mpwt 0.3.5a1

Multiprocessing for Pathway-Tools

Pathway-tools multiprocessing script

mpwt is a python package for running Pathway-Tools on multiple genomes using multiprocessing.

There is no guarantee that this script will work, it is a Work In Progress in early state.

Table of contents

• Installation

  • Requirements

  • Using pip

• Use

  • Input data

  • Input files created by mpwt

  • Command Line Example

  • Python Example

  • Useful functions

  • Errors

Installation

Requirements

You must have an environment where Pathway-Tools is installed. Pathway-Tools can be obtained here. For some versions you need to have Blast installed on you system, for further informations look at this page.

If your OS doesn’t support Pathway-Tools, you can use a docker. If it’s your case, look at Pathway-Tools Multiprocessing Docker. It is a dockerfile that will create container with Pathway-Tools, its dependancies and this package. You just need to give a Pathway-Tools installer as input.

Using pip

pip install mpwt

Use

Input data

The script takes a folder containing sub-folders as input. Each sub-folder contains a genbank file. Genbank files must have the same name as the folder in which they are located and also finished with a .gbk.

Folder_input
├── species_1
│   └── species_1.gbk
├── species_2
│   └── species_2.gbk
├── species_3
│   └── species_3.gbk
..

Pathway-Tools will run on each genbank file. It will create an output folder inside the folder containing all the result files from the PathoLogic inference for each species. You can also choose another output folder.

Genbank file example:

LOCUS       scaffold1         XXXXXX bp    DNA     linear   INV DD-MMM-YYYY
DEFINITION  My species genbank.
ACCESSION   scaffold1
VERSION     scaffold1
KEYWORDS    Key words.
SOURCE      Source
ORGANISM  Species name
            Taxonomy; Of; My; Species; With;
            The; Genus.
FEATURES             Location/Qualifiers
    source          1..XXXXXX
                    /scaffold="scaffold1"
                    /db_xref="taxon:taxonid"
    gene            START..STOP
                    /locus_tag="gene1"
    mRNA            START..STOP
                    /locus_tag="gene1"
    CDS             START..STOP
                    /locus_tag="gene1"
                    /db_xref="InterPro:IPRXXXXXX"
                    /EC_number="X.X.X.X"
                    /translation="AMINOAACIDSSEQUENCE"

Input files created by mpwt

Three input files are created by mpwt. Informations are extracted from the genbank file. myDBName corresponds to the name of the folder and the genbank file. taxonid corresponds to the taxonid in the db_xref of the source feature in the genbank. species_name is extracted from the genbank file.

organism-params.dat:
ID  myDBName
STORAGE FILE
NCBI-TAXON-ID   taxonid
NAME    species_name

genetic-elements.dats:
NAME
ANNOT-FILE  gbk_pathname
//

script.lisp:
(in-package :ecocyc)
(select-organism :org-id 'myDBName)
(create-flat-files-for-current-kb)

Command Line Example

mpwt is usable as a command line.

mpwt -f path/to/folder/input [-o path/to/folder/output] [-d] [-v]

Optional argument are identified by [].

-f input folder as described in Input data.

-o output folder containing PGDB data or dat files (see -d arguments).

-d will create dat files and only move them inside the output folder.

-v verbose.

Possible uses of mpwt:

mpwt -f path/to/folder/input

Create PGDBs of studied organisms inside ptools-local.

mpwt -f path/to/folder/input -d

Create PGDBs of studied organisms inside ptools-local and create dat files.

mpwt -f path/to/folder/input -o path/to/folder/output

Create PGDBs of studied organisms inside ptools-local. Then extract the files inside the output folder.

mpwt -f path/to/folder/input -o path/to/folder/output -d

Create PGDBs of studied organisms inside ptools-local and create dat files. Then extract the dat files inside the output folder.

Python Example

mpwt can be used in a python script with an import:

import mpwt

folder_input = "path/to/folder/input"
folder_output = "path/to/folder/output"

mpwt.multiprocess_pwt(folder_input, folder_output, dat_extraction=optional_boolean, size_reduction=optional_boolean, verbose=optional_boolean)

folder_input: folder containing sub-folders with Genbank file inside.

folder_output: output folder where all the result of Pathway-Tools will be moved. This argument is optional. If you don’t enter an argument, results will be stored in a folder named output inside the sub-folders containg Genbank file.

dat_extraction: True or nothing. If True, mpwt will only return dat files of the PGDB.

size_reduction: True or nothing. If True, after moving the data to the output folder, mpwt will delete files in ptools-local. This to decrease the size of the results.

verbose: True or nothing. If true, mpwt will be verbose.

Useful functions

1. multiprocess_pwt(folder_input, folder_output, dat_extraction=optional_boolean, size_reduction=optional_boolean, verbose=optional_boolean)

Run the multiprocess Pathway-Tools on input folder.

2. cleaning()

Delete all the previous PGDB and the metadata files.

This can also be used with a command line argument:

mpwt --clean

If you use clean and the argument -f input_folder, it will delete input files (‘script.lisp’, ‘pathologic.log’, ‘genetic-elements.dat’ and ‘organism-params.dat’).

mpwt --clean -f input_folder

2. delete_pgdb(pgdb_name)

With this command, it is possible to delete a specified db, where pgdb_name is the name of the PGDB (ending with ‘cyc’). It can be multiple pgdbs, to do this, put all the pgdb IDs in a string separated by a ‘,’.

And as a command line:

mpwt --delete mydbcyc1,mydbcyc2

4. ptools_path()

Return the path to ptools-local.

Errors

If you encounter errors (and it is highly possible) there is some tips that can help you resolved them.

For error during PathoLogic inference, a log is created where you launch the command. The log contains the summary of the build and the error for each species. There is also a pathologic.log in each sub-folders.

If the build passed you have also the possibility to see the result of the inference with the file resume_inference.tsv. For each species, it contains the number of genes/proteins/reactions/pathways/compounds in the metabolic network.

For others errors, currently nothing is made to help you. Maybe in the future.