gongyh/metaron

Metagenomic opeRon Prediction pipeline. MetaRon presents the first pipeline for the prediction of metagenomic operons without any functional or experimental data.

Introduction

MetaRon (Metagenomic opeRon prediction pipeline) is a computational workflow for the prediction of operons from metagenomic data. The pipeline predicts metagenomic operons without any any functional or experimental data. It comes with options to process the metagenomic data starting from filtered raw reads, which includes: assembly into scaffolds via IDBA, data manipulation, gene prediction via prodigal and lastly operon prediction based on gene's co-directionality, intergenic distance (IGD) and promoters.

Metagenomic operon prediction redefines the operonic clusters by identifying promoters in co-directional genes with an intergenic distance threshold of <= 600 bp.

Installation

Prerequisites

MetaRon requires:

* Python (2.7 )
* IDBA (iterative De Bruijn Graph De Novo Assembler) [conda install -c bioconda idba]
* Prodigal [conda install -c bioconda prodigal]
* BDGP: Neural Network Promoter Prediction 2.2
* antiSMASH: antibiotics & Secondary Metabolite Analysis Shell (Optional: required for downstream analysis only.)
* BOWTIE (Optional: only required for downstream analysis)

If you already have Anaconda environment setup, you can quickly install the prerequisites using any one command from each section:

1. IDBA

   conda install -c bioconda idba

   conda install -c bioconda/label/cf201901 idba

2. Prodigal

   conda install -c bioconda prodigal

   conda install -c bioconda/label/cf201901 prodigal

3. antiSMASH

   conda install -c bioconda antismash

   conda install -c bioconda/label/cf201901 antismash

4. BOWTIE2

   conda install -c bioconda bowtie

   conda install -c bioconda/label/cf201901 bowtie

Install MetaRon

You can install MetaRon either from PyPi using pip and install it from the source. Please make sure you have already installed the above mentioned python libraries required to run MetaRon.

Install from PyPi::

pip install metaron

Install from the source::

tar -zxvf metaron-1.0.tar.gz
cd metaron-1.0
python setup.py install

How to use MetaRon

Once you have installed MetaRon, you can type:

metaron --help

to find the available commands and required parameters to run MetaRon.

-h, --help
Show this help message and exit

-n, --sample
Sample name without any dot/underscore/dash

-p, --process
1. ago: assembly gene prediction and operon prediciton 2. op: operon prediction only.

If 'ago', please provide the following parameters:

--sample,--process, --read_type, --read_length, --paired_1, --paired_2, --output

If 'op', please provide the following parameters:

--sample, --process, --igp, --isc, --tool, --output

-rt, --read_type
Enter read type. 'merge' if the reads are paired-end in two files. 'paired' if the reads are paired-end in one file.

-rl, --read_length
Enter 'l' if read length is longer than 128 bases and 'r' if read length is shorter than 128 bases

-pe1, --paired_1
Enter paired read file 1

-pe2, --paired_2
Enter paired read file 2

-pm, --paired_merged
Enter the paired end read file if both paired-end reads are in one file

-i, --igp
Select the gene prediction .tab file generated via MetageneMark or Prodigal

-j, --isc
Select the file containing all scaftigs

-t, --tool
Enter 1 for MetaGeneMark, 2 for Prodigal

-o, --output
Enter output destination folder

=======================================================NOTE=======================================================

1- If the selected --process is 'op', then please refer to the provided scaftig and gene prediction file format

2- Add NNPP2.2 path to the config.txt file

====================================================================================================================

Make predictions

Metagenomic operon prediction could be performed by providing filtered raw reads under the process "ago" i.e. assembly, gene prediction and operon identification

## test_sample: ERR022075.1.fastq & ERR022075.2.fastq

metaron --sample ERR022075 --process ago --read_type merge OR paired --read_length r OR l --paired_1 ~/path/to/ERR022075.1.fastq --paired_2 ~/path/to/ERR022075.2.fastq --output ~/path/to/output/directory/

If metagenomic scaffolds and gene predictions are already available, the user can predict operon under the process "op"

## test_assembly: ERR022075_scaf.fa 
## test_gene_prediction: ERR022075

metaron --sample ERR022075 --process op --igp ERR022075 --isc ERR022075_scaf.fa --tool 1 OR 2 --output ~/path/to/output/directory/

This will save metagenomic operon predictions Operon_File.tab. The prediction file will report the operonic information based on the above mentioned parameters.

Proposed downstream anslysis

1. Secondary Metabolites

   a. Secondary Metabolites identified from operonic sequences using antiSMASH

   b. Differntial abundance of Secondary Metabolites (condition-1 / Disease vs Condition-2 / Control)

2. Operonnic pathways

   a. Mapping raw metagenomic reads to operonic sequences using BOWTIE

   b. Submitting the mapped reads to Functional Mapping and Analysis Pipeline (FMAP)

   c. Identifying differential abundance of pathways between disease and control or environment-1 and environment-2

Support

If you have questions, or found any bug in the program, please write to us at

syedshujaat[at]comsats.edu.pk syedzaidi[at]arizona.edu