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Assembling reads into contigs and mapping reads to the assembled contigs are the primary steps in the assembly-based metagenomics. This tutorial walks you through the various steps involved in the process of genome assembly and mapping.
Prior to the genome assembly, reads are cleaned using a custom ARS-RQC pipeline, which utilizes a suite of functions from BBtools to remove host genome contamination and trim the standard Illumina barcodes. JSON files obtained from ARS-RQC can be exported as csv file and read in R to create a summary file.
conda create -n Ametagenomics python=3.6
source activate Ametagenomics
conda install pandas
conda install numpy
conda install -c conda-forge mkl_random
conda install -c anaconda cython y
conda install -c bioconda bbmap
1-2. Get ars-rqc from the repository and install the editable version. This allows to auto-update the changes made in the repository – so that the used program will be the most recent one.
git clone https://github.com/USDA-ARS-GBRU/ARS-RQC.git
cd ARS-RQC
pip install --editable .
mkdir data
cd data
ln -vs /project/**/**/*.fastq.gz .
###-4. Run 01_arsqc_interleave.sh to create interleaved fastq
#!/bin/bash
#SBATCH --job-name=rqc_interleave_Ametagenomics
#SBATCH --output=rqc_Ametagenomics_%A_%a.out
#SBATCH --error=rqc_Ametagenomics_%A_%a.err
#SBATCH --time=01:00:00
#SBATCH --array=1-156
#SBATCH -p short
#SBATCH -N 1
#SBATCH -n 10
module load miniconda
source activate Ametagenomics
module load pigz
module load bbtools/gcc/64/36.86
RUN=${SLURM_ARRAY_TASK_ID}
echo "My Slurm RUN_ID: '${RUN}'"
echo "My TMPDIR IS: " $TMPDIR
infile1=$(ls data/*_R1.fastq.gz | sed -n ${RUN}p)
echo "$infile1"
infile2=$(echo $infile1 | sed 's/R1/R2/')
echo "$infile2"
outbase=$(basename $infile1 _R1.fastq.gz)
outfile=data/${outbase}_interleave.fastq.gz
echo "$outfile"
reformat.sh in=$infile1 in2=$infile2 out=$outfile
#!/bin/bash #SBATCH --job-name=rqc_filter_Ametagenomics #SBATCH --output=rqc_Ametagenomics_%A_%a.out #SBATCH --error=rqc_Ametagenomics_%A_%a.err #SBATCH --time=01:00:00 #SBATCH --array=1-156 #SBATCH -p short #SBATCH -N 1 #SBATCH -n 10
module load miniconda source activate Ametagenomics module load pigz
RUN=${SLURM_ARRAY_TASK_ID} echo "My Slurm RUN_ID: '${RUN}'" echo "My TMPDIR IS: " $TMPDIR
rootdir="/project/" echo "$rootdir"
itl_file=$(ls data/*_interleave.fastq.gz | sed -n ${RUN}p) echo "$itl_file" time rqcfilter.py --fastq $itl_file --output "$rootdir"rqc_data -p
6. Run 03_parse_json.py – crate a summary data-frame using json files obtained from qc. This file is then analyzed in R to create qc-report using R markdown.
import json import pandas as pd import os
wdir = os.getcwd()
list = [] for lfile in os.listdir("rqc_data"): file = os.path.join(wdir, "rqc_data", lfile) if file.endswith('.json'): bfile = file with open(file) as js: js_dict = json.load(js) tr = js_dict['scaffoldStats1.txt']['desc']['TotalReads'] tb = js_dict['scaffoldStats1.txt']['desc']['TotalBases'] contam = js_dict['scaffoldStats1.txt']['desc']['ReadsMatched'] pctcontam = js_dict['scaffoldStats1.txt']['desc']['PctReadsMatched'] list.append((os.path.basename(bfile), tr, tb, contam, pctcontam))
cols = ['SampleID', 'TotalReads', 'TotalBases', 'Contaminants', "Percent_Contaminants"] result = pd.DataFrame(list, columns=cols) result.to_csv("rqc_data/parse_json.csv")
# Step 2: De Novo Assembly
At this point, we have clean reads that are ready for assembling to contigs. Given we have a large number of fastq files (here 156), lets first create a list of file names and separate each file name with a comma, then pass to megahit.
