ArticleFrontiers in bioinformatics2022
Taxonomy Informed Clustering, an Optimized Method for Purer and More Informative Clusters in Diversity Analysis and Microbiome Profiling.
Article in Frontiers in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed.
- Elucidating the Impact of Bacterial Composition and Diversity on Skin Homeostasis Using a Human 3D In Vitro Skin Model.Microorganisms · 2026Article
- Comparison of Different Sites of Microbiota Shows Increased Pseudomonas Species in the Gut Mucosa in Inflammatory Bowel Disease.Inflammatory bowel diseases · 2026Article
- Bacterial colonized melanoma skin models allow to study host-microbe interactionsFrontiers in microbiology · 2026Article
- Spatiotemporal and tissue-specific dynamics of bacterial communities in adult femaleFrontiers in cellular and infection microbiology · 2026Article
- Baricitinib Augments Lonafarnib Therapy to Preserve Colonic Homeostasis and Microbial Balance in a Mouse Model of Progeria.Aging cell · 2025Article
- The microbial metabolite desaminotyrosine protects against graft-versus-host disease via mTORC1 and STING-dependent intestinal regeneration.Nature communications · 2025Article
- Refining microbiome diversity analysis by concatenating and integrating dual 16S rRNA amplicon reads.NPJ biofilms and microbiomes · 2025Article
- Global Archaeal Diversity Revealed Through Massive Data Integration: Uncovering Just Tip of Iceberg.Microorganisms · 2025Article
- The microbial metabolite desaminotyrosine enhances T-cell priming and cancer immunotherapy with immune checkpoint inhibitors.EBioMedicine · 2023Article
- Large-Scale Integration of Amplicon Data Reveals Massive Diversity withinMicroorganisms · 2023Article
- Exploring body weight-influencing gut microbiota by elucidating the association with diet and host gene expression.Scientific reports · 2023Article
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4 authors.
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Abstract
Bacterial diversity is often analyzed using 16S rRNA gene amplicon sequencing. Commonly, sequences are clustered based on similarity cutoffs to obtain groups reflecting molecular species, genera, or families. Due to the amount of the generated sequencing data, greedy algorithms are preferred for their time efficiency. Such algorithms rely only on pairwise sequence similarities. Thus, sometimes sequences with diverse phylogenetic background are clustered together. In contrast, taxonomic classifiers use position specific taxonomic information in assigning a probable taxonomy to a given sequence. Here we introduce Taxonomy Informed Clustering (TIC), a novel approach that utilizes classifier-assigned taxonomy to restrict clustering to only those sequences that share the same taxonomic path. Based on this concept, we offer a complete and automated pipeline for processing of 16S rRNA amplicon datasets in diversity analyses. First, raw reads are processed to form denoised amplicons. Next, the denoised amplicons are taxonomically classified. Finally, the TIC algorithm progressively assigning clusters at molecular species, genus and family levels. TIC outperforms greedy clustering algorithms like USEARCH and VSEARCH in terms of clusters' purity and entropy, when using data from the Living Tree Project as test samples. Furthermore, we applied TIC on a dataset containing all
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