FunFlux

Integrated workflow for fungal short-read genome assembly and annotation.

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FunFlux v1.1.0

June 2026

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Synopsis

FunFlux is a Snakemake workflow designed for genome assembly and annotation of fungal short reads sequenced with Illumina technology. Pre-assembled fungal genomes can be analyzed with the bundled Funnotator flavour. The workflow includes read preprocessing, assembly, contig selection and decontamination, assembly quality control, genome completeness assessment, ITS extraction and taxonomic assignment, repeat masking, gene prediction, functional annotation, and summary reporting.

Table of Contents

• Rationale
• Description
• Installation
• Configuration
• Running FunFlux
• Output
• Acknowledgements
• Citation
• References

Rationale

The analysis of fungal whole-genome sequencing (WGS) data involves a complex series of bioinformatic steps that can be challenging to execute manually. This process is time-consuming, prone to errors, and difficult to reproduce. FunFlux addresses these problems by providing an automated Snakemake workflow for fungal genome assembly and annotation.

FunFlux is designed to streamline the annotation process with funannotate in the absence of RNA sequencing evidence. It relies on both ab initio annotation and protein FASTA sequences from organisms of the same species or genus to enhance the accuracy of gene prediction and annotation.

Description

Here's a breakdown of the FunFlux workflow:

1. Preprocessing:

   • Raw reads are checked for Illumina phiX contamination using bowtie2.
   • Adapters are removed and reads are filtered using fastp.

2. Assembly:

   • Filtered reads are assembled into contigs with SPAdes.

3. QC, Decontamination, Completeness Assessment, and ITS extraction:

   • Contigs are filtered based on minimum length and coverage.
   • Filtered reads are mapped back to contigs using bowtie2 and samtools. The resulting BAM file is analyzed with QualiMap.
   • Local alignments of contigs are performed against the NCBI core nt database using BLAST+.
   • Contaminant contigs are checked with BlobTools. Contig selection is handled by a dedicated taxonomy selector script. The selector can keep all contigs, keep the most abundant assigned genus, include specified genera, exclude specified genera, and optionally discard no-hit contigs.
   • Genome assembly quality is evaluated with QUAST.
   • Genome completeness is assessed with BUSCO.
   • ITS markers are detected and extracted with ITSx.
   • ITS taxonomic assignment is performed with the SINTAX classifier in VSEARCH using the UNITE database.

4. Gene Prediction:

   FunFlux is optimized to leverage funannotate when RNA sequencing data is not available. Instead, it uses external protein evidence and ab initio predictors to produce fungal gene models. The workflow splits the previous monolithic prediction step into:

   funannotate_preprocess -> repeat_masking -> funannotate_prediction
   

   This split makes repeat masking replaceable without changing the downstream funannotate prediction and annotation logic.

   • Preprocessing the genome assembly

     • N50 calculation and contig duplication checking are performed by funannotate clean.
     • Contigs are sorted and headers are renamed with funannotate sort.

   • Repeat masking

     • The default strategy is direct tantan softmasking.
     • The optional advanced strategy runs RepeatModeler and RepeatMasker with the de novo repeat library produced by the former.

   • Incorporating protein evidence

     • DIAMOND is used by funannotate to search for homology between the genome and provided protein sequences from related taxa, as well as the UniProt database bundled in the configured funannotate database snapshot. Matches are refined by Exonerate.

   • Ab initio gene prediction

     • GeneMark-ES is made available to funannotate and contributes self-trained ab initio gene predictions from the genome sequence.
     • BUSCO conserved genes are passed to Augustus to improve training.
     • SNAP, GlimmerHMM, and other funannotate-supported predictors contribute to consensus model building.

   • Combining and refining predictions

     • EVidenceModeler combines evidence into final gene models.
     • tRNA genes are predicted with tRNAscan-SE.
     • NCBI-compatible annotation files are generated by funannotate.