7. Create a list of files, open in text editor, then find carry-over and replace with comma, then delete comma at the EOF. This information will be pass as input files for genome assembly in megahit.
find /project/rqc_data/reads/** -type f > file_list_withpath.txt
#!/bin/bash #SBATCH --job-name=megahit_assembly #SBATCH --output=megahit_assembly_%A_%a.out #SBATCH --error=megahit_assembly_%A_%a.err #SBATCH --time=96:00:00 #SBATCH -p mem #SBATCH -N 1 #SBATCH -n 120
export PATH=$PATH:/home/ravin.poudel/bbmap export PATH=$PATH:/home/ravin.poudel/megahit/1.1.1 module load miniconda #source activate Ametagenomics module load pigz module load megahit
echo "My TMPDIR IS: " $TMPDIR
time megahit --k-min 27 --k-max 127 --k-step 10 -m 0.98 -t 120 --out-dir megahit_output --kmin-1pass --min-contig-len 300 --tmp-dir $TMPDIR --12 /project/rqc_data/reads/A1_run1_lane1_interleave.rqc.fq.gz,/project/rqc_data/reads/A1_run2_lane1_interleave.rqc.fq.gz,/project/rqc_data/reads/A1_run2_lane2_interleave.rqc.fq.gz,/project/rqc_data/reads/A1_run2_lane3_interleave.rqc.fq.gz,/project/rqc_data/reads/A1_run2_lane4_interleave.rqc.fq.gz,/project/rqc_data/reads/A1_run3_lane1_interleave.rqc.fq.gz,/project/rqc_data/reads/A1_run3_lane2_interleave.rqc.fq.gz,/project/rqc_data/reads/A1_run3_lane3_interleave.rqc.fq.gz,/project/rqc_data/reads/A1_run3_lane4_interleave.rqc.fq.gz,/project/rqc_data/reads/A1_run4_lane1_interleave.rqc.fq.gz,/project/rqc_data/reads/A1_run4_lane2_interleave.rqc.fq.gz,/project/rqc_data/reads/A1_run4_lane3_interleave.rqc.fq.gz,/project/rqc_data/reads/A1_run4_lane4_interleave.rqc.fq.gz,/project/rqc_data/reads/A2_run1_lane1_interleave.rqc.fq.gz,/project/rqc_data/reads/A2_run2_lane1_interleave.rqc.fq.gz,/project/rqc_data/reads/A2_run2_lane2_interleave.rqc.fq.gz,/project/rqc_data/reads/A2_run2_lane3_interleave.rqc.fq.gz,/project/rqc_data/reads/A2_run2_lane4_interleave.rqc.fq.gz,/project/rqc_data/reads/A2_run3_lane1_interleave.rqc.fq.gz,/project/rqc_data/reads/A2_run3_lane2_interleave.rqc.fq.gz,/project/rqc_data/reads/A2_run3_lane3_interleave.rqc.fq.gz,/project/rqc_data/reads/A2_run3_lane4_interleave.rqc.fq.gz,/project/rqc_data/reads/A2_run4_lane1_interleave.rqc.fq.gz,/project/rqc_data/reads/A2_run4_lane2_interleave.rqc.fq.gz,/project/rqc_data/reads/A2_run4_lane3_interleave.rqc.fq.gz,/project/rqc_data/reads/A2_run4_lane4_interleave.rqc.fq.gz,/project/rqc_data/reads/A3_run1_lane1_interleave.rqc.fq.gz,/project/rqc_data/reads/A3_run2_lane1_interleave.rqc.fq.gz,/project/rqc_data/reads/A3_run2_lane2_interleave.rqc.fq.gz,/project/rqc_data/reads/A3_run2_lane3_interleave.rqc.fq.gz,/project