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New Method Reveals Bacterial Diversity in Subsurface Sediments
Published: February 06, 2013
Posted: April 18, 2013

A fundamental question in microbial ecology is how do community diversity and composition change in response to perturbations. Most ecological studies have a limited ability to deeply sample community structure or a limited taxonomic resolution to track changing microbial diversity. To address this issue, researchers at the University of California, Berkeley, developed a method to assemble full length 16S rRNA sequences from short-read sequencing to assay the abundance and identity of organisms that represent as little as 0.01% of sediment bacterial communities. This approach, termed EMIRGE and optimized for large sequencing data size, allows researchers to differentiate the community composition among samples acquired before and after an environmental perturbation. Briefly, EMIRGE relies on a database of candidate 16S sequences for a template-guided assembly. An iterative method, sequencing reads are first aligned and probabilistically attributed to candidate 16S genes. Subsequently, candidate gene abundances and consensus sequences are adjusted based on the calculated probabilistic read attribution. The results were highly reproducible across very high alpha microbial diversity and abundant organisms from phyla that do not have cultivated representatives. This method allows for sensitive, accurate profiling of the “long tail” of low-abundance organisms that exist in many microbial communities and can resolve population dynamics in response to environmental change.

Reference: Miller, C. S., K. M. Handley, K. C. Wrighton, K. R. Frischkorn, B. C. Thomas, and J. F. Banfield. 2013. “Short-Read Assembly of Full-Length 16S Amplicons Reveals Bacterial Diversity in Subsurface Sediments,” PLoS ONE 8(2), e56018. DOI: 10.1371/journal.pone.0056018. (Reference link)

Contact: Susan Gregurick, SC-23.2, (301) 903-7672
Topic Areas:

  • Research Area: Subsurface Biogeochemical Research
  • Research Area: Genomic Analysis and Systems Biology
  • Research Area: Microbes and Communities
  • Research Area: Computational Biology, Bioinformatics, Modeling

Division: SC-33.2 Biological Systems Science Division, BER

 

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