TreeTuner

A pipeline to coarse and fine-tuning large phylogenetic datasets via minimizing the redundancy and complexity

1. What is TreeTuner Pipeline?

The TreeTuner pipeline combines the software and tools required for both coarse- and fine-scale tuning.

1. For coarse tuning, to run TreeTrimmer (Maruyama et al., 2013) locally, pre-installed Ruby v2.5.1 and BioRuby v2.0.3 are required.

Usage: ruby treetrimmer.rb [Newick_tree_file] [Parameter_input_file] [Taxonomic_information_file] > output_file

2. For fine tuning, the necessary custom Perl scripts (rm_inparal_rank.pl and trim2untrim.pl) and Python script (rename_ncbi_blastdb.py) can be found at the TreeTuner GitHub website link . Pre-installed Perl 5 and BioPerl are required to run these scripts.

#Renaming the header of protein ID so as to pull out the taxonomic terms in the header.
Usage:python3 rename_ncbi_blastdb.py <FASTA File> <Taxon Id FILE> <Renamed FASTA File>

Usage: perl rm_inparal_rank.pl [tree file] [alignment file] [distance cutoff] [taxa not to remove] [taxa rank]
Usage: perl trim2untrim.pl [trimmed alignement] [untrimmed alignment]

3. To color the Newick tree, the Environment for Tree Exploration (ETE3) toolkit (Huerta-Cepas et al., 2016) and associated Python scripts (e.g., color_coarse_tuning_tree.py and color_fine_tuning_tree.py) are needed.

Usage: python3 color_coarse_tuning_tree.py <taxonomic_info_file> <newick_tree_file>
Usage: python3 color_fine_tuning_tree.py <newick_tree_file> 

2. Computational Requirement

TreeTuner users can either follow the Star Protocol pipeline to install the required packages or use the Conda environment file to deploy the pipeline.

2.1 Create an environment from Conda YAML file

To install all required dependencies, we provided a conda environment definition file (tested on the MacOSX system)TreeTunerENV.yaml with all the dependencies. The Perl, Ruby, Python, Shell scripts have been pre-storaged in the respective directory folders.

1. You need to have the Conda to be installed on your system. For example, to install Anaconda on MacOSX, Double-click the Anaconda3-2021.11-MacOSX-x86_64.pkg file.

2. Run the following command to create an environemnt from Conda YAML file

source ~/.bashrc
conda env create --file TreeTunerENV.yaml

3. After downloading a long list of required dependencies. Activate the Conda environemnt and follow the pipeine to run respective packages/custom scripts.

# To activate this environment, use
#
#     $ conda activate TreeTuner
#
# To deactivate an active environment, use
#
#     $ conda deactivate

2.2 Dependencies, package/script versions and directory paths

For example, BLAST searches(e.g., ncbi-blast-2.12.0) (Altschul et al., 1997); MAFFT v7(Katoh and Standley, 2013), BMGE v1.12 (Criscuolo and Gribaldo, 2010), trimAl v1.4 (Capella-Gutiérrez et al., 2009), FastTree v2.1 (Price et al., 2010) and IQ-TREE v1.6.12 (Nguyen et al., 2015)

Step 1-9: Preparing the BLAST results

cd /Your/Directory/Path/TreeTuner/Tutorial/TreeTuner_file_examples/Step1-9_preparing_input_files/

1. BLAST searches (e.g., ncbi-blast-2.12.0)
2. NCBI-nr BLAST v5 database
3. Batch Entrez
4. SeqKit

Step 10 & 17: Build a preliminary tree

cd /Your/Directory/Path/TreeTuner/Tutorial/TreeTuner_file_examples/Step10_building_preliminary_tree/

1. MAFFT v7
2. BMGE v1.12
3. trimAl v1.4
4. FastTree v2.1
5. IQ-TREE v1.6.12

Step 11: Coarse-tuning pipeline

cd /Your/Directory/Path/TreeTuner/Tutorial/TreeTuner_file_examples/Step11-14_coarse_tuning/

1. Ruby v2.5.1 and BioRuby v2.0.3
2. acc2tax
3. NCBI taxonomy database
4. TreeTrimmer

Step 12-14: Color the Newick Tree

cd /Your/Directory/Path/TreeTuner/Tutorial/TreeTuner_file_examples/Step11-14_coarse_tuning/color_tree/

