Cortinarius barcoding database of Western Siberia and adjacent areas

Registros

Los datos en este recurso de registros biológicos han sido publicados como Archivo Darwin Core(DwC-A), el cual es un formato estándar para compartir datos de biodiversidad como un conjunto de una o más tablas de datos. La tabla de datos del core contiene 624 registros.

también existen 1 tablas de datos de extensiones. Un registro en una extensión provee información adicional sobre un registro en el core. El número de registros en cada tabla de datos de la extensión se ilustra a continuación.

Este IPT archiva los datos y, por lo tanto, sirve como repositorio de datos. Los datos y los metadatos del recurso están disponibles para su descarga en la sección descargas. La tabla versiones enumera otras versiones del recurso que se han puesto a disposición del público y permite seguir los cambios realizados en el recurso a lo largo del tiempo.

Versiones

La siguiente tabla muestra sólo las versiones publicadas del recurso que son de acceso público.

¿Cómo referenciar?

Los usuarios deben citar este trabajo de la siguiente manera:

Filippova N, Zvyagina E, Bulyonkova T, Rudykina E, Ageev D, Gashkov S, Agafonova N, Vayshlya O (2026). Cortinarius barcoding database of Western Siberia and adjacent areas. Version 1.25. Yugra State University Biological Collection (YSU BC). Occurrence dataset. http://ipt.ugrasu.ru:8080/resource?r=cortdb&v=1.25

Derechos

Los usuarios deben respetar los siguientes derechos de uso:

El publicador y propietario de los derechos de este trabajo es Yugra State University Biological Collection (YSU BC). Esta obra está bajo una licencia Creative Commons de Atribución/Reconocimiento (CC-BY 4.0).

Registro GBIF

Este recurso ha sido registrado en GBIF con el siguiente UUID: e1c0913a-49b2-4d39-8dde-d840e93ffbb3.  Yugra State University Biological Collection (YSU BC) publica este recurso y está registrado en GBIF como un publicador de datos avalado por Participant Node Managers Committee.

Palabras clave

Occurrence

Contactos

Nina Filippova

• Proveedor De Los Metadatos ●
• Originador ●
• Punto De Contacto

researcher

Yugra State University

Khanty-Mansiysk

RU

filippova.courlee.nina@gmail.com

https://orcid.org/0000-0002-9506-0991

Elena Zvyagina

• Originador

researcher

Ygra State University

RU

Tatiana Bulyonkova

• Originador

researcher

Yugra State University

RU

Elena Rudykina

• Originador

researcher

Yugra State University

RU

Dmitry Ageev

• Originador

Independent researcher

Грибы Сибири

RU

Sergei Gashkov

• Originador

researcher

Tomsk State University

Tomsk

RU

Nadezhda Agafonova

• Originador

researcher

Tomsk State University

Tomsk

RU

Olga Vayshlya

• Originador

researcher

Tomsk State University

Tomsk

RU

Cobertura geográfica

The sequenced specimens were collected primarily in the Khanty-Mansi Autonomous Okrug (63%) and Novosibirsk Oblast (32%), with a small number from four other regions of Western Siberia. The samples were obtained from 31 localities (standardized to a 5 km radius), with 85% concentrated in just six key sites: the vicinity of Ugut village (22%), Novosibirsk city (16%), Shapsha village (15%), Mukhrino research station (12%), Karakansky Bor (12%), and Yugansky Nature Reserve (9%).

Cobertura temporal

Datos del proyecto

No hay descripción disponible

Métodos de muestreo

The collection comprises approximately 2,500 specimens of Cortinarius s.l., with about 1,000 selected for sequencing. The work involving collection consolidation, DNA extraction, and sequence generation spanned three years.

Descripción de la metodología paso a paso:

