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Fungi’s lost data being found thanks to Species Hypothesis

Andy Taylor
Andy Taylor
“The concept we have used within the database is revolutionary, it is a tool to handle the unknown. The creation of UNITE allowed researchers to allocate identities to sequence data within a particular group of fungi with a high degree of certainty, but now there is so much data from unidentified taxa that we need new tools and new approaches to create references and allow comparisons between studies. The species hypothesis (SH) will go some way to solving this issue"

There is no generally agreed upon, up-to-date system for fungal classification, with several different, partly incompatible classification systems used across many mycological resources. This confusion is partly due to the vast number of unidentifiable fungal species that are recovered in any molecular analysis of environmental samples. The DNA sequence database (UNITE) has created the concept of Species Hypothesis (SH) with a view to linking and communicating data representing this dark taxonomy.

UNITE was first launched in 2003, the initial aim was to create a reliable sequence database, which included only high-quality sequences obtained from expertly-identified vouchered fungal collections. These identification ‘barcodes’ could then be used to identify unknown sequences derived from environmental samples with a high degree of confidence.

Since then, field mycology has undergone far-reaching changes, with vast amounts of sequence data being produced from environmental samples which contain high numbers of taxa not included in reference data sets. It is therefore difficult to refer to the same taxon across studies and classifications without a reference system. This results in valuable data being lost. To address these needs, the new release of UNITE allows representation of an arbitrary number of alternative taxonomic systems. The database has introduced the Taxon Hypothesis (TH) concept that allows communication of sequence-based Species Hypothesis (SH) over many classifications at the same time.

Dr. Andy Taylor, a fungal ecologist in the James Hutton Institute’s Ecological Sciences group and current UNITE board member said: “The concept we have used within the database is revolutionary, it is a tool to handle the unknown. The creation of UNITE allowed researchers to allocate identities to sequence data within a particular group of fungi with a high degree of certainty, but now there is so much data from unidentified taxa that we need new tools and new approaches to create references and allow comparisons between studies. The species hypothesis (SH) will go some way to solving this issue.

“UNITE regularly collates all relevant sequence data from global databases and create phylogenetic trees linking sequences at various levels of similarity. If there are names which can be assigned to the group of DNA sequences at different levels of relatedness, then they are used. However, in most cases this is not possible at the species level. This is where we use a species hypothesis (SH), with each SH assigned a unique digital object identifier (DOI), which is fixed in time. This means that data from different studies can then be compared when SH DOIs are used for communication, which is a massive shift forward.”   

At frequent intervals, all available sequence data from international depositories are uploaded automatically into UNITE, screened for quality and then added to the existing data. In this way the database is kept up-to-date at a global scale.

Wherever possible all associated metadata is also captured. This includes information on herbarium, geographical location (with illustrations or photos), ecology, taxonomy, and nomenclature - all of it vital to the interpretation of results. The taxon hypothesis pages are due for launch late 2018.

More information about the recent developments to the database can be found in a paper published by Oxford Academic, available here.  

More information from: 

Adam Walker, Communications Officer, Tel:01224 395095 (direct line), +44 (0)344 928 5428 (switchboard).


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The James Hutton Research Institute is the result of the merger in April 2011 of MLURI and SCRI. This merger formed a new powerhouse for research into food, land use, and climate change.