NastyBugs: a simple method for extracting antimicrobial resistance information from metagenomes
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README.md

NastyBugs

A Simple Method for Extracting Antimicrobial Resistance Information from Metagenomes

Hackathon team: Lead: Steve Tsang - SysAdmins: Greg Fedewa, Daniel Quang, Sherif Farag - Writers: Matthew Moss, Alexey V. Rakov

How to cite this work in a publication: Tsang H, Moss M, Fedewa G et al. NastyBugs: A simple method for extracting antimicrobial resistance information from metagenomes [version 1; referees: 1 approved with reservations]. F1000Research 2017, 6:1971

(doi: 10.12688/f1000research.12781.1)

Antibiotic resistance (AMR) of bacterial pathogens is a growing public health threat around the world. Fast and reliable extraction of antimicrobial resistance genomic signatures from large raw sequencing datasets obtained from human metagenomes is a key task for bioinformatics.

NastyBugs is a versatile workflow for fast extracting of antimicrobial resistance genomic signatures from metagenomic sequencing data.

Objective: Create a reusable, reproducible, scalable, and interoperable workflow to locate antimicrobial resistant genomic signatures in SRA shotgun sequencing (including metagenomics) datasets.

This project was part of the Summer 2017 NCBI Hackathon.

Dependencies💻

Software:

Magic-BLAST 1.3 is a tool for mapping large next-generation RNA or DNA sequencing runs against a whole genome or transcriptome.

SAMtools 1.3.1 is a suite of programs for interacting with high-throughput sequencing data.

FASTX-Toolkit is a collection of command line tools for Short-Reads FASTA/FASTQ files preprocessing.

Docker is the leading software container platform.

DBs used for BLAST databases:

NCBI GRCh37/UCSC hg19 human reference genome

CARD (Comprehensive Antibiotic Resistance Database)

RefSeq Reference Bacterial Genomes

NastyBugs Workflow

My image

Workflow method

The pipeline use three databases that should be downloaded with the script:

  1. GRCh37/hg19 human reference genome database used for alignment and filtering reads of human origin from metagenomics samples.
  2. CARD database used for search of genomic signatures in the subset of reads unaligned to human genome.
  3. RefSeq reference bacterial genomes database used for search and assigning of 16S RNA taxonomic labels the subset of reads unaligned to human genome.

Step 1. Mapping sample SRR to human genome using Magic-BLAST:

magicblast13 -sra SRRXXXXXXX -db ~/references/human -num_threads 12 -score 50 -penalty -3 -out ~/test_run/SRRXXXXXXX_human.sam

Step 2. Filtering reads mapped to human genome using SAMtools (Removal of host (human) genome from metagenomics data):

samtools fasta -f 4 SRRXXXXXXX_human.sam -1 SRRXXXXXXX_read1.fasta  -2 SRRXXXXXXX_read2.fasta -0 SRRXXXXXXX_read0.fasta
fastx_clipper [-i INFILE] [-o OUTFILE]

Step 3. Searching 16S RNA taxonomic labels in RefSeq reference bacterial genomes database to identify microbial species presented in metagenome using Magic-BLAST:

magicblast13 -infmt fasta -query ~/test_run/SRRXXXXXXX_read1.fasta -query_mate ~/test_run/SRRXXXXXXX_read2.fasta -num_threads 12 -score 50 -penalty -3 -out ~/test_run/SRRXXXXXXX_refseq.sam -db ~/references/REFSEQ

Step 4. Searching genes and SNPs from CARD database in metagenome using Magic-BLAST:

magicblast13 -infmt fasta -query ~/test_run/SRRXXXXXXX_read1.fasta -query_mate ~/test_run/SRRXXXXXXX_read2.fasta -num_threads 12 -score 50 -penalty -3 -out ~/test_run/SRRXXXXXXX_CARD_SNP.sam -db ~/references/CARD_variant
magicblast13 -infmt fasta -query SRRXXXXXXX_read1.fasta -query_mate SRRXXXXXXX_read2.fasta -num_threads 12 -score 50 -penalty -3 -out SRRXXXXXXX_CARD_gene.sam -db ~/references/CARD_gene

Step 5. Converting SAM to BAM format and sorting using SAMtools:

samtools view -bS SRRXXXXXXX_SNP.sam | samtools sort - -o SRRXXXXXXX_SNP.bam
samtools view -bS SRRXXXXXXX_CARD_gene.sam | samtools sort - -o SRRXXXXXXX_CARD_gene.bam

Step 6. Producing detailed output file(s) including names of detected bacterial species and resistance genes with statistical metrics in text and graphical formats.

Deliverables

Documented workflow with containerized tools in Docker.

How to use/run a Docker image

Installation

sudo docker images
sudo docker pull stevetsa/docker-magicblast
sudo docker run -it stevetsa/docker-magicblast
sudo docker ps -a 

Usage

main.sh <options> -S SRA -o output_directory

Input file format

SRA accession numbers (ERR or SRR) or FASTQ files

Output

  1. Table (in CSV or TAB-delimited format) with the next columns:
  • RefSeq accession number (Nucleotide)
  • Genus
  • Resistance gene
  • ARO (Antibiotic Resistance Ontology) accession number
  • Score (number of mapped reads per 1kb)
  1. Dot plot showing relative abundance of antimicrobial resistance/bacterial species in metagenomic sample.

  2. Pie chart vizualization of bacterial abundance in the given dataset using Krona (Ondov BD, Bergman NH, and Phillippy AM. Interactive metagenomic visualization in a Web browser. BMC Bioinformatics. 2011 Sep 30; 12(1):385).

My image

Validation

The NastyBugs workflow was validated using the next SRAs: ERR1600439 and SRR5239736.

Planned Features

  1. Code optimization.
  2. Improved more detailed output.
  3. Prediction of novel resistance genes (using HMM).

F.A.Q.

  1. How to cite?

Tsang H, Moss M, Fedewa G et al. NastyBugs: A simple method for extracting antimicrobial resistance information from metagenomes [version 1; referees: awaiting peer review]. F1000Research 2017, 6:1971 doi: 10.12688/f1000research.12781.1

  1. How to use?

Follow the instructions on this page.

  1. What if I need a help?

Feel free to contact authors if you need help.

Reference

Tsang H, Moss M, Fedewa G, Farag S, Quang D, Rakov AV, Busby B. NastyBugs: A simple method for extracting antimicrobial resistance information from metagenomes [version 1; referees: awaiting peer review]. F1000Research 2017, 6:1971 doi: 10.12688/f1000research.12781.1

People/Team