Skip to content

Latest commit

 

History

History
165 lines (130 loc) · 7.81 KB

README.md

File metadata and controls

165 lines (130 loc) · 7.81 KB
Raw

Nasty_Metagenomes

Antimicrobial Resistance Characterization in Metagenomes

Previous Hackathon Work

Goals:

  • Compare alignment of FASTQ files 1) MagicBlast 2) HMM-er and 3) MASH using reference AMR Finder database
  • Generate a "hits" file with aligned reads to each of the publically available AMR databases
  • Determine species and plasmid contextual information about AMR genes by creating a merged chromosome and plasmid database and BLAST-ting "hits"

Dependencies & Tools

AMR Database

Workflow Diagram

workflow

Workflow Steps

  • Input: SRA ID
  • Output files: AMR hits file, AMR by species, AMR's on plasmids
  1. Create Blast Databases
  2. Use SamTools
  3. Input SRA/FASTQ to MagicBlast or HHM-er
  4. SKESA guided assembly
  5. Species identification, plasmid identification & Alignment and Statistics

Step 1.

------------------

# Download [RefSeq Plasmid Database](https://www.ncbi.nlm.nih.gov/refseq/)
## Use [FTP](ftp://ftp.ncbi.nlm.nih.gov/refseq/release/plasmid/) to download plasmid database, and concatinate into one file

# Download [AMR Finder Database](https://www.ncbi.nlm.nih.gov/bioproject/PRJNA313047)
## Use webserver to download database [AMR_CDS](ftp://ftp.ncbi.nlm.nih.gov/pathogen/Antimicrobial_resistance/AMRFinder/data/2019-04-29.1/)

# Download [bacterial chromosome Databases](https://www.ncbi.nlm.nih.gov/assembly)
## Search assemblies all[sb]
## Download Assembly: Bacteria, Latest RefSeq, Assembly from Type
## Download Assembly: Bacteria, Latest RefSeq, Reference

# Merge Plasmid, Assembly from Type, Reference Databses
## Create non-redudant list of accession numbers

# Create Blast Databases
## AMR blast db
## Merged Plasmid, Assembly from Type, Reference
  
#**command line**
/opt/ncbi-blast-2.9.0+/bin/makeblastdb -in /data/DBs/Bacteria_type_rep_plasmid_cat_nr.fa -parse_seqids -input_type fasta -dbtype nucl -out /data/DBs/Bacteria_type_rep_plasmid_refseq_nr.blastdb -max_file_sz 4GB 
# Create Mash sketches:
## mash sketch -i AMR.fa
## mash sketch -i -p 12 Bacteria_rep.fna
## mash sketch -i -p 12 Bacteria_typ.fna

(Back to Workflow)

Step 2.

------------------

# Use Sam tools

#**command line**

(Back to Workflow)

Step 3.

------------------

# MagicBlast
# Use AMR finder database as reference
# Use SamTools to sort and create depth summary file
# Run Cov_dep_cal.pl for coverage depth and average sequence coverage
  
#**command line**
/opt/magicblast/ncbi-magicblast-1.4.0/bin/magicblast -sra ERR1600439 -db /data/AMR_CDS.blastdb -outfmt sam -out ERR1600439_v_AMR_CDS_magicblast_sam.out -num_threads 8 -paired -no_unaligned
  
# HHM-er
# Use AMR finder database as a reference; use a user-specified bitscore as a threshold
# to filter hmm hits.
# Translate each read into protein in all six possible reading frames
# Break translated reads into ORFs; discard any length 25 aa or less
# Use hmmsearch to run 562 HMM profiles against the translated reads
# For each hmm profile that scored a hit, extract a representative nucleotide sequence
# Output the representative nucleotide sequences for guided assembly
  
#**command line**
./hmm_pipeline.sh -q [fastq file path] -h [path to hmm_databases directory] -a 
[threshold bitscore]

# MASH
# Given a fasta file with AMR genes we build a MASH sketch and screen the reads against the sketch. 
# This produces kmer distances between the read set and each AMR, which then is used to extract only 
# the AMRs that are close the readset.

#**command line**
# Screen the reads against the AMR sequences with the minimum score 0.85:
## mash screen -p 12 -w AMR.fa.msh ERR1600439*.fastq | awk '$1>0.85' > ERR1600439.amr.screen
# produce a list of candidate AMRs:
## cut -f 5 ERR1600439.amr.screen

(Back to Workflow)

Step 4.

------------------

# SKESA Guided Assembly
# Guided assembly allows to assemble contigs based on some known sequences used as baits. The assembler stacks kmers and extends the ends of each guide sequence optionally output a list of variants assembled. 
# In our analysis we use AMR fasta sequences selected in previous steps as guides for assembly. The resutls are presented as contigs fasta file. 

#**command line**
# to assemble contigs with AMR_CDS_by_ERR1600439_ref.fasta as guides:
# guidedassembler --cores 8 --sra_run ERR1600439 --targets /data/ERR1600439/magicblast_output/AMR_CDS_by_ERR1600439_ref.fasta --contigs_out ERR1600439.ga.fa --fraction 0.1
# to assemble contigs and print out all variants of contigs:
# guidedassembler_graph --targets ../../AMR_CDS_norm.fasta --consensus ERR1600439.amr.contigs.fa --all_variants ERR1600439.amr.all-contigs.fa --gfa /dev/null --sra_run ERR1600439

(Back to Workflow)

Step 5A.

------------------

# Species and Plasmid Identification
# Blast AMR hits lists against combined database
# Parse for Species level and Plasmid identification

#**command line**
sudo /opt/ncbi-blast-2.9.0+/bin/blastn -query /data/ERR1600439/magicblast_output/ERR1600439.ga.fa -task blastn -db /data/DBs/Bacteria_type_rep_plasmid_refseq_nr.blastdb -outfmt 6 -evalue 1e-6 -out /data/ERR1600439/magicblast_output/ERR1600439.ga.fa_vs_Bacteria_RefSeq_nr_blastn.out -max_target_seqs 10 -num_threads 8

(Back to Workflow)

Step 5B.

------------------

# Alignment and stats
## build blast db for contigs
## align reads onto the contigs

#**command line**
# makeblastdb -parse_seqids -in ERR1600439.ga.fa -input_type fasta -dbtype nucl -out ERR1600439.ga.blastdb
# magicblast -db ERR1600439.ga.blastdb -query ERR1600439*.fastq  -infmt fastq | samtools view -Sb -F 4 - | samtools sort - > ERR1600439_amr_contigs.bam

(Back to Workflow)

Authors

  • Xin Huang
    • National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20851
  • Inês Mendes
    • Instituto de Microbiologia, Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal; University of Groningen, University Medical Center Groningen, Department of Medical Microbiology and Infection Prevention, Groningen, The Netherlands
  • Jonathan Parkinson
    • Qpex Biopharma, Inc., San Diego, CA 92121
  • Samantha Sevilla
    • Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, National Institutes of Health, Leidos Biomedical, Inc., Gaithersburg, MD 20877
  • Vadim Zalunin
    • National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA