!!! Please visit https://github.com/Plant-Net/LEAPH-EffectorComb.git for the final version of the tool !!!

LEAPH - (ensembLe model for Effector clAssification in PHytoplasmas)

LEAPH is an ensemble machine learning predictor able to classify effector proteins form other function-generic proteins. It is compose by 4 classification models: Random Forest, XGBoost, Gaussian Naive Bayes and Multinomial Naive Bayes.

LEAPH output is a binary classification of proteins (effector/non effector), associated with the models agreement score. To be considered effector, a protein has to reach a correcteness prediction-probability >= 90% by at least one of the models.

This repository contains both LEAPH source code and scripts to build-up the feature tables necessary for its application along with a Singularity3.7 container for a smoother usage of this effector proteins predictor. Moreover in directory app_LEAPH/Effector-Comb/ a Shiny App showing different configuration of Self-Organizing-Maps it's available to explore the results from LEAPH application to 13 phytoplasma proteomes. The Shiny App usage is explained in the README_SOM.md file in the aforementioned directory

Usage

LEAPH can be used as a stand-alone script or with the available singularity3.7 container (recommended)

LEAF from container

To properly use LEAPH you can clone the directory and execute the LEAF1.0.sh file in the provided container

---LEAF.sh help---

-first argument: -dft/pre_computed_feature_table.tsv	use "-dft" (do feature table) if no pre-computed feature table is available (LEAF will start the prediction of the features and feature table build-up)
							use /path/to/pre_computed_feature_table.tsv otherwise
							(the column in feature_table must be in the same order of those in training_feature_tables.tsv) 
if -dft:
	-second argument: path/to/protein_sequence.fasta	the input file in FASTA format containing AA sequences (can be a selection of proteins or an entire proteome)
	-third argument: path/to/output_directory		ouput directory in which to save both feature predictions/feature table/LEAF putative effector prediction 
	-fourth argument: suffix/prefix				to distinguish the current run of LEAF (e.g. strain name, CaPmali_AT)

otherwise:
	-second argument: path/to/output_directory		ouput directory in which to save LEAF putative effector prediction 
	-third argument: prefix					to distinguish the current run of LEAF (e.g. strain name, CaPmali_AT)

Container Usage

singularity exec -B binding/dirs LEAPH1.0.simg /opt/LEAPH.sh -dft /path/to/aa_sequences.fasta /path/to/output_dir suffix/prefix

or

singularity exec -B /binding/dirs LEAPH1.0.simg /opt/LEAPH.sh /path/to/feature_table.tsv /path/to/output_dir prefix

LEAPH stand-alone

The required python3.8.10 libraries are:

• biopython
• pandas
• joblib

The required software to be installed separately or used by other containers (e.g. singularity), are:

• SignalP - v4.1
• TMHMM - v2.0
• MobiDB-lite - v3.0
• (Prosite - v1.86 if you are changing the training set of +)

To properly use LEAF you can download the directory and execute the following steps:

Predict features

signalp4.1 -f long -s notm -t gram+ -T /path/to/tmpdir /path/to/aa_sequences.fasta > signalp_out.txt

tmhmm /path/to/aa_sequences.fasta > tmhmm_out.txt

python mobidb-lite.py -bin mobidb-lite/binx -l /path/to/aa_sequences.fasta -o mobidb_out.txt

or

singularity exec -B /binding/dirs /path/to/signalp4.1.simg signalp4.1 -f long -s notm -t gram+ -T /path/to/tmpdir /path/to/aa_sequences.fasta > signalp_out.txt

singularity exec -B /binding/dirs /path/to/tmhmm2.0.simg tmhmm /path/to/aa_sequences.fasta > tmhmm_out.txt

singularity exec -B /binding/dirs /path/to/mobidb-lite.simg python /opt/mobidb-lite.py -bin /opt/mobidb-lite/binx -l /path/to/aa_sequences.fasta -o mobidb_out.txt

Build-up the feature table

python3.8.10 ./build_feature_table.py -i /path/to/aa_sequences.fasta\
					-o /path/to/output_dir/feature_table_name\
					-sp /path/to/feature_prediction_dir/signalp_out.txt\
					-tm /path/to/feature_prediction_dir/tmhmm_out.txt\
					-mb /path/to/feature_prediction_dir/mobidb_out.txt\
					-pr ./pre_feature_prediction/prosite_eff.fasta\
					-fte ./training_feature_tables/feature_table_eff_std.tsv\
					-prm ./pre_feature_prediction/prosite_motifs_profiles_eff.txt\
					-ms ./pre_feature_prediction/monster_score_eff.tsv\
					-mmc ./pre_feature_prediction/df_motif_CLUMPs_eff.tsv

Predict putative effector proteins with LEAF

python3.8.10 ./LEAPH1.0.py -ft /path/to/feature_table_name.tsv -o /path/to/output_dir -px distinguishable_name 

Feature Considered

Protein features considered for classification are:

• Protein length
• Signal Peptide (SignalP 4.1)
• Transmembrane Region (TMHMM)
• AA in transmembrane domain
• first 60 AA
• Probability of N-in
• Warning signal sequence
• Intrinsically disordered regions (MobiDB-lite)
• Motifs/Profiles in AA sequence (Prosite)
• CLUMPs (MOnSTER)
• bin of sequence and presence of CLUMPs

Classification

• Methods: Random Forest, XGBoost, Gaussian Naive Bayes, Multinomial Naive Bayes
• Overall data-set partition and validation: 5-fold cross validation
• Goodness of model:
  • Accuracy
  • F-measure
  • Precision-Recall
  • Feature importance (SHAP)
• number of variables (features): 30