-
miniconda - Follow instruction on https://docs.conda.io/projects/miniconda/en/latest/
make sure you have the conda version 23.7.3
conda activate base conda install conda=23.7.3 -y -
clone the repository:
git clone https://github.com/EnvGen/phyloBOTL
Installation option 1 - step by step, in case you already have some programs installed or databases downloaded
Make sure you are using the same version as indicated hereafter:
Conda R_env:
cd phyloBOTL
conda env create -f conda_env/R_env.yaml -y
conda activate R_env
Rscript --vanilla support/Install_Rpackages.R <numberofcpus>
Snakemake (https://snakemake.readthedocs.io/en/stable/getting_started/installation.html):
conda create -n phylobotl_env -c bioconda snakemake=7.25.0 Python=3.11.4 -y
conda activate phylobotl_env
conda config --set channel_priority strict
Genomad and genomad_db (https://portal.nersc.gov/genomad/index.html)
conda create -n genomad_env -c conda-forge -c bioconda genomad=1.6.1 -y
conda activate genomad_env
mkdir -p <directory_DB_path>/genomad_env/data
genomad download-database <directory_DB_path>/genomad_env/data
conda deactivate
EGGNOG: emapper.py and EGGNOG database (https://github.com/eggnogdb/eggnog-mapper/wiki/eggNOG-mapper-v2.1.5-to-v2.1.12#user-content-v2112)
conda create -n eggnog_mapper_env -c bioconda -c conda-forge eggnog-mapper=2.0.1 -y
conda activate eggnog_mapper_env
mkdir -p <directory_DB_path>/eggnog_mapper_env/data
download_eggnog_data.py --data_dir <directory_DB_path>/eggnog_mapper_env/data -y
conda deactivate
Installation option 2 - all in one go, in case you want to install/download all the required programs and databases from scratch.
cd phyloBOTL
bash install_key_envs.sh <directory_DB_path> <cpus>
<directory_DB_path> a directory where you want to download the databases. number of cpus to download R packages when creating the R_env
if you want to use gtdb or kSNP4 to build the phylogenetic tree
A. gtdbtk - source: https://ecogenomics.github.io/GTDBTk/installing/index.html#installing - conda: https://ecogenomics.github.io/GTDBTk/installing/bioconda.html
conda create -n gtdb_env -c conda-forge -c bioconda gtdbtk=2.1.1 -y
conda activate gtdb_env
download-db.sh
# Set the environment variable to the directory containing the GTDB-Tk reference data
conda env config vars set GTDBTK_DATA_PATH="/path/to/unarchived/gtdbtk/data"
B. kSNP4 - source: https://sourceforge.net/projects/ksnp/files/latest/download - manual: https://sourceforge.net/projects/ksnp/files/kSNP4.1%20User%20Guide.pdf/download
Modify config file:
nano support/config_phylobotl.json
"workdir":"/abs/path/to/phyloBOTL",
"threads": 20, - CPUs
"input": {
"File_list":"GENOME_LIST", - csv file <sample name>,<abs/path/to/contig.fasta[.gz]>,<group label>,<special_group_name>
"group_1_label": "C", - your selection
"group_1_name": "Clinical", - your selection
"group_2_label": "E", - your selection
"group_2_name": "Environmental", - your selection
"Special_group_name": "None" - your selection or "None". For instance "Baltic Sea"
},
"tree_file": "Tree_rooted_boot.treefile", - tree file name. If you already have a tree, set the path to it here and the pipeline won't build the tree
"tree_using": "iqtree",
"iqtree": {
"iqtree_params":"-m GTR+I+G -B 1000 -bnni",
"iqtree_rooted": "TRUE" - or "FALSE"
},
"FastTree_params":"-gtr -nt -gamma",
"GTDB": {
"gtdb_params": "--bacteria",
"taxa_filter":"p__Proteobacteria",
"outgroup_taxon":"p__Firmicutes"
},
"KSNP": {
"kSNP4_param_phylo_method":"ML", - Maximum Likelihood
"kSNP4_param_genome_fraction": "core",
"Path_to_kSNP4pkg": "/usr/local/kSNP4pkg"
},
"Prokka_params": "--rawproduct --quiet",
"Proteome_dir": "proteome", - directory containing the protein files <sample name>.faa.
"orthology":
{
"orthofinder_parameters": "-f proteome -a 4 -S diamond -og", - double check that name in -f <Proteom_dir> corresponds to "Proteome_dir" above
"ortholog_count_table": "Orthogroups.GeneCount.tsv",
"ortholog_table": "Orthogroups.tsv",
"path_to_orthologs_sequences": "Orthologues/Results_dir/Orthogroup_Sequences"
},if you provide a full path to existing "ortholog_count_table","ortholog_table" and "path_to_orthologs_sequences" files, the pipeline won't run orthofinder
"up_to_Eggnog_all_orthologs": False, - Set to True if you want to perform only the EggNOG annotation of orthologs groups
"Eggnog_all_orthologs_selection": "longest", - Options : "longest", "random". criterium for selection one sequences from the groups of orthologs group, either the longest sequence or one randomly selected.
"path_to_eggnog_db":"directory_DB_path/eggnog_mapper_env/data", - abs path to eggnog database directory.
"PHYLOGLM": {
"phyloglm_Bootnumber":0,- phyloglm parameter, number of independent bootstrap replicates, 0 means no bootstrap.
"p_adj_value_cutoff":0.05,
"orthologue_ratio_in_genome_dataset":0.95,- In this case if the orthologue is present/absent in 95% of the genomes, it won't be considered in the analysis.
"phyloglm_btol_number":10, - phyloglm parameter, (logistic regression only) bound on the linear predictor to bound the searching space.