5. Gene Annotation:

   Gene annotation integrates multiple tools and culminates in a final funannotate annotate step:

   • InterProScan is expected as an external local installation.
   • EggNOG-mapper is used for orthology and functional annotation.
   • antiSMASH detects secondary metabolite biosynthetic gene clusters. The antiSMASH database directory is a temporary Snakemake output and should be removed after antiSMASH jobs complete.
   • The configured funannotate database snapshot contains resources such as UniProt, MEROPS, dbCAN, Pfam, GO, MIBiG, InterPro, and BUSCO outgroups.

6. Report:

   • Results are parsed and aggregated with MultiQC.

7. Funnotator flavour:

   • Funnotator is the annotation-only FunFlux flavour for pre-assembled fungal genome FASTA files. It skips Illumina preprocessing and SPAdes assembly, then applies the same fungal annotation logic where relevant.

⬆ Back to Table of Contents

Installation

FunFlux automatically downloads most Conda-managed dependencies and several workflow databases. Some external databases and licensed tools still require manual installation before running the workflow.

1. Download FunFlux:

   git clone https://github.com/iLivius/FunFlux.git

2. Install Snakemake:

   FunFlux relies on Snakemake to manage workflow execution.

   conda create -c conda-forge -c bioconda -n snakemake snakemake
   conda activate snakemake

3. Databases and external software:

   • NCBI core nt database:

     rsync --list-only rsync://ftp.ncbi.nlm.nih.gov/blast/db/core_nt.*.gz | grep '.tar.gz' | awk '{print "ftp.ncbi.nlm.nih.gov/blast/db/" $NF}' > nt_links.list
     cat nt_links.list | parallel -j4 'rsync -h --progress rsync://{} .'
     find . -name '*.gz' | parallel -j4 'echo {}; tar -zxf {}'
     
     wget -c 'ftp://ftp.ncbi.nlm.nih.gov/pub/taxonomy/taxdump.tar.gz'
     tar -zxvf taxdump.tar.gz
     
     wget 'ftp://ftp.ncbi.nlm.nih.gov/blast/db/taxdb.tar.gz'
     tar -zxvf taxdb.tar.gz
     
     wget -c 'ftp://ftp.ncbi.nlm.nih.gov/pub/taxonomy/accession2taxid/nucl_gb.accession2taxid.gz'
     gunzip nucl_gb.accession2taxid.gz

     NOTE: the complete NCBI core nt database and taxonomy-related files require more than 200 GB of disk space. It is not needed when using Funnotator on already assembled FASTA files.

   • UNITE database:

     Manual download is not required for the standard workflow. The config contains:

     links:
       unite_its_link: https://s3.hpc.ut.ee/plutof-public/original/338a1413-6039-4e00-b5cf-410346a1e366.gz

     The workflow downloads and decompresses this file automatically into:

     03.post-processing/ITS_extraction/unite_its_sintax.fasta
     

   • eggNOG diamond database:

     conda create -n eggnog-mapper eggnog-mapper=2.1.13
     conda activate eggnog-mapper
     mkdir /data/eggnog_db
     download_eggnog_data.py --data_dir /data/eggnog_db -y

     NOTE: the eggNOG database requires roughly 50 GB of disk space.

   • Download and set up GeneMark-ES/ET:

     • Visit the GeneMark download page.

     • Download GeneMark-ES/ET.

     • Change Perl script shebangs if necessary:

       cd /path/to/gmes_linux_64_4
       find . -type f -name "*.pl" -exec sed -i '1s|^#!/usr/bin/perl|#!/usr/bin/env perl|' {} +
       ./gmes_petap.pl

   • Download and set up InterProScan:

     The version tested was v5.77-108.0, which is not downloaded by the workflow. The official download instructions are available here. Download the tested archive and checksum file:

     wget https://ftp.ebi.ac.uk/pub/software/unix/iprscan/5/5.77-108.0/interproscan-5.77-108.0-64-bit.tar.gz
     wget https://ftp.ebi.ac.uk/pub/software/unix/iprscan/5/5.77-108.0/interproscan-5.77-108.0-64-bit.tar.gz.md5
     md5sum -c interproscan-5.77-108.0-64-bit.tar.gz.md5

     Then extract and initialize InterProScan:

     tar -pxvzf interproscan-5.77-108.0-64-bit.tar.gz
     cd interproscan-5.77-108.0
     python3 setup.py -f interproscan.properties
     ./interproscan.sh

   • Repeat masking tools:

     tantan, RepeatModeler, and RepeatMasker are installed through the workflow Conda environment. The advanced masking mode uses the custom RepeatModeler library with RepeatMasker -lib, so no separate curated RepeatMasker database is required for that mode.

