ArticleMycoKeys2018
Great differences in performance and outcome of high-throughput sequencing data analysis platforms for fungal metabarcoding.
Article in MycoKeys, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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
23 citing papers in PubMed.
- A Scalable and Cost-Effective In-Line Barcoding Strategy for Standardized 16S rRNA Gene Amplicon Sequencing: Performance Evaluation and Bias Assessment.Molecular ecology resources · 2026Article
- Another dark taxon comes to light:IMA fungus · 2026Article
- FunVIP: Fungal Validation and Identification Pipeline based on phylogenetic analysis.Journal of microbiology (Seoul, Korea) · 2025Article
- Comparison of commonly used software pipelines for analyzing fungal metabarcoding data.BMC genomics · 2024Article
- A pile of pipelines: An overview of the bioinformatics software for metabarcoding data analyses.Molecular ecology resources · 2024Review
- New and Interesting Fungi. 7.Fungal systematics and evolution · 2024Article
- New-Generation Sequencing Technology in Diagnosis of Fungal Plant Pathogens: A Dream Comes True?Journal of fungi (Basel, Switzerland) · 2022Review
- In depth search of the Sequence Read Archive database reveals global distribution of the emerging pathogenic fungus Scedosporium aurantiacum.Medical mycology · 2022Article
- Analyzing the human gut mycobiome - A short guide for beginners.Computational and structural biotechnology journal · 2022Review
- Environmental DNA reveals diversity and abundance ofAerobiologia · 2022Article
- Lampenflora in a Show Cave in the Great Basin Is Distinct from Communities on Naturally Lit Rock Surfaces in Nearby Wild Caves.Microorganisms · 2021Article
- Fungal taxonomy and sequence-based nomenclature.Nature microbiology · 2021Review
- Efficient and stable metabarcoding sequencing data using a DNBSEQ-G400 sequencer validated by comprehensive community analyses.GigaByte (Hong Kong, China) · 2021Article
- Daring to be differential: metabarcoding analysis of soil and plant-related microbial communities using amplicon sequence variants and operational taxonomical units.BMC genomics · 2020Article
- GlobalFungi, a global database of fungal occurrences from high-throughput-sequencing metabarcoding studies.Scientific data · 2020Article
- Unambiguous identification of fungi: where do we stand and how accurate and precise is fungal DNA barcoding?IMA fungus · 2020Article
- Optimisation and Benchmarking of Targeted Amplicon Sequencing for Mycobiome Analysis of Respiratory Specimens.International journal of molecular sciences · 2019Article
- High-throughput identification and diagnostics of pathogens and pests: Overview and practical recommendations.Molecular ecology resources · 2019Review
- PacBio amplicon sequencing for metabarcoding of mixed DNA samples from lichen herbarium specimens.MycoKeys · 2019Article
- Mycobiome diversity: high-throughput sequencing and identification of fungi.Nature reviews. Microbiology · 2019Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Along with recent developments in high-throughput sequencing (HTS) technologies and thus fast accumulation of HTS data, there has been a growing need and interest for developing tools for HTS data processing and communication. In particular, a number of bioinformatics tools have been designed for analysing metabarcoding data, each with specific features, assumptions and outputs. To evaluate the potential effect of the application of different bioinformatics workflow on the results, we compared the performance of different analysis platforms on two contrasting high-throughput sequencing data sets. Our analysis revealed that the computation time, quality of error filtering and hence output of specific bioinformatics process largely depends on the platform used. Our results show that none of the bioinformatics workflows appears to perfectly filter out the accumulated errors and generate Operational Taxonomic Units, although PipeCraft, LotuS and PIPITS perform better than QIIME2 and Galaxy for the tested fungal amplicon dataset. We conclude that the output of each platform requires manual validation of the OTUs by examining the taxonomy assignment values.
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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.