ArticleNucleic acids research2012
Bayesian estimation of bacterial community composition from 454 sequencing data.
Article in Nucleic acids research, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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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
9 citing papers in PubMed.
- A toolbox of machine learning software to support microbiome analysis.Frontiers in microbiology · 2023Review
- Comparison of Methods for Picking the Operational Taxonomic Units From Amplicon Sequences.Frontiers in microbiology · 2021Article
- DMSC: A Dynamic Multi-Seeds Method for Clustering 16S rRNA Sequences Into OTUs.Frontiers in microbiology · 2019Article
- Phylogenetics and the human microbiome.Systematic biology · 2015Review
- ARK: Aggregation of Reads by K-Means for Estimation of Bacterial Community Composition.PloS one · 2015Article
- Effects on diversity of soil fungal community and fate of an artificially applied Beauveria bassiana strain assessed through 454 pyrosequencing.Microbial ecology · 2013Article
- Clustering evolving proteins into homologous families.BMC bioinformatics · 2013Article
- M-pick, a modularity-based method for OTU picking of 16S rRNA sequences.BMC bioinformatics · 2013Article
- Long-term temporal analysis of the human fecal microbiota revealed a stable core of dominant bacterial species.PloS one · 2013Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Estimating bacterial community composition from a mixed sample in different applied contexts is an important task for many microbiologists. The bacterial community composition is commonly estimated by clustering polymerase chain reaction amplified 16S rRNA gene sequences. Current taxonomy-independent clustering methods for analyzing these sequences, such as UCLUST, ESPRIT-Tree and CROP, have two limitations: (i) expert knowledge is needed, i.e. a difference cutoff between species needs to be specified; (ii) closely related species cannot be separated. The first limitation imposes a burden on the user, since considerable effort is needed to select appropriate parameters, whereas the second limitation leads to an inaccurate description of the underlying bacterial community composition. We propose a probabilistic model-based method to estimate bacterial community composition which tackles these limitations. Our method requires very little expert knowledge, where only the possible maximum number of clusters needs to be specified. Also our method demonstrates its ability to separate closely related species in two experiments, in spite of sequencing errors and individual variations.
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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.