1. python3
2. ETE3 toolkit
3. color_coarse_tuning_tree.py

Step 15-16: Renaming the protein ID

cd /Your/Directory/Path/TreeTuner/Tutorial/TreeTuner_file_examples/Step15-24_fine_tuning/rename/

1. python3
2. rename_ncbi_blastdb.py

Step 18-21: Fine-tuning pipeline

cd /Your/Directory/Path/TreeTuner/Tutorial/TreeTuner_file_examples/Step15-24_fine_tuning/Laura_perl/

1. Perl 5 and BioPerl
2. Python 3
3. rm_inparal_rank.pl, trim2untrim.pl, lauralib.pm
4. rename_ncbi_blastdb.py

Step 22-24: Color the Newick Tree

cd /Your/Directory/Path/TreeTuner/Tutorial/TreeTuner_file_examples/Step15-24_fine_tuning/color_tree/

1. python3
2. ETE3 toolkit
3. color_fine_tuning_tree.py

3. Instructions for adjusting parameters in Coarse- and fine-tuning pipeline

Here is an example file (i.e., clps_paramer_input.in) for the parameters in coarse-tuning pepiline

### TreeTrimmer parameter input file

### The cutoff value for de-replication (greater than this and will assess for removal)
# Specify a cutoff of support values (e.g. bootstrap values, posterior probability)
# either in integer (0-100) or decimal (0.0-1.0).
# Leave it blank or use default (0.0) for trees with no branch supports.

cutoff=0.0

### Query tag (optional: default is "query_tag=QUERY")
# If a tree includes a 'query' OTU, which is used as a reference 
# and is supposed to be retained after the de-replication process, 
# a string in the OTU name can be specified as a tag to avoid removal 

query_tag=564937

### Delimiter for categories of the taxonomic information
# (default: "taxon_delimiter=;\s" [semicolon plus single space])
# Only use regular expression for a space character

taxon_delimiter=;\s 

### Which taxonomic categories should be pruned? How many OTUs should be retained?
# The category information can be provided in Taxonomic_information_file,
# otherwise OTU names are used as labels of taxonomic information.
# example: Bacteria	2 (tab-delimited)

Bacteria	4
Archaea	3
Eukaryota	1

### How many OTUs should be retained in each supported branch of the tree unless specified above?

num_retained=1

Here is the guide for setting the parameters in fine-tuning pepiline

#### Trim your tree specifically to reduce taxonomic redundancy

## 'taxa_not_remove.txt' contains a list of taxa/phyla you don't want to reduce 
#(script looks for match to strings provided here)
564937.1
Rhodophyta
Haptista

## 'taxa_rank.txt' contains information on how to reduce specific genera/phyla/kingdoms...
#the way I have it, it will reduce at the phyla/class  (0 = domain, 1 = kingdom, 2 = phyla, 3 = subclade/class etc.)
#(e.g.,Eukaryota(0),Viridiplantae(1),Streptophyta(2),Streptophytina(3),etc..)
Bacteria 3
Archaea 2
Eukaryota 3

# The taxa rank is determined based on specific header formats as follows:
>Arabidopsis_thaliana@NCBI_NP_564937.1_Eukaryota_Viridiplantae_Streptophyta_Streptophytina_Embryophyta_Tracheophyta_Euphyllophyt a_Spermatophyta_Magnoliopsida_Mesangiospermae_eudicotyledons_Gunneridae_Pentapetalae_rosids_malvids_Brassicales_Brassicaceae_Ca

	
## 1.0 is the distance cutoff you want to consider 
#(less than this and will assess for removal)
>rm_inparal_rank.pl $FASTTREE $ALIGNEMNET 1.0 taxa_not_remove.txt taxa_rank.txt
>perl rm_inparal_rank.pl renamed_clps_aligned_trimmed.newick renamed_clps_aligned_trimmed.fasta 1.0 taxa_not_remove.txt taxa_rank.txt

## Then you run the following script to go from Will remove sequences 
#from the untrimmed alignement based on sequences present in the trimmed alignement
>trim2untrim.pl $GENE.qalign.genus_trimmed $GENE.qalign
# OR
>trim2untrim.pl $GENE.qalign.genus_trimmed $GENE.original.fasta