"phyloglm_outfiles_prefix":"Vv"
},
"synteny_visualization": {
"present_in_at_least_n_genomes": 10, - parameter used when creating the Graph, representing the weigth cutoff to be included in graph
"locus_size_window": 40000, - bps, window used when looking for enriched orthologs locilised in the region
"max_n_genomes_per_fig": 22 - 22 representative genomes will be included in the .pdf figure
},
"tax_specific_protein_annotation": { - if you want to annotated Enriched and depleted orthologs using a specific database. For instance UniRef90 Vibrionacea
"status": True,
"path_to_database": "/abs/path/to/Your_Specific_database.fasta"
},
"genomad":{ "include": False, - Set to True in you want to predict plasmid and Virus on genomes using genomad
"path_to_genomad_db": "directory_DB_path/genomad_env/data/genomad_db", - abs path to genomad database directory
"Genomad_score_cut_off": 0.8, - Minimum score to classify a contig as plasmid (or Virus)
"params_genomad": "--cleanup --conservative --enable-score-calibration", - genomad parameters. The flag --enable-score-calibration will work if there are more than 1000 sequences in the fasta file (in a genome file)
},
"output_dir": "Results_with_IQTREE_rooted" - Output directory name
and save CTRL+x, y
Run:
Option 1:
Using a laptop or one node on a HPC.
conda activate phylobotl_env
snakemake -s phylobotl.smk --use-conda --conda-frontend conda --cores <numberofcpus>
Option 2:
If you want to run the pipeline using several nodes in a HPC:
a. Download cookiecutter:
conda create -n cookiecutter_env -c conda-forge cookiecutter -y
conda activate cookiecutter_env
b. Create the profile directory and answer the questions:
profile_dir="/abs/path/to/phyloBOTL/.config/snakemake"
mkdir -p "$profile_dir"
template="gh:Snakemake-Profiles/slurm"
cookiecutter --output-dir "$profile_dir" "$template"
conda deactivate
c. Run the pipeline:
conda activate phylobotl_env
snakemake --profile .config/snakemake/phylobotl_slurm -s phylobotl.smk --use-conda --conda-frontend conda
├── Genomes -- Folder with all the genomes that will be used
│ ├── <isolate1>.fa -- expample of genome name file
├── Annotations
│ ├── GBK_files -- Prokka output gbk files for each genome
│ ├── GFF_files -- Prokka output gff files for each genome
│ ├── Orthologues/orthologues_gff.tsv -- gff file format of orthologs present in genomes
│ └── Rep_seq_Orth_groups -- Folder with representative sequence (randomly selected or the longest) eggNOG annotation for each Ortholog group
├── Genes
│ ├── <isolate1>.ffn -- Gene sequences, Prokka output
├── Orthologues
│ └── Results_dir -- Orthofinder output directory
├── Pangenome_graph -- Pangenome output directory
│ ├── pangenome.h5
│ ├── MSA -- Folder with the core msa file.
├── phyloglm_input -- intermediare file used as input for phyloBOTL.R, generated bu the pipeline.
│ ├── Annotations.txt
│ └── Orthogroups.tsv
├── Prokka_out -- Rest of proka output files
├── proteome -- folder with proteins sequences for each genome
│ ├── <isolate1>.faa
├── GENOMAD -- folder with GENOMAD output for each genome
│ ├── Genomad_output_<isolate1>
├── Trees -- Output directory from phylogenetic tree software used (e.g, IQTREE)
│ ├── IQ_TREE.bionj
In <output_dir>: -- Output result directory based on user-defined groups
├── Annotations
│ ├── Depleted_eggNOG
│ ├── Depleted_GBK_files
│ ├── Depleted_KEGG
│ ├── Depleted_KEGG_core
│ ├── Depleted_Loci -- co-localization depleted orthologs EggNOG/KEGG annotation folder
│ ├── Enriched_eggNOG
│ ├── Enriched_GBK_files
│ ├── Enriched_KEGG
│ ├── Enriched_KEGG_core
│ ├── Loci -- co-localization Enriched orthologs EggNOG/KEGG annotation folder
│ ├── Specific_db
│ ├── Vv_Candidates_depleted_orthologues.tsv
│ ├── Vv_Candidates_enriched_orthologues.tsv
│ ├── Vv_core_depleted_orthologues.tsv
│ ├── Vv_core_enriched_orthologues.tsv
│ ├── Vv_depleted_core_orthologues_annotation.tsv
│ ├── Vv_Depleted_orthologues_annotation.tsv
│ ├── Vv_Enriched_core_orthologues_annotation.tsv
│ └── Vv_Enriched_orthologues_annotation.tsv
├── co_localization_figures -- Enriched orthologues
├── co_localization_figures_Depleted -- Depleted orthologues
├── Depleted_Orthologues_DNA_sequences
│ ├──<OrthologD1>.fna
├── Enriched_Orthologues_DNA_sequences
│ ├──<OrthologE1>.fna
├── Unsupervise -- Several unsupervised machine learning methods output directory based on enriched and depleted orthologues
├── Unsupervise_core -- Several unsupervised machine learning methods output directory based on core enriched and depleted orthologues
├── Visualization
│ ├── Vv_Tree_with_core_orthologues_fan_branch.length.pdf
│ ├── Vv_Tree_with_depleted_core_orthologues_fan_branch.length.pdf
│ ├── Vv_Tree_with_enriched_core_orthologues_fan_branch.length.pdf
│ └── Vv_Tree_with_Selected_orthologues_fan_branch.length.pdf
├── Vv_Depleted_core.txt -- List of core Depleted orthologues
├── Vv_Depleted.txt -- List of Depleted orthologues
├── Vv_Enriched_core.txt -- List of core Enriched orthologues
└── Vv_Enriched.txt -- List of Enriched orthologues