ArticleFrontiers in microbiology2021
Comparison of Methods for Picking the Operational Taxonomic Units From Amplicon Sequences.
Article in Frontiers in microbiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
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- Modulatory Effects of Isolated Lactobacillus paracasei from Malaysian Water Kefir Grains on the Intestinal Barrier and Gut Microbiota in Diabetic Mice.Probiotics and antimicrobial proteins · 2025Article
- Analysis of Sensory Qualities and Microbial Community Structure in the Fermented Grains by Low-Temperature Daqu from Different Regions of China.Current microbiology · 2025Article
- ICU care through NGS - Based identification of infectious agents: A comparative study.Heliyon · 2024Article
- Comparative analysis of sensory features, microbial diversity, and their correlations in light-flavor Daqu from different regions.Food science & nutrition · 2024Article
- pathMap: a path-based mapping tool for long noisy reads with high sensitivity.Briefings in bioinformatics · 2024Article
- Effects ofFrontiers in plant science · 2024Article
- Cutting edge tools in the field of soil microbiology.Current research in microbial sciences · 2024Review
- Co-Occurrence Analysis of Citrus Root Bacterial Microbiota under Citrus Greening Disease.Plants (Basel, Switzerland) · 2023Article
- Multicenter evaluation of gut microbiome profiling by next-generation sequencing reveals major biases in partial-length metabarcoding approach.Scientific reports · 2023Article
- Best Practices in Designing, Sequencing, and Identifying Random DNA Barcodes.Journal of molecular evolution · 2023Review
- A toolbox of machine learning software to support microbiome analysis.Frontiers in microbiology · 2023Review
- Predicting the effects of winter water warming in artificial lakes on zooplankton and its environment using combined machine learning models.Scientific reports · 2022Article
- Microbial mechanisms to improve immune checkpoint blockade responsiveness.Neoplasia (New York, N.Y.) · 2022Article
- PhyloFunDB: A Pipeline to Create and Update Functional Gene Taxonomic Databases.Microorganisms · 2022Article
- Analyzing the human gut mycobiome - A short guide for beginners.Computational and structural biotechnology journal · 2022Review
- Concatenation of paired-end reads improves taxonomic classification of amplicons for profiling microbial communities.BMC bioinformatics · 2021Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the advent of next-generation sequencing technology, it has become convenient and cost efficient to thoroughly characterize the microbial diversity and taxonomic composition in various environmental samples. Millions of sequencing data can be generated, and how to utilize this enormous sequence resource has become a critical concern for microbial ecologists. One particular challenge is the OTUs (operational taxonomic units) picking in 16S rRNA sequence analysis. Lucky, this challenge can be directly addressed by sequence clustering that attempts to group similar sequences. Therefore, numerous clustering methods have been proposed to help to cluster 16S rRNA sequences into OTUs. However, each method has its clustering mechanism, and different methods produce diverse outputs. Even a slight parameter change for the same method can also generate distinct results, and how to choose an appropriate method has become a challenge for inexperienced users. A lot of time and resources can be wasted in selecting clustering tools and analyzing the clustering results. In this study, we introduced the recent advance of clustering methods for OTUs picking, which mainly focus on three aspects: (i) the principles of existing clustering algorithms, (ii) benchmark dataset construction for OTU picking and evaluation metrics, and (iii) the performance of different methods with various distance thresholds on benchmark datasets. This paper aims to assist biological researchers to select the reasonable clustering methods for analyzing their collected sequences and help algorithm developers to design more efficient sequences clustering methods.
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