ArticleBMC bioinformatics2017
Using 16S rRNA gene as marker to detect unknown bacteria in microbial communities.
Article in BMC bioinformatics, 2017. 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
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Review
- Isolation, Identification, and Drug Sensitivity Test ofVeterinary sciences · 2025Article
- MetaBIDx: a new computational approach to bacteria identification in microbiomes.Microbiome research reports · 2024Article
- A qRT-PCR Method Capable of Quantifying Specific Microorganisms Compared to NGS-Based Metagenome Profiling Data.Microorganisms · 2022Article
- Review
- Comparison of Clinical Characteristics for Distinguishing COVID-19 From Influenza During the Early Stages in Guangdong, China.Frontiers in medicine · 2021Article
- Genetic diversity of symbiotic bacteria nodulating common bean (Phaseolus vulgaris) in western Kenya.PloS one · 2018Article
- Proceedings of the 2017 MidSouth Computational Biology and Bioinformatics Society (MCBIOS) Conference.BMC bioinformatics · 2017Article
- Oral microbiome: Unveiling the fundamentals.Journal of oral and maxillofacial pathology : JOMFPReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundQuantification and identification of microbial genomes based on next-generation sequencing data is a challenging problem in metagenomics. Although current methods have mostly focused on analyzing bacteria whose genomes have been sequenced, such analyses are, however, complicated by the presence of unknown bacteria or bacteria whose genomes have not been sequence.
resultsWe propose a method for detecting unknown bacteria in environmental samples. Our approach is unique in its utilization of short reads only from 16S rRNA genes, not from entire genomes. We show that short reads from 16S rRNA genes retain sufficient information for detecting unknown bacteria in oral microbial communities.
conclusionIn our experimentation with bacterial genomes from the Human Oral Microbiome Database, we found that this method made accurate and robust predictions at different read coverages and percentages of unknown bacteria. Advantages of this approach include not only a reduction in experimental and computational costs but also a potentially high accuracy across environmental samples due to the strong conservation of the 16S rRNA gene.
Indexed as
Identifiers
What OpenQuestion holds
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.