# e.g.,
>perl trim2untrim.pl renamed_clps_aligned_trimmed.fasta.genus_trimmed renamed_clps_aligned_trimmed.fasta

4. File index

├── TreeTuner pipeline
├── Step1-9_preparing_input_files
│   ├── BLASTP_clps.tsv
│   ├── BLASTP_clps_without_header.tsv
│   ├── clps.fasta
│   ├── clps_hits.fasta
│   ├── clps_hits_id_trimmed.txt
│   ├── clps_hits_id.txt
│   ├── clps_hits_trimmed.fasta
│   └── clps_taxonomic_info.txt
├── Step10_building_preliminary_tree
│   ├── clps.aligned.fasta
│   ├── clps.aligned.trimmed.fasta
│   └── clps.aligned.trimmed.newick
├── Step11-14_**coarse_tuning**
│   ├── clps_aligned_trimmed.newick__AT1G68660.1_parameter_input.in.tt0.0.tre
│   ├── clps_treetrimmer.out
│   ├── color_tree
│   │   ├── AT1G68660.1_aligned_trimmed.newick__AT1G68660.1_parameter_input.in.tt0.0.tre.png
│   │   ├── clps_aligned_trimmed.newick__AT1G68660.1_parameter_input.in.tt0.0.tre
│   │   ├── clps_taxonomic_info_clean.txt
│   │   └── color_coarse_tuning_tree.py
│   ├── ReadME.txt
│   ├── Sample
│   │   ├── clps.aligned.trimmed.newick
│   │   ├── clps_paramer_input.in
│   │   └── clps_taxonomic_info_clean.txt
│   └── treetrimmer.rb
└── Step15-24_**fine_tuning**
    ├── color_tree
    │   ├── color_fine_tuning_tree.py
    │   ├── renamed_clps_aligned_trimmed.fasta.fasttree
    │   └── renamed_clps_aligned_trimmed.fasta.fasttree.png
    ├── Laura_perl
    │   ├── FastTree
    │   ├── FastTree.c
    │   ├── Instructions.txt
    │   ├── lauralib.pm
    │   ├── renamed_clps_aligned_trimmed.fasta
    │   ├── renamed_clps_aligned_trimmed.fasta.fasttree
    │   ├── renamed_clps_aligned_trimmed.fasta.genus_trimmed
    │   ├── renamed_clps_aligned_trimmed.fasta_removedSeq
    │   ├── renamed_clps_aligned_trimmed.fasta_sub
    │   ├── renamed_clps_aligned_trimmed.newick
    │   ├── rm_inparal_rank.pl
    │   ├── taxa_not_remove.txt
    │   ├── taxa_rank.txt
    │   └── trim2untrim.pl
    └── rename
        ├── clps_acc2tax_prot_all.txt
        ├── clps_hits.fasta
        ├── clps_hits_no_description.fasta
        ├── new_renamed_clps_hits.fasta
        ├── renamed_clps_hits.fasta
        ├── rename_ncbi_blastdb.py
        ├── Sample
        │   ├── new_renamed_clps_hits.fasta
        │   ├── renamed_clps_aligned.fasta
        │   ├── renamed_clps_aligned_trimmed.fasta
        │   └── renamed_clps_aligned_trimmed.newick
        └── taxdump.tar.gz


10 directories, 52 files

5.Limitations

Taxonomy-based dataset trimming might result in biased OTU retention because highly diverse clades may be represented by more leaves than less diverse ones. Also, the TreeTuner pipeline is not fully automated and relies on user-defined OTUs. Nevertheless, we provide a step-by-step solution to guide users who need to trim down their large phylogenetic datasets for more rigorous downstream analysis.

6. References

• Zhang X., Hu Y., Eme L., Maruyama S.,Eveleigh R.JM, Curtis B.A., Sibbald S.J., Hopkins J.F., Filloramo1 G.V.,Wijk K.J.V., Archibald J.M., 2021.Protocol for TreeTuner: A pipeline for minimizing redundancy and complexity in large phylogenetic datasets. doi: upcoming
• Maruyama, S., Eveleigh, R. J. & Archibald, J. M. 2013. Treetrimmer: a method for phylogenetic dataset size reduction. BMC research notes, 6, 1-6.
• Sibbald, S. J., Hopkins, J. F., Filloramo, G. V. & Archibald, J. M. 2019. Ubiquitin fusion proteins in algae: implications for cell biology and the spread of photosynthesis. BMC genomics, 20, 1-13.

7. Copyright

Usage of this pipeline follows GPL-3.0 License. © Copyright (C) 2021. If you think this work is useful, please cite the related references above after using.