PREDIPATH

PREDIcting the bacterial PATHogenicity on plants

A Plant Pathogenicity Predictor Pipeline

This repository is used to Predipath scripts and databases.

Introduction

Make sure you have git installed in your computer:

sudo apt install python-setuptools python-dev build-essential # Ubuntu

Download the PrediPath folder from Github:

git clone https://github.com/felipelira/PrediPath
cd PrediPath
chmod +x predipath_pipeline.py
ln -s predipath_pipeline.py /usr/local/bin/predipath

Pipeline

Download the files in https://github.com/felipelira/PrediPath/tree/master/databases/general and move them to your desired PATH.

The basic command is:

blastp -db Predipath_DB_aa_nr_raw -query [your_proteome.faa]

Development

PrediPath development was started by the collaboration between the Institut de Recheche Agronomic - INRA and the Université Bretagne Loire - UBL.

Fundings

This project, performed at the Institut de Recherche en Horticulture et Semences (IRHS), is supported by fundings from:

1. People Programme Marie Curie Actions of the European Union’s Seventh Framework Programme (FP7/2007-2013) under REA grant agreement n. PCOFUND-GA-2013-609102, through the PRESTIGE Programme coordinated by Campus France;
2. University Bretagne Loire, Programme d’Attractivité Post-Doctorale;
3. Regional program “Objectif Végétal, Research, Education and Innovation in Pays de la Loire”, supported by the French Region Pays de la Loire and Angers Loire Métropole.

Development of PREDIPATH Database

The PREDIPATH-DB was compiled using the sequences from five public repositories: Virulence Factors Database (VFDB) (Liu, Zheng, Jin, Chen, & Yang, 2019)⁠, Antibacterial, Biocide and Metal Resistance Genes Database (BacMet) (Pal, Bengtsson-Palme, Rensing, Kristiansson, & Larsson, 2014)⁠, Comprehensive Antibiotic Resistance Database (ARPCARD) (McArthur et al., 2013)⁠, Antibiotic Resistance Genes Database (ARDB) (Liu & Pop, 2009)⁠, and the Antibiotic Resistance Gene-ANNOTation (ARG-ANNOT) (Gupta et al., 2014)⁠. All the sequences files were merged and clustered using CD-HIT with 95% of identity and 80% of coverage.

  1. Liu, B., Zheng D., Jin Q., Chen L. and Yang J.: VFDB 2019: a comparative pathogenomic platform with an interactive web interface. Nucleic Acids Research, Vol. 47, Database issue D687–D692 (2019). doi: 10.1093/nar/gky1080
  2. Pal C., Bengtsson-Palme J., Rensing C., Kristiansson E. and Larsson D.G.J.: BacMet: antibacterial biocide and metal resistance genes database. Nucleic Acids Research, Vol. 42, D737–D743 (2014). doi:10.1093/nar/gkt1252
  3. McArthur A.G., Waglechner N., Nizam F., Yan A., Azad M.A., Baylay A.J., Bhullar K., Canova M.J., De Pascale G., Ejim L., Kalan L., King A.M., Koteva K., Morar M., Mulvey M.R., O’Brien J.S., Pawlowski A.C., Piddock L.J.V., Spanogiannopoulos P., Sutherland A.D., Tang I., Taylor P.L., Thaker M., Wang W., Yan M., Yu T., Wright G.D.: The Comprehensive Antibiotic Resistance Database. Antimicrobial Agents and Chemotherapy, Vol. 57, Number 7, p. 3348–3357 (2013). doi:10.1128/AAC.00419-13
  4. Liu B., Pop M.: ARDB—Antibiotic Resistance Genes Database. Nucleic Acids Research, 2009, Vol. 37, Database issue D443–D447 (2009). doi:10.1093/nar/gkn656
  5. Gupta, S.K., Padmanabhan, B.R., Diene, S.M., Lopez-Rojas, R., Kempf, M., Landraud, L., Rolain, Jean-Marc. ARG-ANNOT, a New Bioinformatic Tool To Discover Antibiotic Resistance Genes in Bacterial Genomes. Antimicrobial Agents and Chemotherapy, Vol. 58 Number 1, p. 212–220 (2014). doi:10.1128/AAC.01310-13

Who we are

• Felipe Lira
• [Martial Briand]
• [Perrine Portier]
• [Gilles Hunault]
• [Claudine Landes]
• [Marion Fischer-Le Saux]

This directory is created to store the databases used by PrediPath.

For each genus, you will retrieve a specific database containing specific marquers to detect the potential bacterial pathogenicity.