1. Fresh material was collected and described according to the standard methods used in fungal taxonomy (Clémençon et al. 2004). Fresh collections were photographed and described in the field. Morphological descriptions of fruitbodies were  based on the examination of fresh and dried material. A standard set of reagents (5% KOH, 1% Congo Red solution, Melzer’s reagent), and a Zeiss AxioStar microscope with a digital camera AxioCam ERc5s and Zeiss AxioVision 4.8.2 software were used for the study and documentation of microstructures. The specimen data were stored in the Specify 7 collection database, organized into dedicated tables for samples, collection events, personnel, DNA extracts, associated images (Fig. 2), morphological descriptions, and related measurement files.
2. The PCR was made using the TransDirect® Plant Tissue PCR Kit without DNA extraction. PCR reactions were performed in 20 μL of reaction mixtures containing 4 μL of ScreenMix (Evrogen), 0.2 μL of each PCR primer, 14 μL of deionized H2O, and 1.6 μL of template DNA. For amplification of the ITS region the primers ITS1 F (Gardes, Bruns, 1993) and ITS4 (White et al., 1990) were used. PCR cycle parameters were as follows: initial denaturation for 5 min at 95˚C, 30 cycles (denaturation for 20 sec at 95˚C, primers annealing for 30 sec at 54˚C, extend DNA for 60 sec at 72˚C), final extension for 7 min at 72˚C. PCR and sequence reaction products were purified using ExSPure (SkyGen), CleanMag DNA (Evrogen) and Dynabeads™ Sequencing Clean Up kits. Sequencing was performed with BrilliantDye™ Terminator (v3.1) Cycle Sequencing kit (NimaGen) using Applied Biosystems® Sanger Sequencing 3500 Series Genetic Analyzer. The obtained sequences underwent manual quality assessment. For sequences failing quality thresholds, re-sequencing was performed (either forward or reverse direction, as needed).
3. Raw sequences were processed with strict quality thresholds (minimum length: 50 bp, average quality: Q ≥ 20, end quality: ≥15). A sliding window approach (5 bp) was used to determine reliable sequence boundaries. Sequences were trimmed at positions where average quality dropped below threshold values. Forward and reverse sequences were aligned using the Needleman-Wunsch algorithm (parameters: match=2, mismatch=-3, gap_open=-5, gap_extend=-2). Contigs were assembled with ≥20 bp overlap, with conflicting positions resolved by prioritizing forward-read nucleotides. For single-read samples (forward or reverse only), truncated sequences were retained. The problematic sequences were processed manually. For each sequence, the following metrics were computed to estimate resulted sequence quality: length and count of ambiguous bases (N), percentage of N bases, composite quality score: *(length × 0.4) - (%N × 0.6)*, classified as: failed, low, medium, good, or excellent.  
4. The taxonomic assignment of sequences was performed through analysis with massBLASTer (BLAST+ 2.13.0) on the PlutoF platform using the UNITE (fungi) and INCD (fungi) databases with the 'Similar sequences (fast)' algorithm parameters. The exported BLAST results were incorporated into the original sequence table for further analysis. Several new fields were created in the dataset: a 'Taxon' field for recording the best taxonomic match (best hit), an 'other taxa' field containing all remaining species-level taxonomic assignments from the BLAST results for each sequence, and fields for quantitative BLAST parameters including percentage similarity and other relevant metrics.
5. The selection of best hits followed strict criteria. Only matches identified to species level with proper binomial nomenclature were considered, while entries containing markers like sp., cf., or aff. were excluded from consideration. The primary selection priority was given to type specimens (is_type = True) using NCBI database of type specimens. When multiple species-level matches existed, the secondary selection criterion was the highest BLAST Score among the qualifying matches. This approach ensured the most reliable taxonomic assignments while maintaining traceability of all potential matches and their respective alignment quality metrics. The complete set of BLAST parameters used in this analysis is documented for reproducibility purposes (Suppl. material 1).
6. The resulting annotated table contains several key components: the original sequence data, filtered best hits meeting the established criteria, all alternative species-level matches, and comprehensive alignment statistics (Table 1, Suppl. material 2). This structure facilitates both the immediate taxonomic interpretation and potential future re-evaluation of sequence assignments. The methodology emphasizes scientific rigor through its transparent selection hierarchy and preservation of all relevant matching data. Quantitative metrics from the BLAST alignments remain available for assessing match confidence and performing subsequent quality control analyses.
7. For subsequent submission to GenBank, SRA, and GBIF databases, we employed taxon names based on automated classification complemented by perentage identity. Alternatively, sequences showing 76-97% similarity to reference entries could be assigned to genus level only. Low percentage identity flags potentially novel taxa that may require further phylogenetic investigation and formal description through focused studies of specific taxonomic groups.

Datos de la colección

Referencias bibliográficas

1. Filippova NV, Bulyonkova TM, Zvyagina EA, Ageev DV, Rudykina EA. 2025. Cortinarius barcoding database of Western Siberia and adjacent areas. Biodiversity Data Journal 14: e196734, https://doi.org/10.3897/BDJ.14.e196734 https://doi.org/10.3897/BDJ.14.e196734
2. Filippova, N. (2025). Analysis code and source data for the article (Filippova NV, Bulyonkova TM, Zvyagina EA, Ageev DV, Rudykina EA «Cortinarius barcoding database of Western Siberia and adjacent areas»). Zenodo. https://doi.org/10.5281/zenodo.17598843 https://doi.org/10.5281/zenodo.17598843

Metadatos adicionales