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Configuration

Before running FunFlux, edit config/config.yaml. The file is organized into links, directories, files, resources, and parameters.

• links

  • phix_link: PhiX genome reference used as Illumina sequencing control.
  • funannotate_link: URL to a frozen funannotate database snapshot. This is a database archive, not the funannotate executable version.
  • unite_its_link: URL to the compressed UNITE SINTAX FASTA used for ITS classification.

• directories

  • input_dir: Directory containing paired-end FASTQ reads. Requirements:

    1. Files can only have fastq, fq, fastq.gz, or fq.gz extensions.
    2. All files in a run must use the same extension.
    3. Sample names must not contain underscores or these characters: _*#@%^/! ?&:;|<>.
    4. Use _R1 and _R2 to define paired-end reads, for example strain-42_R1.fastq.gz and strain-42_R2.fastq.gz.

    For Funnotator, provide assembled FASTA files instead. FASTA files can use fasta, fa, or fna extensions and sample names must not contain underscores.

  • output_dir: Directory where output files, .snakemake metadata, and Conda environments are stored. Reusing the same output directory avoids reinstalling environments.

  • blast_db: Path to the NCBI core nt database and taxonomy files.

  • eggnog_db: Path to the eggNOG-mapper database.

  • genemark_dir: Path to the gmes_linux_64_4 directory.

  • funannotate_db: Path where the funannotate database snapshot is installed or already available.

• files

  • annotation_params: Path to a tab-delimited annotation parameter file. An example is provided in config/annotation_parameters.tsv.

    #Sample	Species	Proteins	Model
    ARSEF3097	Beauveria bassiana	/path/to/proteins.faa	fusarium_graminearum
    strain-42	Lecanicillium fungicola	/path/to/proteins.faa	fusarium_graminearum

  • iprscan: Path to the InterProScan shell script.

• resources

  • threads: Maximum CPUs passed to individual tools inside rules. Some tools are capped internally where higher values are not useful or can be unstable.
  • ram_gb: Maximum RAM value used by memory-aware tools such as SPAdes and QualiMap.

  --cores is Snakemake's scheduler limit. resources: threads controls tool-level thread arguments.

• parameters

  Decontamination

  decontamination:
    mode: off
    discard_no_hit: true
    include_genera:
    include_genera_by_sample:
    exclude_genera:
    exclude_genera_file:
    sample_overrides:

  Available modes:

  • off: keep all contigs. discard_no_hit is ignored.
  • auto: keep the most abundant assigned genus.
  • include: keep only listed genera.
  • exclude: remove listed genera.

  discard_no_hit: true removes BLAST no-hit contigs only when the mode is auto, include, or exclude. In auto and include modes, the selector can treat selected genus aliases and retained legacy prefixes as equivalent. This is mainly a safeguard against false contig removal when BLAST/BlobTools assigns related or recently reclassified genera inconsistently. Although FunFlux targets fungal genomes, bacterial genera may appear here because bacterial contamination can occur in fungal WGS assemblies. Alias-based decisions are recorded in contig_taxonomy_decisions.tsv with reasons such as auto_genus_alias or included_genus_alias. exclude mode remains exact. These aliases are heuristic safeguards, not a formal taxonomic reconciliation system.

  Genera can be supplied directly:

  exclude_genera: Acidovorax;Pseudomonas;Sphingomonas

  or through a one-genus-per-line file:

  exclude_genera_file: /path/to/exclude_genera.txt

  Optional sample overrides use a tab-separated file:

  sample<TAB>mode<TAB>include_genera<TAB>exclude_genera<TAB>discard_no_hit
  strain-42<TAB>exclude<TAB><TAB><TAB>true
  

  Use real tab characters, not the literal string <TAB>. A sample-specific include or exclude list replaces the global list for that sample.

