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"
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
- Install all the tools, libraries and their related databases.
- Replace the second block in the jupyter notebook with custom path.
- Run the code, but need to switch environment for PlasFlow.
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
blastn -db reference_[temp]_bridge -query [temp]_input_seq.fasta -outfmt 6 -num_threads 10 -out arg_mapping/[temp]_bridge_blast
python split_read_blast.py
emu abundance [temp]_input.fasta --output-dir output_dir --output-basename [temp]_emu --threads 10 --db dbs