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AIV_seeker is optimized for detecting and identification of Avian Influenza Virus from metagenomics data.

Introduction

The application of next generation sequencing (NGS) in infectious disease surveillance and outbreak investigations has become a promising area. Environmental sampling provides a method to collect and identify potentially dangerous pathogens that are circulating in an animal population, however detection of a low-abundance pathogen target in a large metagenomic background is still a challenge. AIV_seeker pipeline that is optimized for detecting and identifying low-abundance avian influenza virus (AIV) from metagenomic NGS data.

Features

• AIV_seeker can be used to automatically process metagenomics NGS data and generate subtyping results.
• A heatmap is generated to visualize the results.
• An experimental method is incorporated to deal with index-hopping issue resulted from Illumina platfrom
• The results can be used to generate phylogenetic tree through our another pipeline AIV_tree
• Web based service has been developed for Galaxy and IRIDA (http://206.12.95.227/).

How to install

Download the code from GitHub

$ git clone https://github.com/duanjunhyq/AIV_seeker.git

Install environments

$ cd AIV_seeker
$ bash install_envs.sh

Tips: If it takes too long to resolve Conda package conflicts, then try with mamba to install environments.

$ bash install_envs.sh mamba

If there is anything wrong with the environment, you can uninstall environment:

$ bash uninstall_envs.sh

How to use

Mount conda environement

$ source activate aiv_seeker

Test if AIV_sekeer is installed correctly. You can type aiv_seeker.pl in any directory.

$ aiv_seeker.pl

Then it will promot like this:

AIV_seeker: A pipeline for detecting avian influenza virus from NGS metagenomics Data

Usage: aiv_seeker.pl -i run_folder -o result_folder
         -i path for NGS fastq file directory
         -o result folder
         -s step number
            step 1: Generate the fastq file list
            step 2: Generate QC report
            step 3: Quality filtering
            step 4: First round search by Diamond
            step 5: Group reads to remove duplicates
            step 6: Second round search by BLAST
            step 7: Remove chimeric sequences
            step 8: Assign subtypes and generate report
            step 9: cross-contamination detection and generate report
         -f only run the designated step (defined by -s, default false), no parameter
         -b BSR score (default 0.4)
         -m margin of BSR score (default 0.3)
         -p percentage of concordant subtype (default 0.9)
         -t number of threads (default 2)
         -h display help message
         -l overlap level (default 0.7)
         -x threshold for identity (default 90%)
         -z threshold for chimeric check (default 75%)
         -c identity for clustering when dealing with cross-talking (default 0.97)
         -a run by cluster (default false)
         -g run galaxy job (default false)
         -w run debleeding process
         -k generate results based on paired reads only (remove unpaired reads)
         -u keep the intermediate files (default remove)

Once you put all your NGS raw data in a folder, you can run the pipeline like this

$ aiv_seeker.pl -i your_input_folder -o your_output_folder

If you are using Sun Grid Engine scheduler, you can add "-a" to let it run by cluster, and add "-t" to designate how many threads you want to use like this

$ aiv_seeker.pl -i your_input_folder -o your_output_folder -a -t 20

The default is not to keep intermediate files. If you want to keep intermediate files, you can add "-u"

$ aiv_seeker.pl -i your_input_folder -o your_output_folder -t 20 -u

The default is not to run debleeding step. You can add "-w" to get debleeding results

$ aiv_seeker.pl -i your_input_folder -o your_output_folder -t 20 -w

If you want to run some specific step, you can run it like this:

$ aiv_seeker.pl -i your_input_folder -o your_output_folder -t 20 -s 1 -f

How to find results