A framework for analysis of metagenomic sequencing data.
The human body is home to a diverse assemblage of microbial species. In fact, the number of microbial cells in each person is an order of magnitude greater than the number of cells that make up the body itself. Changes in the composition and relative abundance of these microbial species are highly associated with intestinal and respiratory disorders and diseases of the skin and mucus membranes. While cultivation-independent methods employing PCR-amplification, cloning and sequence analysis of 16S rRNA or other phylogenetically informative genes have made it possible to assess the composition of microbial species in natural environments, until recently this approach has been too time consuming and expensive for routine use. Advances in high throughput pyrosequencing have largely eliminated these obstacles, reducing cost and increasing sequencing capacity by orders of magnitude. In fact, although numerous arithmetic and statistical measurements are available to assess the composition and diversity of microbial communities, the limiting factor has become applying these analyses to millions of sequences and visualizing the results. We introduce a new, easy-to-use, extensible visualization and analysis software framework that facilitates the manipulation and interpretation of large amounts of metagenomic sequence data. The framework automatically performs an array of standard metagenomic analyses using FASTA files that contain 16S rRNA sequences as input. The framework has been used to reveal differences between the composition of the microbiota in healthy individuals and individuals with diseases such as bacterial vaginosis and necrotizing enterocolitis.
Department of Computer Science, University of New Orleans, 2000 Lakeshore Drive, New Orleans, LA 70148, USA. aeren@uno.edu
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Murat Eren
Michael J
MJ
Ferris
Christopher M
CM
Taylor
D001419:Bacteria / Q000145:classification / Q000235:genetics; D019295:Computational Biology; D003627:Data Interpretation, Statistical; D020345:Enterocolitis, Necrotizing / Q000382:microbiology; D005260:Female; D059014:High-Throughput Nucleotide Sequencing / Q000706:statistics & numerical data; D006801:Humans; D054892:Metagenome; D056186:Metagenomics / Q000706:statistics & numerical data*; D010802:Phylogeny; D012329:RNA, Bacterial / Q000235:genetics; D012336:RNA, Ribosomal, 16S / Q000235:genetics; D012984:Software; D016585:Vaginosis, Bacterial / Q000382:microbiology
D012329:RNA, Bacterial; D012336:RNA, Ribosomal, 16S