/rqc_data/reads/A3_run2_lane4_interleave.rqc.fq.gz,/project/rqc_data/reads/A3_run3_lane1_interleave.rqc.fq.gz,/project/rqc_data/reads/A3_run3_lane2_interleave.rqc.fq.gz,/project/rqc_data/reads/A3_run3_lane3_interleave.rqc.fq.gz,/project/rqc_data/reads/A3_run3_lane4_interleave.rqc.fq.gz,/project/rqc_data/reads/A3_run4_lane1_interleave.rqc.fq.gz,/project/rqc_data/reads/A3_run4_lane2_interleave.rqc.fq.gz,/project/rqc_data/reads/A3_run4_lane3_interleave.rqc.fq.gz,/project/rqc_data/reads/A3_run4_lane4_interleave.rqc.fq.gz,/project/rqc_data/reads/C1W_run1_lane1_interleave.rqc.fq.gz,/project/rqc_data/reads/C1W_run2_lane1_interleave.rqc.fq.gz,/project/rqc_data/reads/C1W_run2_lane2_interleave.rqc.fq.gz,/project/rqc_data/reads/C1W_run2_lane3_interleave.rqc.fq.gz,/project/rqc_data/reads/C1W_run2_lane4_interleave.rqc.fq.gz,/project/rqc_data/reads/C1W_run3_lane1_interleave.rqc.fq.gz,/project/rqc_data/reads/C1W_run3_lane2_interleave.rqc.fq.gz,/project/rqc_data/reads/C1W_run3_lane3_interleave.rqc.fq.gz,/project/rqc_data/reads/C1W_run3_lane4_interleave.rqc.fq.gz,/project/rqc_data/reads/C1W_run4_lane1_interleave.rqc.fq.gz,/project/rqc_data/reads/C1W_run4_lane2_interleave.rqc.fq.gz,/project/rqc_data/reads/C1W_run4_lane3_interleave.rqc.fq.gz,/project/rqc_data/reads/C1W_run4_lane4_interleave.rqc.fq.gz,/project/rqc_data/reads/C2W_run1_lane1_interleave.rqc.fq.gz,/project/rqc_data/reads/C2W_run2_lane1_interleave.rqc.fq.gz,/project/rqc_data/reads/C2W_run2_lane2_interleave.rqc.fq.gz,/project/rqc_data/reads/C2W_run2_lane3_interleave.rqc.fq.gz,/project/rqc_data/reads/C2W_run2_lane4_interleave.rqc.fq.gz,/project/rqc_data/reads/C2W_run3_lane1_interleave.rqc.fq.gz,/project/rqc_data/reads/C2W_run3_lane2_interleave.rqc.fq.gz,/project/rqc_data/reads/C2W_run3_lane3_interleave.rqc.fq.gz,/project/rqc_data/reads/C2W_run3_lane4_interleave.rqc.fq.gz,/project/rqc_data/reads/C2W_run4_lane1_interleave.rqc.fq.gz,/project/rqc_data/reads/C2W_run4_lane2_interleave.rqc.fq.gz,/project/rqc_data/reads/C2W_run4_lane3_interleave.rqc.fq.gz,/project/rqc_data/reads/C2W_run4_lane4_interleave.rqc.fq.gz,/project/rqc_data/reads/C3W_run1_lane1_interleave.rqc.fq.gz,/project/rqc_data/reads/C3W_run2_lane1_interleave.rqc.fq.gz,/project/rqc_data/reads/C3W_run2_lane2_interleave.rqc.fq.gz,/project/rqc_data/reads/C3W_run2_lane3_interleave.rqc.fq.gz,/project/rqc_data/reads/C3W_run2_lane4_interleave.rqc.fq.gz,/project/rqc_data/reads/C3W_run3_lane1_interleave.rqc.fq.gz,/project/rqc_data/reads/C3W_run3_lane2_interleave.rqc.fq.gz,/project/rqc_data/reads/C3W_run3_lane3_interleave.rqc.fq.gz,/project/rqc_data/reads/C3W_run3_lane4_interleave.rqc.fq.gz,/project/rqc_data/reads/C3W_run4_lane1_interleave.rqc.fq.gz,/project/rqc_data/reads/C3W_run4_lane2_interleave.rqc.fq.gz,/project/rqc_data/reads/C3W_run4_lane3_interleave.rqc.fq.gz,/project/rqc_data/reads/C3W_run4_lane4_interleave.rqc.fq.gz,/project/rqc_data/reads/M1_run1_lane1_interleave.rqc.fq.gz,/project/rqc_data/reads/M1_run2_lane1_interleave.rqc.fq.gz,/project/rqc_data/reads/M1_run2_lane2_interleave.rqc.fq.gz,/project/rqc_data/reads/M1_run2_lane3_interleave.rqc.fq.gz,/project/rqc_data/reads/M1_run2_lane4_interleave.rqc.fq.gz,/project/rqc_data/reads/M1_run3_lane1_interleave.rqc.fq.gz,/project/rqc_data/reads/M1_run3_lane2_interleave.rqc.fq.gz,/project/rqc_data/reads/M1_run3_lane3_interleave.rqc.fq.gz,/project/rqc_data/reads/M1_run3_lane4_interleave.rqc.fq.gz,/project/rqc_data/reads/M1_run4_lane1_interleave.rqc.fq.gz,/project/rqc_data/reads/M1_run4_lane2_interleave.rqc.fq.gz,/project/rqc_data/reads/M1_run4_lane3_interleave.rqc.fq.gz,/project/rqc_data/reads/M1_run4_lane4_interleave.rqc.fq.gz,/project/rqc_data/reads/M2_run1_lane1_interleave.rqc.fq.gz,/project/rqc_data/reads/M2_run2_lane1_interleave.rqc.fq.gz,/project/rqc_data/reads/M2_run2_lane2_interleave.rqc.fq.gz,/project/rqc_data/reads/M2_run2_lane3_interleave.rqc.fq.gz,/project/rqc_data/reads/M2_run2_lane4_interleave.rqc.fq.gz,/project/rqc_data/reads/M2_run3_lane1_interleave.rqc.fq.gz,/project/rqc_data/reads/M2_run3_lane2_interleave.rqc.fq.gz,/project/rqc_data/reads/M2_run3_lane3_interleave.rqc.fq.gz,/project/rqc_data/reads/M2_run3_lane4_interleave.rqc.fq.gz,/project/rqc_data/reads/M2_run4_lane1_interleave.rqc.fq.gz,/project/rqc_data/reads/M2_run4_lane2_interleave.rqc.fq.gz,/project/rqc_data/reads/M2_run4_lane3_interleave.rqc.fq.gz,/project/rqc_data/reads/M2_run4_lane4_interleave.rqc.fq.gz,/project/rqc_data/reads/M3_run1_lane1_interleave.rqc.fq.gz,/project/rqc_data/reads/M3_run2_lane1_interleave.rqc.fq.gz,/project/rqc_data/reads/M3_run2_lane2_interleave.rqc.fq.gz,/project/rqc_data/reads/M3_run2_lane3_interleave.rqc.fq.gz,/project/rqc_data/reads/M3_run2_lane4_interleave.rqc.fq.gz,/project/rqc_data/reads/M3_run3_lane1_interleave.rqc.fq.gz,/project/rqc_data/reads/M3_run3_lane2_interleave.rqc.fq.gz,/project/rqc_data/reads/M3_run3_lane3_interleave.rqc.fq.gz,/project/rqc_data/reads/M3_run3_lane4_interleave.rqc.fq.gz,/project/rqc_data/reads/M3_run4_lane1_interleave.rqc.fq.gz,/project/rqc_data/reads/M3_run4_lane2_interleave.rqc.fq.gz,/project/rqc_data/reads/M3_run4_lane3_interleave.rqc.fq.gz,/project/rqc_data/reads/M3_run4_lane4_interleave.rqc.fq.gz,/project/rqc_data/reads/O1S_run1_lane1_interleave.rqc.fq.gz,/project/rqc_data/reads/O1S_run2_lane1_interleave.rqc.fq.gz,/project/rqc_data/reads/O1S_run2_lane2_interleave.rqc.fq.gz,/project/rqc_data/reads/O1S_run2_lane3_interleave.rqc.fq.gz,/project/rqc_data/reads/O1S_run2_lane4_interleave.rqc.fq.gz,/project/rqc_data/reads/O1S_run3_lane1_interleave.rqc.fq.gz,/project/rqc_data/reads/O1S_run3_lane2_interleave.rqc.fq.gz,/project/rqc_data/reads/O1S_run3_lane3_interleave.rqc.fq.gz,/project/rqc_data/reads/O1S_run3_lane4_interleave.rqc.fq.gz,/project/rqc_data/reads/O1S_run4_lane1_interleave.rqc.fq.gz,/project/rqc_data/reads/O1S_run4_lane2_interleave.rqc.fq.gz,/project/rqc_data/reads/O1S_run4_lane3_interleave.rqc.fq.gz,/project/rqc_data/reads/O1S_run4_lane4_interleave.rqc.fq.gz,/project/rqc_data/reads/O2S_run1_lane1_interleave.rqc.fq.gz,/project/rqc_data/reads/O2S_run2_lane1_interleave.rqc.fq.gz,/project/rqc_data/reads/O2S_run2_lane2_interleave.rqc.fq.gz,/project/rqc_data/reads/O2S_run2_lane3_interleave.rqc.fq.gz,/project/rqc_data/reads/O2S_run2_lane4_interleave.rqc.fq.gz,/project/rqc_data/reads/O2S_run3_lane1_interleave.rqc.fq.gz,/project/rqc_data/reads/O2S_run3_lane2_interleave.rqc.fq.gz,/project/rqc_data/reads/O2S_run3_lane3_interleave.rqc.fq.gz,/project/rqc_data/reads/O2S_run3_lane4_interleave.rqc.fq.gz,/project/rqc_data/reads/O2S_run4_lane1_interleave.rqc.fq.gz,/project/rqc_data/reads/O2S_run4_lane2_interleave.rqc.fq.gz,/project/rqc_data/reads/O2S_run4_lane3_interleave.rqc.fq.gz,/project/rqc_data/reads/O2S_run4_lane4_interleave.rqc.fq.gz,/project/rqc_data/reads/O3S_run1_lane1_interleave.rqc.fq.gz,/project/rqc_data/reads/O3S_run2_lane1_interleave.rqc.fq.gz,/project/rqc_data/reads/O3S_run2_lane2_interleave.rqc.fq.gz,/project/rqc_data/reads/O3S_run2_lane3_interleave.rqc.fq.gz,/project/rqc_data/reads/O3S_run2_lane4_interleave.rqc.fq.gz,/project/rqc_data/reads/O3S_run3_lane1_interleave.rqc.fq.gz,/project/rqc_data/reads/O3S_run3_lane2_interleave.rqc.fq.gz,/project/rqc_data/reads/O3S_run3_lane3_interleave.rqc.fq.gz,/project/rqc_data/reads/O3S_run3_lane4_interleave.rqc.fq.gz,/project/rqc_data/reads/O3S_run4_lane1_interleave.rqc.fq.gz,/project/rqc_data/reads/O3S_run4_lane2_interleave.rqc.fq.gz,/project/rqc_data/reads/O3S_run4_lane3_interleave.rqc.fq.gz,/project/rqc_data/reads/O3S_run4_lane4_interleave.rqc.fq.gz
# Step 3: Mapping
The output obtained after assembly is also a fasta file, assembled contig. This assembled contigs will be used to map the reads. Prior we mapped the reads, at this point we can concatenate/merge reads from technical replicates. In our case for each experimental unit, we have 13 fastq files.
