Journal Article

Visual and statistical comparison of metagenomes

Suparna Mitra, Bernhard Klar and Daniel H. Huson

in Bioinformatics

Volume 25, issue 15, pages 1849-1855
Published in print August 2009 | ISSN: 1367-4803
Published online June 2009 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btp341
Visual and statistical comparison of metagenomes

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Background: Metagenomics is the study of the genomic content of an environmental sample of microbes. Advances in the through-put and cost-efficiency of sequencing technology is fueling a rapid increase in the number and size of metagenomic datasets being generated. Bioinformatics is faced with the problem of how to handle and analyze these datasets in an efficient and useful way. One goal of these metagenomic studies is to get a basic understanding of the microbial world both surrounding us and within us. One major challenge is how to compare multiple datasets. Furthermore, there is a need for bioinformatics tools that can process many large datasets and are easy to use.

Results: This article describes two new and helpful techniques for comparing multiple metagenomic datasets. The first is a visualization technique for multiple datasets and the second is a new statistical method for highlighting the differences in a pairwise comparison. We have developed implementations of both methods that are suitable for very large datasets and provide these in Version 3 of our standalone metagenome analysis tool MEGAN.

Conclusion: These new methods are suitable for the visual comparison of many large metagenomes and the statistical comparison of two metagenomes at a time. Nevertheless, more work needs to be done to support the comparative analysis of multiple metagenome datasets.

Availability: Version 3 of MEGAN, which implements all ideas presented in this article, can be obtained from our web site at: www-ab.informatik.uni-tuebingen.de/software/megan.

Contact: mitra@informatik.uni-tuebingen.de

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  3615 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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