Third-party Annotations: Linking PlutoF platform and the ELIXIR Contextual Data ClearingHouse for the reporting of source material annotation gaps and inaccuracies

Abstract

Third-party annotations are a valuable resource to improve the quality of public DNA sequences. For example, sequences in International Nucleotide Sequence Databases Collaboration (INSDC) often lack important features like taxon interactions, species level identification, information associated with habitat, locality, country, coordinates, etc. Therefore, initiatives to mine additional information from publications and link to the public DNA sequences have become common practice (e.g. Tedersoo et al. 2011, Nilsson et al. 2014, Groom et al. 2021). However, third-party annotations have their own specific challenges. For example, annotations can be inaccurate and therefore must be open for permanent data management. Further, every DNA sequence (except sequences from type material) can carry different species names, which must be databased as equal scientific hypotheses. PlutoF platform provides such data management services for third-party annotations. PlutoF is an online data management platform and computing service provider for biology and related disciplines. Registered users can enter and manage a wide range of data, e.g., taxon occurrences, metabarcoding data, taxon classifications, traits, and lab data. It also features an annotation module where third-party annotations (on material source, geolocation and habitat, taxonomic identifications, interacting taxa, etc.) can be added to any collection specimen, living culture or DNA sequence record. The UNITE Community is using these services to annotate and improve the quality of INSDC rDNA Internal Transcribed Spacer (ITS) sequence datasets. The National Center for Biotechnology Information (NCBI) is linking its ITS sequences with their annotations in PlutoF. However, there is still missing an automated solution for linking annotations in PlutoF with any sequence and sample record stored in INSDC databases. One of the ambitions of the BiCIKL Project is to solve this through operating the ELIXIR Contextual Data ClearingHouse (CDCH). CDCH offers a light and simple RESTful Application Programming Interface (API) to enable extension, correction and improvement of publicly available annotations on sample and sequence records available in ELIXIR data resources. It facilitates feeding improved or corrected annotations from databases (such as secondary databases, e.g., PlutoF, which consume and curate data from repositories) back to primary repositories (databases of the three INSDC collaborative partners). In the Biodiversity Community Integrated Knowledge Library (BiCIKL) Project, the University of Tartu Natural History Museum is leading the task of linking the two components—the web interface provided by the PlutoF platform and CDCH APIs—to allow user-friendly and effortless reporting of errors and gaps in sequenced material source annotations. The API and web interface will be promoted to those communities (such as taxonomists, those abstracting from the literature, and those already using the community curated data) with the appropriate knowledge and tools who will be encouraged to report their enhanced annotations back to primary repositories.

Microbial communities and biogeochemical functioning across peatlands in the Athabasca Oil Sands region of Canada: Implications for reclamation and management

“…Taxonomic assignment was performed with the naive Bayes feature‐classifier (Bokulich et al, 2018). The taxonomic assignment was performed with the Silva 138 SSURef NR99 database for prokaryotes (Robeson et al, 2021) and UNITE (version 8.3) for fungi (Abarenkov et al, 2020; Nilsson et al, 2019). ASVs with taxonomic assignments to mitochondria, chloroplast, and eukaryote 18 S sequences were filtered from the prokaryote dataset, and non‐fungal eukaryotes were excluded from the ITS2 dataset.…”

Habitat and tree species identity shape aboveground and belowground fungal communities in central European forests

“…Because of the mixed orientation, we checked for reverse complement reads with DADA2s rc() function (Callahan et al, 2016) and added them to the original ASV table. In a last step, the three replicates were merged into a single table, which was matched against the UNITE database (version 9.0, including eukaryotic ITSs as outgroups; Abarenkov et al, 2022) by the DADA2 assignTaxonomy() function (Callahan et al, 2016) with minBoot = 50 and tryRC = TRUE. The ASV table was checked for potential contaminants with the decontam algorithm (Davis et al, 2018), set to both prevalence and frequency, and further curated using the LULU algorithm (Frøslev et al, 2017) with default parameters, grouping ASVs through patterns of similarity and sequence co-occurrence.…”

Metabolomics and microbiome reveal potential root microbiota affecting the alkaloidal metabolome in Aconitum vilmorinianum Kom.

“…The taxonomy of the OTUs was created using VSEARCH. The SILVA and Unite databases were used to determine the taxonomy of bacterial and fungal OTUs, respectively [60,61]. Non-bacterial or non-fungal sequences were removed using the amplicon package within R [49].…”