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wasterwater_arg_metagenomics

antibiotic resistance genes (ARGs) discovery and comparison among metagenomics sequenced data pipeline for paper "Profiling the fate of antibiotic resistance across a WWTP using metagenomics and epicPCR for risk estimation"

Part1 Sample ARG Identification

Required libraries, databases and tools:

megahit https://github.com/voutcn/megahit 
rgi https://github.com/arpcard/rgi
card-databse https://card.mcmaster.ca/download 
mob-suite https://github.com/phac-nml/mob-suite
bowtie2 https://github.com/BenLangmead/bowtie2
blastn and blastx 
MGE-database https://bench.cs.vt.edu/ftp/data/databases/MGEs90.fasta 
centrifuge https://github.com/DaehwanKimLab/centrifuge 
PlasFlow https://github.com/smaegol/PlasFlow
seqkit https://github.com/shenwei356/seqkit
seqtk https://github.com/lh3/seqtk 
os, subprocess, collections, pathlib, numpy, pandas, BioPython

Usage

  1. Install all the tools, libraries and their related databases.
  2. Replace the second block in the jupyter notebook with custom path.
  3. Run the code, but need to switch environment for PlasFlow.

Part2 EpicPCR Analysis

Methods

Step1 Trim and filter out low qulity reads

a) Trim reads using porechop

porechop -t 15 -i [temp]_input.fastq --discard_middle -o trimmed_[temp].fastq

b) Remove reads with quality score below 7

NanoFilt -q 7 trimmed_[temp].fastq > nanofilt_[temp].fastq

Step2 Blast agaisnt linker primer sequences

blastn -db reference_[temp]_bridge -query [temp]_input_seq.fasta -outfmt 6 -num_threads 10 -out arg_mapping/[temp]_bridge_blast

Step3 Split fusion reads using a customized script

python split_read_blast.py

Step4 Taxonomic Classification

emu abundance [temp]_input.fasta --output-dir output_dir --output-basename [temp]_emu --threads 10 --db dbs