What is UFCG?

UFCG stands for Universal Fungal Core Genes, which is a combined suite of the fungal marker gene database and the bioinformatic pipeline tool, developed to provide an accessible and credible software suite for the phylogenetic study of fungi.

UFCG project features:

  • Pre-defined marker gene database, along with the downloadable sequences and HMMs
  • Reference fungal species database, providing taxonomic information and pre-extracted genetic markers
  • Pipeline tool and manual to analyze your own biological sequences

Gene set database

UFCG gene database is a combination of canonical genes and core genes of fungi.

The canonical genes were first defined and included by literature search, which have been frequently used and accepted by fungal taxonomists.

In addition, the novel set of genes were also included using the concept of core gene: the most widely used methodology for the genome-based phylogenetic tree reconstruction. Core genes can be defined as:

  • Genes that are present in a majority of species
  • Genes that are proven to be orthologous as a single copy

We prepared the organized list of the genes including their annotations, visualized MSAs and downloadable resources.

Species database

UFCG species database contains a list of 1,587 reference species that have been used to define the novel markers.

You can navigate through the list by sorting or searching their taxonomic names.

For each species, you have an access to:

  • Link to the NCBI Assembly database with their accession number
  • Downloadable ITS, UFCG, and BUSCO sequences in JSON format, pre-extracted with UFCG pipeline

In addition, we provide downloadable archives of the resources from 10,984 assemblies, encompassing both taxonomically representative and redundant entries.

Bioinformatics pipeline

We designed the pipeline for the users who wish to analyze hundreds of whole-genome assemblies. The core modules of the pipeline are briefed here to help you understand and maximize the utility of our pipeline.

  • profile module extracts previously described core genes, using the corresponding HMM profiles.
    • In addition, ITS nucleotide marker and BUSCO sequences can also be extracted as an option.
  • Extracted UFCG profiles from each genome are stored in a single .ucg formatted file. See below for more information about this file format.
  • tree module carries out phylogenetic analysis using a set of .ucg files from different species. Specifically, tree module features:
    • Multiple alignment and concatenation of the genes
    • Phylogenetic analysis using core gene profiles
    • Calculation and embedding of GSI

List of modules in the pipeline

Module Input Output Description
profile .fa .ucg Extracts UFCG profile from Fungal whole genome sequences
profile-rna .fq .ucg Extracts UFCG profile from Fungal RNA-seq reads
train .fa .fa .hmm Trains and generates sequence model of fungal markers
align .ucg .fa Aligns genes and provides multiple sequence alignments from UFCG profiles
tree .ucg .nwk .trm Reconstructs the phylogenetic relationship with UFCG profiles
prune .trm .nwk Fixes UFCG tree labels or gets a single gene tree

File formats used in the pipeline

Format Input Output Desciption
.fa .fna .fasta profile train train align Standard FASTA file format for genome sequences or MSAs.
.fq .fastq profile-rna - Standard FASTQ file format for sequence reads
.ucg align tree profile profile-rna JSON-formatted profile containing extracted sequences of core genes, along with the metadata of the genome. These files can also be read and edited via any text editor.
.nwk - tree prune Standard Newick format file for phylogenetic trees.
.trm prune tree JSON-formatted file containing Newick-formatted trees and the metadata of individual gene trees and concatenated UFCG tree.



Dongwook Kim1
Cameron Gilchrist2
Jon Jongsik Chun1,2,3,
Martin Steinegger1,2,3,4,

1 Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea

2 School of Biological Sciences, Seoul National University, Seoul, Korea

3 Institute of Molecular Biology and Genetics, Seoul National University, Seoul, Korea

4 Artificial Intelligence Institute, Seoul National University, Seoul, Korea

Corresponding authors