The 16S ribosomal RNA gene commonly serves as a molecular marker for investigating microbial community composition and structure. Vast amounts of 16S rRNA amplicon data generated from environmental samples thanks to the recent advances in sequencing technologies allowed microbial ecologists to explore microbial community dynamics over temporal and spatial scales deeper than ever before. However, widely used methods for the analysis of bacterial communities generally ignore subtle nucleotide variations among high-throughput sequencing reads and often fail to resolve ecologically meaningful differences between closely related organisms in complex microbial datasets. Lack of proper partitioning of the sequencing data into relevant units often masks important ecological patterns.
Oligotyping is a supervised computational method recently introduced to address this issue. It uses Shannon entropy to identify subtle nucleotide variations among high-throughput sequencing reads. By defining more relevant ecological and biological units, oligotyping makes it possible to better explain the diversity of closely related but distinct bacterial organisms in large datasets.
Oligotyping is being used in an increasing number of projects seeking to understand occurrence patterns of microorganisms, their environmental controls, or their hosts physiology. This method already permitted to further explore temporal and spatial variations among samples from human oral cavity, animal gastrointestinal tract, plant leaf surface, sponge symbiosis, viral communities and pelagic marine ecosystems.
Based on these initial results and our experience with oligotyping, we are now convinced of the high potential of this method as in many cases oligotyping analyses provided new insights compared to traditional approaches.
We would thus like to encourage microbial ecologist currently revisiting their large-scale microbial diversity studies with oligotyping, to share their results in this Frontiers Research Topic. Beyond promoting this new tool, we envision that such collection of articles might contribute to the development of an updated view of microbial diversity, linking ecology, biogeography, and evolutionary processes in microbial world through subtle nucleotide variations.
The 16S ribosomal RNA gene commonly serves as a molecular marker for investigating microbial community composition and structure. Vast amounts of 16S rRNA amplicon data generated from environmental samples thanks to the recent advances in sequencing technologies allowed microbial ecologists to explore microbial community dynamics over temporal and spatial scales deeper than ever before. However, widely used methods for the analysis of bacterial communities generally ignore subtle nucleotide variations among high-throughput sequencing reads and often fail to resolve ecologically meaningful differences between closely related organisms in complex microbial datasets. Lack of proper partitioning of the sequencing data into relevant units often masks important ecological patterns.
Oligotyping is a supervised computational method recently introduced to address this issue. It uses Shannon entropy to identify subtle nucleotide variations among high-throughput sequencing reads. By defining more relevant ecological and biological units, oligotyping makes it possible to better explain the diversity of closely related but distinct bacterial organisms in large datasets.
Oligotyping is being used in an increasing number of projects seeking to understand occurrence patterns of microorganisms, their environmental controls, or their hosts physiology. This method already permitted to further explore temporal and spatial variations among samples from human oral cavity, animal gastrointestinal tract, plant leaf surface, sponge symbiosis, viral communities and pelagic marine ecosystems.
Based on these initial results and our experience with oligotyping, we are now convinced of the high potential of this method as in many cases oligotyping analyses provided new insights compared to traditional approaches.
We would thus like to encourage microbial ecologist currently revisiting their large-scale microbial diversity studies with oligotyping, to share their results in this Frontiers Research Topic. Beyond promoting this new tool, we envision that such collection of articles might contribute to the development of an updated view of microbial diversity, linking ecology, biogeography, and evolutionary processes in microbial world through subtle nucleotide variations.
