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Metabarcoding free-living marine nematodes using curated 18S and CO1 reference sequence databases for species-level taxonomic assignments.

ABSTRACT: High-throughput sequencing has the potential to describe biological communities with high efficiency yet comprehensive assessment of diversity with species-level resolution remains one of the most challenging aspects of metabarcoding studies. We investigated the utility of curated ribosomal and mitochondrial nematode reference sequence databases for determining phylum-specific species-level clustering thresholds. We compiled 438 ribosomal and 290 mitochondrial sequences which identified 99% and 94% as the species delineation clustering threshold, respectively. These thresholds were evaluated in HTS data from mock communities containing 39 nematode species as well as environmental samples from Vietnam. We compared the taxonomic description of the mocks generated by two read-merging and two clustering algorithms and the cluster-free Dada2 pipeline. Taxonomic assignment with the RDP classifier was assessed under different training sets. Our results showed that 36/39 mock nematode species were identified across the molecular markers (18S: 32, JB2: 19, JB3: 21) in UClust_ref OTUs at their respective clustering thresholds, outperforming UParse_denovo and the commonly used 97% similarity. Dada2 generated the most realistic number of ASVs (18S: 83, JB2: 75, JB3: 82), collectively identifying 30/39 mock species. The ribosomal marker outperformed the mitochondrial markers in terms of species and genus-level detections for both OTUs and ASVs. The number of taxonomic assignments of OTUs/ASVs was highest when the smallest reference database containing only nematode sequences was used and when sequences were truncated to the respective amplicon length. Overall, OTUs generated more species-level detections, which were, however, associated with higher error rates compared to ASVs. Genus-level assignments using ASVs exhibited higher accuracy and lower error rates compared to species-level assignments, suggesting that this is the most reliable pipeline for rapid assessment of alpha diversity from environmental samples.

SUBMITTER: Macheriotou L

PROVIDER: S-EPMC6374678 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundIn metabarcoding analyses, the taxonomic assignment is crucial to place sequencing data in biological and ecological contexts. This fundamental step depends on a reference database, which should have a good taxonomic coverage to avoid unassigned sequences. However, this goal is rarely achieved in many geographic regions and for several taxonomic groups. On the other hand, more is not necessarily better, as sequences in reference databases belonging to taxonomic groups out of the studied region/environment context might lead to false assignments.MethodsWe investigated the effect of using several subsets of a cytochrome c oxidase subunit I (COI) reference database on taxonomic assignment. Published metabarcoding sequences from the Mediterranean Sea were assigned to taxa using COInr, which is a comprehensive, non-redundant and recent database of COI sequences obtained both from BOLD and NCBI, and two of its subsets: (i) all sequences except insects (COInr-WO-Insecta), which represent the overwhelming majority of COInr database, but are irrelevant for marine samples, and (ii) all sequences from taxonomic families present in the Mediterranean Sea (COInr-Med). Four different algorithms for taxonomic assignment were employed in parallel to evaluate differences in their output and data consistency.ResultsThe reduction of the database to more specific custom subsets increased the number of unassigned sequences. Nevertheless, since most of them were incorrectly assigned by the less specific databases, this is a positive outcome. Moreover, the taxonomic resolution (the lowest taxonomic level to which a sequence is attributed) of several sequences tended to increase when using customized databases. These findings clearly indicated the need for customized databases adapted to each study. However, the very high proportion of unassigned sequences points to the need to enrich the local database with new barcodes specifically obtained from the studied region and/or taxonomic group. Including novel local barcodes to the COI database proved to be very profitable: by adding only 116 new barcodes sequenced in our laboratory, thus increasing the reference database by only 0.04%, we were able to improve the resolution for ca. 0.6-1% of the Amplicon Sequence Variants (ASVs).

Project description:BackgroundTaxonomic identification through DNA barcodes gained considerable traction through the invention of next-generation sequencing and DNA metabarcoding. Metabarcoding allows for the simultaneous identification of thousands of organisms from bulk samples with high taxonomic resolution. However, reliable identifications can only be achieved with comprehensive and curated reference databases. Therefore, custom reference databases are often created to meet the needs of specific research questions. Due to taxonomic inconsistencies, formatting issues, and technical difficulties, building a custom reference database requires tremendous effort. Here, we present taxalogue, an easy-to-use software for creating comprehensive and customized reference databases that provide clean and taxonomically harmonized records. In combination with extensive geographical filtering options, taxalogue opens up new possibilities for generating and testing evolutionary hypotheses.Methodstaxalogue collects DNA sequences from several online sources and combines them into a reference database. Taxonomic incongruencies between the different data sources can be harmonized according to available taxonomies. Dereplication and various filtering options are available regarding sequence quality or metadata information. taxalogue is implemented in the open-source Ruby programming language, and the source code is available at https://github.com/nwnoll/taxalogue. We benchmark four reference databases by sequence identity against eight queries from different localities and trapping devices. Subsamples from each reference database were used to compare how well another one is covered.Resultstaxalogue produces reference databases with the best coverage at high identities for most tested queries, enabling more accurate, reliable predictions with higher certainty than the other benchmarked reference databases. Additionally, the performance of taxalogue is more consistent while providing good coverage for a variety of habitats, regions, and sampling methods. taxalogue simplifies the creation of reference databases and makes the process reproducible and transparent. Multiple available output formats for commonly used downstream applications facilitate the easy adoption of taxalogue in many different software pipelines. The resulting reference databases improve the taxonomic classification accuracy through high coverage of the query sequences at high identities.

