The Functional Order (FunOrder) tool - Identification of essential biosynthetic genes through computational molecular co-evolution – searches for co-evolutionary linked genes in a set of inputted genes. The functionality and applicability was tested with biosynthetic gene clusters (BGCs). The resulting information can be used to choose which genes of a gene cluster are most likely the core genes necessary for the biosynthesis of a secondary metabolite. The flexibility and adaptability of the core program allows the integration of any protein database and can thus be adapted for different phyla and research objectives. FunOrder might be used for the analysis of co-evolution on a whole proteome, enabling the genome wide detection of evolutionary linked genes.
The Functional Order (FunOrder) tool - Identification of essential biosynthetic genes through computational molecular co-evolution. FunOrder is copyright 2020 Gabriel A. Vignolle, Denise Schaffer, Robert L. Mach, Astrid R. Mach-Aigner and Christian Derntl, and is released under the MIT License. If you find FunOrder useful to your work, please cite:
Vignolle GA, Mach RL, Mach-Aigner AR and Zimmermann C (2022) FunOrder 2.0 – a method for the fully automated curation of co-evolved genes in fungal biosynthetic gene clusters. Front. Fungal Biol. 3:1020623. doi: 10.3389/ffunb.2022.1020623
FunOrder 2.0 – a fully automated method for the identification of co-evolved genes Gabriel A Vignolle, Robert L Mach, Astrid R Mach-Aigner, Christian Derntl bioRxiv 2022.01.10.475597; doi: https://doi.org/10.1101/2022.01.10.475597
https://zenodo.org/record/5827722 and DOI: 10.5281/zenodo.5827722 for the code and
Vignolle GA, Schaffer D, Zehetner L, Mach RL, Mach-Aigner AR, Derntl C (2021) FunOrder: A robust and semi-automated method for the identification of essential biosynthetic genes through computational molecular co-evolution. PLoS Comput Biol 17(9): e1009372. doi: https://doi.org/10.1371/journal.pcbi.1009372
The Functional Order (FunOrder) tool - Identification of essential biosynthetic genes through computational molecular co-evolution Gabriel A Vignolle, Denise Schaffer, Robert L Mach, Astrid R Mach-Aigner, Christian Derntl. bioRxiv 2021.01.29.428829; doi: https://doi.org/10.1101/2021.01.29.428829
The software input files are biosynthetic gene clusters (BGC) with gene translations in genbank file format or fasta format, that contain the amino acid sequences of all the genes found in the BGC of interest.
FunOrder performs a sequence similarity search using blastp on our manually curated database, multiple sequence alignment using the ClustalW algorithm, calculates the best scoring ML tree with RAxML (Randomized Axelerated Maximum Likelihood) for each gene and uses the TreeKO algorithm to calculate the pairwise distances between these trees. Based on these distances FunOrder 2 automatically determines the optimal number of clusters in the output, and a subsequent k-means clustering based on the first three principal components of the PCAs clusters the genes/proteins into co-evolutionary linked protein families. See our newest publications for further details.
FunOrder 2 is provided with a database of ascomycete proteomes and can therefore be used for the detection of co-evolution of proteins in this fungal division. If other divisions, classes, or even kingdoms shall be analyzed, a suitable new proteome database must be compiled and tested, see our Wiki for further details.
Third party programs
Perl packages:
Python packages:
R packages:
- readr
- stats
- gplots
- car
- mdatools
- xlsx
- cluster
- NbClust
- randtests
These instructions should work on Debian-based linux distributions such as Ubuntu.
First we install the EMBOSS package according to the instructions. Then we install RAxML according to the instructions. After R, Perl and Python is installed, install the ete2 package.
pip install ete2
Now install the R packages if not already installed.
install.packages('readr') # at the R prompt
install.packages('stats') # at the R prompt
install.packages('gplots') # at the R prompt
install.packages('car') # at the R prompt
install.packages('mdatools') # at the R prompt
install.packages('xlsx') # at the R prompt
install.packages('cluster') # at the R prompt
install.packages('NbClust') # at the R prompt
install.packages('randtests') # at the R prompt
Now download FunOrder funorder_XX.tar.xz and unpack the archive.
tar -xf funorder_XX.tar.xz
open the scripts funorder.sh ; funorder_fasta_only.sh ; funorder_server.sh ; funorder_server_fasta_only.sh change 'SOURCEDIR' value in line 43 in funorder.sh ; funorder_fasta_only.sh and line 45 in funorder_server.sh ; funorder_server_fasta_only.sh:
SOURCEDIR=~/funorder_proj/funorder_XX/
to (path to the funorder_XX directory: e.g. ~/path/to/your/directory/)
SOURCEDIR=~/path/to/your/directory/funorder_XX/
You can now add the FunOrder/pipeline directory to your $PATH environmental variable. Alternativeley you can call the FunOrder/pipeline directory directly.
