ArticleApplied and environmental microbiology2013
Distribution-based clustering: using ecology to refine the operational taxonomic unit.
Article in Applied and environmental microbiology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
70 citing papers in PubMed, 166 citations in OpenAlex.
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- Sequencing-based analysis of microbiomes.Nature reviews. Genetics · 2024Review
- Mucin glycans drive oral microbial community composition and function.NPJ biofilms and microbiomes · 2023Article
- Abundant and persistent sulfur-oxidizing microbial populations are responsive to hypoxia in the Chesapeake Bay.Environmental microbiology · 2022Article
- Gut microbiota of endangered Australian sea lion pups is unchanged by topical ivermectin treatment for endemic hookworm infection.Frontiers in microbiology · 2022Article
- Taxonomy Informed Clustering, an Optimized Method for Purer and More Informative Clusters in Diversity Analysis and Microbiome Profiling.Frontiers in bioinformatics · 2022Article
- Student-Driven Course-Based Undergraduate Research Experience (CUREs) Projects in Identifying Vaginal Microorganism Species Communities to Promote Scientific Literacy Skills.Frontiers in public health · 2022Article
- Influence of Association Network Properties and Ecological Assembly of the Foliar Fugal Community on Crop Quality.Frontiers in microbiology · 2022Article
- Bioavailable Nutrients (N and P) and Precipitation Patterns Drive Cyanobacterial Blooms in Missisquoi Bay, Lake Champlain.Microorganisms · 2021Article
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- Non-additive microbial community responses to environmental complexity.Nature communications · 2021Article
- Non-responder phenotype reveals apparent microbiome-wide antibiotic tolerance in the murine gut.Communications biology · 2021Article
- Microbial diversity of co-occurring heterotrophs in cultures of marine picocyanobacteria.Environmental microbiome · 2021Article
- Functional heterogeneity in the fermentation capabilities of the healthy human gut microbiota.PloS one · 2021Article
- Methanogen Productivity and Microbial Community Composition Varies With Iron Oxide Mineralogy.Frontiers in microbiology · 2021Article
- Prebiotics and Community Composition Influence Gas Production of the Human Gut Microbiota.mBio · 2020Article
10 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
16S rRNA sequencing, commonly used to survey microbial communities, begins by grouping individual reads into operational taxonomic units (OTUs). There are two major challenges in calling OTUs: identifying bacterial population boundaries and differentiating true diversity from sequencing errors. Current approaches to identifying taxonomic groups or eliminating sequencing errors rely on sequence data alone, but both of these activities could be informed by the distribution of sequences across samples. Here, we show that using the distribution of sequences across samples can help identify population boundaries even in noisy sequence data. The logic underlying our approach is that bacteria in different populations will often be highly correlated in their abundance across different samples. Conversely, 16S rRNA sequences derived from the same population, whether slightly different copies in the same organism, variation of the 16S rRNA gene within a population, or sequences generated randomly in error, will have the same underlying distribution across sampled environments. We present a simple OTU-calling algorithm (distribution-based clustering) that uses both genetic distance and the distribution of sequences across samples and demonstrate that it is more accurate than other methods at grouping reads into OTUs in a mock community. Distribution-based clustering also performs well on environmental samples: it is sensitive enough to differentiate between OTUs that differ by a single base pair yet predicts fewer overall OTUs than most other methods. The program can decrease the total number of OTUs with redundant information and improve the power of many downstream analyses to describe biologically relevant trends.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.