Fragment;    Reference Tree;    Taxonomic Assignment;   

DOI  :  10.1186/1471-2164-11-461

 received in 2010-04-23, accepted in 2010-08-05,  发布年份 2010

来源: Springer

【 摘 要 】

BackgroundShotgun sequencing of environmental DNA is an essential technique for characterizing uncultivated microbes in situ. However, the taxonomic and functional assignment of the obtained sequence fragments remains a pressing problem.ResultsExisting algorithms are largely optimized for speed and coverage; in contrast, we present here a software framework that focuses on a restricted set of informative gene families, using Maximum Likelihood to assign these with the best possible accuracy. This framework ('MLTreeMap'; http://mltreemap.org/) uses raw nucleotide sequences as input, and includes hand-curated, extensible reference information.ConclusionsWe discuss how we validated our pipeline using complete genomes as well as simulated and actual environmental sequences.

【 授权许可】

Unknown   
© Stark et al; licensee BioMed Central Ltd. 2010. This article is published under license to 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.