  ITS taxonomy

  its_taxonomy_cutoff: 0.8

  Repeat masking

  masking_method: tantan
  repeatmodeler_quick: true
  repeatmodeler_ltrstruct: false

  To use the advanced RepeatModeler + RepeatMasker strategy, change only masking_method:

  masking_method: repeatmodeler_repeatmasker

  Available masking methods:

  • tantan: default lightweight softmasking.
  • repeatmodeler_repeatmasker: runs BuildDatabase -> RepeatModeler -> RepeatMasker -lib <RepeatModeler library> -xsmall.

  repeatmodeler_quick: true adds RepeatModeler -quick. repeatmodeler_ltrstruct: true adds RepeatModeler -LTRStruct.

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Running FunFlux

FunFlux can be executed as a Snakemake workflow.

conda activate snakemake
snakemake --configfile config/config.yaml --sdm conda --cores 12 --jobs 2

If you resume an interrupted run, keep using the same configuration file. When Snakemake reports incomplete output after a stopped job, rerun with --rerun-incomplete. If you intentionally updated workflow code but want to continue based only on file timestamps, add --rerun-triggers mtime.

snakemake --configfile config/config.yaml --unlock
snakemake --configfile config/config.yaml --sdm conda --cores 12 --jobs 2 --rerun-triggers mtime --rerun-incomplete

To analyze pre-assembled fungal genomes with Funnotator:

conda activate snakemake
snakemake --snakefile workflow/Funnotator --configfile config/config.yaml --sdm conda --cores 24 --jobs 2

After a successful run, optional cleanup of bulky intermediate files can be inspected with:

workflow/scripts/clean_funflux_output.sh --target /path/to/output_dir

To actually remove the listed files:

workflow/scripts/clean_funflux_output.sh --run --target /path/to/output_dir

The cleanup script is dry-run by default and refuses targets that do not look like FunFlux or Funnotator output directories.

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Output

Here's a breakdown of the sub-directories created by FunFlux within the main output folder. Funnotator produces a similar but simplified annotation-only output.

├── 01.pre-processing
├── 02.assembly
├── 03.post-processing
├── 04.annotation
├── logs
└── report

• 01.pre-processing: QC and statistics of raw and trimmed reads, produced by fastp v1.0.1.

• 02.assembly: Output from SPAdes v4.2.0. This directory contains raw contigs, filtered contigs, and selected/decontaminated contigs.

• 03.post-processing: Contains:

  • mapping_evaluation: QualiMap v2.3 output.
  • contaminants: BLAST+ v2.16.0 and BlobTools v1.1.1 decontamination output, including genus composition and contig_taxonomy_decisions.tsv.
  • assembly_evaluation: QUAST v5.3.0 output.
  • completeness_evaluation: BUSCO v6.0.0 output from --auto-lineage-euk.
  • ITS_extraction: ITSx v1.1.3 output and VSEARCH v2.30.0 SINTAX classification against the automatically downloaded UNITE reference.

• 04.annotation: Contains:

  • repeatmasking: RepeatModeler v2.0.8 and RepeatMasker v4.2.3 output, present inside each sample when repeatmodeler_repeatmasker is selected.

  • iprscan: InterProScan v5.77-108.0 XML output.

  • eggnog: EggNOG-mapper v2.1.13 annotation output.

  • antismash: antiSMASH v8.0.4 secondary metabolite output.

  • funannotate: Prediction and annotation directories from funannotate v1.8.17.

    ├── annotate_misc
    ├── annotate_results
    ├── logfiles
    ├── predict_misc
    └── predict_results
    

• report: MultiQC v1.33 report aggregating fastp, QualiMap, QUAST, BUSCO, and other supported outputs.

⬆ Back to Table of Contents

Acknowledgements

This work was originally supported by the BeXyl project (Beyond Xylella, Integrated Management Strategies for Mitigating Xylella fastidiosa impact in Europe), funded under the HORIZON-CL6-2021-FARM2FORK-01-04 programme (grant agreement No. 101060593).

Citation

If you use FunFlux, please cite:

Antonielli, L., Brader, G., & Compant, S. (2024). FunFlux: Integrated workflow for fungal genome assembly and annotation. Zenodo. https://doi.org/10.5281/zenodo.13612159

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