taxalogue: a toolkit to create comprehensive CO1 reference databases.

Background: Taxonomic 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. Methods: taxalogue 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. Results: taxalogue 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.

Repeated subsamples during DNA extraction reveal increased diversity estimates in DNA metabarcoding of Malaise traps.

With increased application of DNA metabarcoding in biodiversity assessment, various laboratory protocols have been optimized, and their further evaluation is subject of current research. Homogenization of bulk samples and subsequent DNA extraction from a subsample of destructed tissue is a common first stage of the metabarcoding process. This can either be conducted using sample material soaked in a storage fixative, e.g., ethanol (here referred to as "wet" treatment) or from dried individuals ("dry"). However, it remains uncertain if perfect mixing and equal distribution of DNA within the tube is ensured during homogenization and to what extent incomplete mixing and resulting variations in tissue composition affect diversity assessments if only a fraction of the destructed sample is processed in the downstream metabarcoding workflow. Here we investigated the efficiency of homogenization under wet and dry conditions and tested how variations in destructed tissue composition might affect diversity assessments of complex arthropod samples. We considered five time intervals of Malaise trap bulk samples and process nine different subsamples of homogenized tissue (20 mg each) in both treatments. Results indicate a more consistent diversity assessment from dried material, but at the cost of a higher processing time. Both approaches detected comparable OTU diversity and revealed similar taxa compositions in a single tissue extraction. With an increased number of tissue subsamples during DNA extraction, OTU diversity increased for both approaches, especially for highly diverse samples obtained during the summer. Here, particularly the detection of small and low-biomass taxa increased. The processing of multiple subsamples in the metabarcoding protocol can therefore be a helpful procedure to enhance diversity estimates and counteract taxonomic bias in biodiversity assessments. However, the process induces higher costs and time effort and the application in large-scale biodiversity assessment, e.g., in monitoring schemes needs to be considered on project-specific prospects.

Pooling size sorted Malaise trap fractions to maximize taxon recovery with metabarcoding.

BACKGROUND: Small and rare specimens can remain undetected when metabarcoding is applied on bulk samples with a high specimen size heterogeneity. This is especially critical for Malaise trap samples, where most of the biodiversity is contributed by small taxa with low biomass. The separation of samples in different size fractions for downstream analysis is one possibility to increase detection of small and rare taxa. However, experiments systematically testing different size sorting approaches and subsequent proportional pooling of fractions are lacking, but would provide important information for the optimization of metabarcoding protocols. We set out to find a size sorting strategy for Malaise trap samples that maximizes taxonomic recovery but remains scalable and time efficient. METHODS: Three Malaise trap samples were sorted into four size classes using dry sieving. Each fraction was homogenized and lysed. The corresponding lysates were pooled to simulate unsorted samples. Pooling was additionally conducted in equal proportions and in four different proportions enriching the small size fraction of samples. DNA from the individual size classes as well as the pooled fractions was extracted and metabarcoded using the FwhF2 and Fol-degen-rev primer set. Additionally, alternative wet sieving strategies were explored. RESULTS: The small size fractions harboured the highest diversity and were best represented when pooling in favour of small specimens. Metabarcoding of unsorted samples decreases taxon recovery compared to size sorted samples. A size separation into only two fractions (below 4 mm and above) can double taxon recovery compared to not size sorting. However, increasing the sequencing depth 3- to 4-fold can also increase taxon recovery to levels comparable with size sorting, but remains biased towards biomass rich taxa in the sample. CONCLUSION: We demonstrate that size fractionation of Malaise trap bulk samples can increase taxon recovery. While results show distinct patterns, the lack of statistical support due to the limited number of samples processed is a limitation. Due to increased speed and lower risk of cross-contamination as well as specimen damage we recommend wet sieving and proportional pooling of the lysates in favour of the small size fraction (80-90% volume). However, for large-scale projects with time constraints, increasing sequencing depth is an alternative solution.