ArticleJournal of microbiology (Seoul, Korea)2012
TBC: a clustering algorithm based on prokaryotic taxonomy.
Article in Journal of microbiology (Seoul, Korea), 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- Effects of the Brown SeaweedFrontiers in nutrition · 2018Article
- Metagenomic insights into the bioaerosols in the indoor and outdoor environments of childcare facilities.PloS one · 2015Article
- Cloacal Microbiome Structure in a Long-Distance Migratory Bird Assessed Using Deep 16sRNA Pyrosequencing.PloS one · 2015Article
- Clinical microbiology informatics.Clinical microbiology reviews · 2014Review
- Performance and bacterial communities of successive alkalinity-producing systems (SAPSs) in passive treatment processes treating mine drainages differing in acidity and metal levels.Environmental science and pollution research international · 2014Article
- Analytical tools and databases for metagenomics in the next-generation sequencing era.Genomics & informatics · 2013Review
- CLUSTOM: a novel method for clustering 16S rRNA next generation sequences by overlap minimization.PloS one · 2013Article
- Bacterial diversity in ornithogenic soils compared to mineral soils on King George Island, Antarctica.Journal of microbiology (Seoul, Korea) · 2012Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
High-throughput DNA sequencing technologies have revolutionized the study of microbial ecology. Massive sequencing of PCR amplicons of the 16S rRNA gene has been widely used to understand the microbial community structure of a variety of environmental samples. The resulting sequencing reads are clustered into operational taxonomic units that are then used to calculate various statistical indices that represent the degree of species diversity in a given sample. Several algorithms have been developed to perform this task, but they tend to produce different outcomes. Herein, we propose a novel sequence clustering algorithm, namely Taxonomy-Based Clustering (TBC). This algorithm incorporates the basic concept of prokaryotic taxonomy in which only comparisons to the type strain are made and used to form species while omitting full-scale multiple sequence alignment. The clustering quality of the proposed method was compared with those of MOTHUR, BLASTClust, ESPRIT-Tree, CD-HIT, and UCLUST. A comprehensive comparison using three different experimental datasets produced by pyrosequencing demonstrated that the clustering obtained using TBC is comparable to those obtained using MOTHUR and ESPRIT-Tree and is computationally efficient. The program was written in JAVA and is available from http://sw.ezbiocloud.net/tbc.
Indexed as
Identifiers
22538644What 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.