Run FunOrder from the folder containing the gbk file you want to analyze. (cd ~/path/to/your/gbk_files)
sh ~/path/to/directory/funorder_XX/funorder.sh [Thread number] [gbk file] [absolute path to outputdirectory] [database]
or if you added the FunOrder/pipeline directory to your $PATH environmental variable.
sh funorder.sh [Thread number] [gbk file] [absolute path to outputdirectory] [database]
following folders will be created in the chosen outputdirectory:
- file.gbk.analysis
- file.gbk.analysis/alignment
- file.gbk.analysis/hits
The output of the optional BLAST analysis is saved in the /file.gbk.analysis directory. The output of FunOrder is saved in /file.gbk.analysis/alignment
| File | Description |
|---|---|
| FunOrder_Supplementary_Rplots.pdf | PDF file with the Analyze.R output as described in our publication FunOrder 2 |
| FunOrder_clustering_Rplots_pred.pdf | PDF file with the Analyze_clustering_pred.R output as described in our publication FunOrder 2 |
| cluster_definition_pred.xlsx | XLSX file with the Analyze_clustering_pred.R output as described in our publication FunOrder 2 |
| strict_distance.matrix | matrix of the strict distance |
| evol_distance.matrix | matrix of the evolutionary [speciation] distance |
| Internal_coevolution_quotient.txt | text file containing the ICQ analysis |
if the automatic clustering failed then the outputfiles are
| File | Description |
|---|---|
| FunOrder_Supplementary_Rplots.pdf | PDF file with the Analyze.R output as described in our publication FunOrder 2 |
| FunOrder_clustering_Rplots_defined.pdf | PDF file with the Analyze_clustering_defined.R output as described in our publication FunOrder 2 |
| cluster_definition_3.xlsx | XLSX file with the Analyze_clustering_defined.R output as described in our publication FunOrder 2 |
| strict_distance.matrix | matrix of the strict distance |
| evol_distance.matrix | matrix of the evolutionary [speciation] distance |
| Internal_coevolution_quotient.txt | text file containing the ICQ analysis |
Run FunOrder from the folder containing the fasta file you want to analyze. (cd ~/path/to/your/fasta_files)
sh ~/path/to/directory/funorder_XX/funorder_fasta_only.sh [Thread number] [fasta file] [absolute path to outputdirectory] [database]
or if you added the FunOrder/pipeline directory to your $PATH environmental variable.
sh funorder_fasta_only.sh [Thread number] [fasta file] [absolute path to outputdirectory] [database]
following folders will be created in the chosen outputdirectory:
- file.fasta.analysis
- file.fasta.analysis/alignment
- file.fasta.analysis/hits
The output of the optional BLAST analysis is saved in the /file.gbk.analysis directory. The output of FunOrder is saved in /file.fasta.analysis/alignment
| File | Description |
|---|---|
| FunOrder_Supplementary_Rplots.pdf | PDF file with the Analyze.R output as described in our publication FunOrder 2 |
| FunOrder_clustering_Rplots_pred.pdf | PDF file with the Analyze_clustering_pred.R output as described in our publication FunOrder 2 |
| cluster_definition_pred.xlsx | XLSX file with the Analyze_clustering_pred.R output as described in our publication FunOrder 2 |
| strict_distance.matrix | matrix of the strict distance |
| evol_distance.matrix | matrix of the evolutionary [speciation] distance |
| Internal_coevolution_quotient.txt | text file containing the ICQ analysis |
if the automatic clustering failed then the outputfiles are
| File | Description |
|---|---|
| FunOrder_Supplementary_Rplots.pdf | PDF file with the Analyze.R output as described in our publication FunOrder 2 |
| FunOrder_clustering_Rplots_defined.pdf | PDF file with the Analyze_clustering_defined.R output as described in our publication FunOrder 2 |
| cluster_definition_3.xlsx | XLSX file with the Analyze_clustering_defined.R output as described in our publication FunOrder 2 |
| strict_distance.matrix | matrix of the strict distance |
| evol_distance.matrix | matrix of the evolutionary [speciation] distance |
| Internal_coevolution_quotient.txt | text file containing the ICQ analysis |
Run FunOrder from the folder containing the gbk file you want to analyze. (cd ~/path/to/your/gbk_files)
sh ~/path/to/directory/funorder_XX/funorder_server.sh [Thread number] [gbk file] [absolute path to outputdirectory] [database]
or if you added the FunOrder/pipeline directory to your $PATH environmental variable.
