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. 2020 May 28;17(1):20190046.
doi: 10.1515/jib-2019-0046.

Fungal metabarcoding data integration framework for the MycoDiversity DataBase (MDDB)

Irene Martorelli  1   2 , Leon S Helwerda  1 , Jesse Kerkvliet  2 , Sofia I F Gomes  2 , Jorinde Nuytinck  2 , Chivany R A van der Werff  1 , Guus J Ramackers  1 , Alexander P Gultyaev  1 , Vincent S F T Merckx  2   3 , Fons J Verbeek  1
Affiliations

Fungal metabarcoding data integration framework for the MycoDiversity DataBase (MDDB)

Irene Martorelli et al. J Integr Bioinform. .

Abstract

Fungi have crucial roles in ecosystems, and are important associates for many organisms. They are adapted to a wide variety of habitats, however their global distribution and diversity remains poorly documented. The exponential growth of DNA barcode information retrieved from the environment is assisting considerably the traditional ways for unraveling fungal diversity and detection. The raw DNA data in association to environmental descriptors of metabarcoding studies are made available in public sequence read archives. While this is potentially a valuable source of information for the investigation of Fungi across diverse environmental conditions, the annotation used to describe environment is heterogenous. Moreover, a uniform processing pipeline still needs to be applied to the available raw DNA data. Hence, a comprehensive framework to analyses these data in a large context is still lacking. We introduce the MycoDiversity DataBase, a database which includes public fungal metabarcoding data of environmental samples for the study of biodiversity patterns of Fungi. The framework we propose will contribute to our understanding of fungal biodiversity and aims to become a valuable source for large-scale analyses of patterns in space and time, in addition to assisting evolutionary and ecological research on Fungi.

Keywords: DNA barcoding; biogeography; data integration; environmental samples; fungal biodiversity.

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Figures

Figure 1:
Figure 1:
Data acquisition workflow for retrieving data from Pubmed and SRA repositories. Once the data is retrieved, the final step of the data acquisition involves the curation of the two data types, the metadata retrieved from PubMed and SRA (Metadata Curation), and of the raw HTS data of the SRA fastq files (Sequencing Data Curation).
Figure 2:
Figure 2:
Components and Dependencies of the fungal metabarcoding data integration.
Figure 3:
Figure 3:
UML data model of the MDDB.
Figure 4:
Figure 4:
Major phylum ZOTU representatives for each continent.
Figure 5:
Figure 5:
Diversity of ZOTUs belonging to the Russulaceae family and their distribution display in a spatial range. Map display by using Marker GeoCharts (http://developers.google.com/chart/interactive/docs/gallery/geochart).
Figure 6:
Figure 6:
Overlap of ZOTUs among continents.
See this image and copyright information in PMC

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