Maignien Lois

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Last Name
Maignien
First Name
Lois
ORCID
0000-0002-5571-5228

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Author: Sogin, Mitchell L. ×

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  • Article
    New insights into microbial ecology through subtle nucleotide variation
    (Frontiers Media, 2016-08-24) Eren, A. Murat ; Sogin, Mitchell L. ; Maignien, Lois
    Characterizing the community structure of naturally occurring microbes through marker gene amplicons has gained widespread acceptance for profiling microbial populations. The 16S ribosomal RNA (rRNA) gene provides a suitable target for most studies since (1) it meets the criteria for robust markers of evolution, e.g., both conserved and rapidly evolving regions that do not undergo horizontal gene transfer, (2) microbial ecologists have identified widely adopted primers and protocols for generating amplicons for sequencing, (3) analyses of both cultivars and environmental DNA have generated well-curated databases for taxonomic profiling, and (4) bioinformaticians and computational biologists have published comprehensive software tools for interpreting the data and generating publication-ready figures. Since the initial descriptions of high-throughput sequencing of 16S rRNA gene amplicons to survey microbial diversity, we have witnessed an explosion of association-based inferences of interactions between microbes and their environment.
  • Article
    Oligotyping : differentiating between closely related microbial taxa using 16S rRNA gene data
    (John Wiley & Sons, 2013-10-23) Eren, A. Murat ; Maignien, Lois ; Sul, Woo Jun ; Murphy, Leslie G. ; Grim, Sharon L. ; Morrison, Hilary G. ; Sogin, Mitchell L.
    Bacteria comprise the most diverse domain of life on Earth, where they occupy nearly every possible ecological niche and play key roles in biological and chemical processes. Studying the composition and ecology of bacterial ecosystems and understanding their function are of prime importance. High-throughput sequencing technologies enable nearly comprehensive descriptions of bacterial diversity through 16S ribosomal RNA gene amplicons. Analyses of these communities generally rely upon taxonomic assignments through reference data bases or clustering approaches using de facto sequence similarity thresholds to identify operational taxonomic units. However, these methods often fail to resolve ecologically meaningful differences between closely related organisms in complex microbial data sets. In this paper, we describe oligotyping, a novel supervised computational method that allows researchers to investigate the diversity of closely related but distinct bacterial organisms in final operational taxonomic units identified in environmental data sets through 16S ribosomal RNA gene data by the canonical approaches. Our analysis of two data sets from two different environments demonstrates the capacity of oligotyping at discriminating distinct microbial populations of ecological importance. Oligotyping can resolve the distribution of closely related organisms across environments and unveil previously overlooked ecological patterns for microbial communities. The URL http://oligotyping.org offers an open-source software pipeline for oligotyping.