sh funorder_server.sh [Thread number] [gbk file] [absolute path to outputdirectory] [database]
following folders will be created in the chosen outputdirectory:
- file.gbk.analysis
- file.gbk.analysis/alignment
- file.gbk.analysis/hits
The output of FunOrder is saved in /file.gbk.analysis/alignment
| File | Description |
|---|---|
| FunOrder_Supplementary_Rplots.pdf | PDF file with the Analyze.R output as described in our publication FunOrder 2 |
| FunOrder_clustering_Rplots_pred.pdf | PDF file with the Analyze_clustering_pred.R output as described in our publication FunOrder 2 |
| cluster_definition_pred.xlsx | XLSX file with the Analyze_clustering_pred.R output as described in our publication FunOrder 2 |
| strict_distance.matrix | matrix of the strict distance |
| evol_distance.matrix | matrix of the evolutionary [speciation] distance |
| Internal_coevolution_quotient.txt | text file containing the ICQ analysis |
if the automatic clustering failed then the outputfiles are
| File | Description |
|---|---|
| FunOrder_Supplementary_Rplots.pdf | PDF file with the Analyze.R output as described in our publication FunOrder 2 |
| FunOrder_clustering_Rplots_defined.pdf | PDF file with the Analyze_clustering_defined.R output as described in our publication FunOrder 2 |
| cluster_definition_3.xlsx | XLSX file with the Analyze_clustering_defined.R output as described in our publication FunOrder 2 |
| strict_distance.matrix | matrix of the strict distance |
| evol_distance.matrix | matrix of the evolutionary [speciation] distance |
| Internal_coevolution_quotient.txt | text file containing the ICQ analysis |
within the antiSMASH output-folder create a new directory "funorder_output"
mkdir funorder_output
then from within the antiSMASH output-folder run following command:
for file in *cluster*.gbk; do echo $file; sh ~/path/to/directory/funorder_XX/funorder_server.sh [Thread number] $file [absolute path to "funorder_output" directory] [database] ; done
This will perform a FunOrder analysis for each cluster predicted by antiSMASH.
Run FunOrder from the folder containing the fasta file you want to analyze. (cd ~/path/to/your/fasta_files)
sh ~/path/to/directory/funorder_XX/funorder_server_fasta_only.sh [Thread number] [fasta file] [absolute path to outputdirectory] [database]
or if you added the FunOrder/pipeline directory to your $PATH environmental variable.
sh funorder_server_fasta_only.sh [Thread number] [fasta file] [absolute path to outputdirectory] [database]
following folders will be created in the chosen outputdirectory:
- file.fasta.analysis
- file.fasta.analysis/alignment
- file.fasta.analysis/hits
The output of FunOrder is saved in /file.fasta.analysis/alignment
| File | Description |
|---|---|
| FunOrder_Supplementary_Rplots.pdf | PDF file with the Analyze.R output as described in our publication FunOrder 2 |
| FunOrder_clustering_Rplots_pred.pdf | PDF file with the Analyze_clustering_pred.R output as described in our publication FunOrder 2 |
| cluster_definition_pred.xlsx | XLSX file with the Analyze_clustering_pred.R output as described in our publication FunOrder 2 |
| strict_distance.matrix | matrix of the strict distance |
| evol_distance.matrix | matrix of the evolutionary [speciation] distance |
| Internal_coevolution_quotient.txt | text file containing the ICQ analysis |
if the automatic clustering failed then the outputfiles are
| File | Description |
|---|---|
| FunOrder_Supplementary_Rplots.pdf | PDF file with the Analyze.R output as described in our publication FunOrder 2 |
| FunOrder_clustering_Rplots_defined.pdf | PDF file with the Analyze_clustering_defined.R output as described in our publication FunOrder 2 |
| cluster_definition_3.xlsx | XLSX file with the Analyze_clustering_defined.R output as described in our publication FunOrder 2 |
| strict_distance.matrix | matrix of the strict distance |
| evol_distance.matrix | matrix of the evolutionary [speciation] distance |
| Internal_coevolution_quotient.txt | text file containing the ICQ analysis |