Project description:BackgroundThe DNA metabarcoding approach has become one of the most used techniques to study the taxa composition of various sample types. To deal with the high amount of data generated by the high-throughput sequencing process, a bioinformatics workflow is required and the QIIME2 platform has emerged as one of the most reliable and commonly used. However, only some pre-formatted reference databases dedicated to a few barcode sequences are available to assign taxonomy. If users want to develop a new custom reference database, several bottlenecks still need to be addressed and a detailed procedure explaining how to develop and format such a database is currently missing. In consequence, this work is aimed at presenting a detailed workflow explaining from start to finish how to develop such a curated reference database for any barcode sequence.ResultsWe developed DB4Q2, a detailed workflow that allowed development of plant reference databases dedicated to ITS2 and rbcL, two commonly used barcode sequences in plant metabarcoding studies. This workflow addresses several of the main bottlenecks connected with the development of a curated reference database. The detailed and commented structure of DB4Q2 offers the possibility of developing reference databases even without extensive bioinformatics skills, and avoids 'black box' systems that are sometimes encountered. Some filtering steps have been included to discard presumably fungal and misidentified sequences. The flexible character of DB4Q2 allows several key sequence processing steps to be included or not, and downloading issues can be avoided. Benchmarking the databases developed using DB4Q2 revealed that they performed well compared to previously published reference datasets.ConclusionThis study presents DB4Q2, a detailed procedure to develop custom reference databases in order to carry out taxonomic analyses with QIIME2, but also with other bioinformatics platforms if desired. This work also provides ready-to-use plant ITS2 and rbcL databases for which the prediction accuracy has been assessed and compared to that of other published databases.

Project description:Metabarcoding is a popular application which warrants continued methods optimization. To maximize barcoding inferences, hierarchy-based sequence classification methods are increasingly common. We present methods for the construction and curation of a database designed for hierarchical classification of a 157 bp barcoding region of the arthropod cytochrome c oxidase subunit I (COI) locus. We produced a comprehensive arthropod COI amplicon dataset including annotated arthropod COI sequences and COI sequences extracted from arthropod whole mitochondrion genomes, the latter of which provided the only source of representation for Zoraptera, Callipodida and Holothyrida. The database contains extracted sequences of the target amplicon from all major arthropod clades, including all insect orders, all arthropod classes and Onychophora, Tardigrada and Mollusca outgroups. During curation, we extracted the COI region of interest from approximately 81 percent of the input sequences, corresponding to 73 percent of the genus-level diversity found in the input data. Further, our analysis revealed a high degree of sequence redundancy within the NCBI nucleotide database, with a mean of approximately 11 sequence entries per species in the input data. The curated, low-redundancy database is included in the Metaxa2 sequence classification software (http://microbiology.se/software/metaxa2/). Using this database with the Metaxa2 classifier, we performed a cross-validation analysis to characterize the relationship between the Metaxa2 reliability score, an estimate of classification confidence, and classification error probability. We used this analysis to select a reliability score threshold which minimized error. We then estimated classification sensitivity, false discovery rate and overclassification, the propensity to classify sequences from taxa not represented in the reference database. Our work will help researchers design and evaluate classification databases and conduct metabarcoding on arthropods and alternate taxa.

Project description:Nematodes are representative soil metazoans with diverged species that play crucial roles in nutrient recycling in the pedosphere. Qualitative and quantitative information on nematode communities is useful for assessing soil quality, and DNA barcode-mediated taxonomic analysis is a powerful tool to investigate taxonomic compositions and changes in nematode communities. Here, we investigated four regions (regions 1-4) of the 18S small subunit ribosomal RNA (SSU) gene as PCR targets of deep amplicon sequencing for the taxonomic profiling of individual soil nematodes. We determined the sequence variants (SVs) of 4 SSU regions for 96 nematodes (total 384 amplicons) isolated from copse soils and assigned their taxonomy using the QIIME2 software with dada2 or deblur algorithm and the SILVA database. Dada2 detected approximately 2-fold more nematode-derived SVs than deblur, and a larger number of SVs were obtained in regions 1 and 4 than those in other regions. These results and sufficient reference sequence coverage in region 4 indicated that DNA barcoding using a primer set for region 4 followed by dada2-based analysis would be most suitable for soil nematode taxonomic analysis. Eighteen SSU-derived operational taxonomic units (rOTUs) were obtained from 68 isolates, and their orders were determined based on the phylogenetic trees built by four regional sequences of rOTUs and 116 nematode reference species as well as the BLASTN search. The majority of the isolates were derived from three major orders Dorylaimida (6 rOTUs, 51.5% in 68 isolates), Rhabditida (4 rOTUs, 29.4%), and Triplonchida (7 rOTUs, 17.6%). The predicted feeding types of the isolates were fungivores (38.2% in total nematodes), plant feeders (32.4%), and 14.7% for both bacterivores and omnivores/predators. Additionally, we attempted to improve the branch structure of phylogenetic trees by using long nucleotide sequences artificially prepared by connecting regional sequences, but the effect was limited.

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Metabarcoding free-living marine nematodes using curated 18S and CO1 reference sequence databases for species-level taxonomic assignments.

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