#!/bin/bas #SBATCH --job-name=merge #SBATCH --output=merge_%A_%a.out #SBATCH --error=merge_%A_%a.err #SBATCH --time=01:00:00 #SBATCH -p short #SBATCH -N 1 #SBATCH -n 40
cat reads/A1*.fq.gz > merged_fastqByRelicates/A1_interleaved_merge.fq.gz cat reads/A2*.fq.gz > merged_fastqByRelicates/A2_interleaved_merge.fq.gz cat reads/A3*.fq.gz > merged_fastqByRelicates/A3_interleaved_merge.fq.gz cat reads/M1*.fq.gz > merged_fastqByRelicates/M1_interleaved_merge.fq.gz cat reads/M2*.fq.gz > merged_fastqByRelicates/M2_interleaved_merge.fq.gz cat reads/M3*.fq.gz > merged_fastqByRelicates/M3_interleaved_merge.fq.gz cat reads/O1S*.fq.gz > merged_fastqByRelicates/O1S_interleaved_merge.fq.gz cat reads/O2S*.fq.gz > merged_fastqByRelicates/O2S_interleaved_merge.fq.gz cat reads/O3S*.fq.gz > merged_fastqByRelicates/O3S_interleaved_merge.fq.gz cat reads/C1W*.fq.gz > merged_fastqByRelicates/C1W_interleaved_merge.fq.gz cat reads/C2W*.fq.gz > merged_fastqByRelicates/C2W_interleaved_merge.fq.gz
10. Also, prior to mapping the reads, the genome need to be indexed. This can be done using bowtie2-build function form bowtie2.
#!/bin/bash
#SBATCH --job-name=index_contig #SBATCH --output=index_contig_%A_%a.out #SBATCH --error=index_contig_%A_%a.err #SBATCH --time=04:00:00 #SBATCH -p short #SBATCH -N 1 #SBATCH -n 40
module load bowtie2/2.3.4 module load samtools
bowtie2-build megahit_output/final.contigs.fa megahit_output/contigs
#!/bin/bash
#SBATCH --job-name=bowtie_map #SBATCH --output=bowtie_map_%A_%a.out #SBATCH --error=bowtie_map_%A_%a.err #SBATCH --time=12:00:00 #SBATCH --array=1-12 #SBATCH -p short #SBATCH -N 1 #SBATCH -n 40
module load bowtie2/2.3.4 module load samtools
rootdir="/project/" echo "$rootdir"
RUN=${SLURM_ARRAY_TASK_ID} echo "My Slurm RUN_ID: '${RUN}'"
iarray=("$rootdir"rqc_data/merged_fastqByRelicates/*_interleaved_merge.fq.gz) echo "$iarray"
infile="${iarray[$SLURM_ARRAY_TASK_ID - 1]}" echo "$infile"
bname=basename $infile _interleaved_merge.fq.gz
echo "$bname"
time bowtie2 -p 40 -x megahit_output/contigs --interleaved $infile -S "$rootdir"mapping/"$bname".sam time samtools sort -o "$rootdir"mapping/"$bname".bam "$rootdir"mapping/"$bname".sam
# Step 4: Calling for bins or metagenomes.
At this point, we have obtained a large genomic fragment (genomic mess)representing microbial communities. We need to separate them into individual genomes/bins, for which we are using an automated software called `MetaBAT`. MetaBAT accurately bins genomes using probabilistic distances of genome abundance and tetranucleotide frequency. More on `MetaBAT` is available at their [repo](https://bitbucket.org/berkeleylab/metabat) and accompanied [paper](https://peerj.com/articles/1165/)
#!/bin/bash #SBATCH --job-name=metabat #SBATCH --output=anvi-metabat_%A_%a.out #SBATCH --error=anvi-metabat_%A_%a.err #SBATCH --time=48:00:00 #SBATCH -p mem #SBATCH -N 1 #SBATCH -n 40
module load miniconda source activate metabat
runMetaBat.sh -m 5000 /project/megahit_output/final.contigs.fa /project/mapping/*.bam
# Step 5: Check the quality of Metagenome-assembled genomes (MAGs).
#!/bin/bash #SBATCH --job-name=CHECKM #SBATCH --output=anvi-metabat_%A_%a.out #SBATCH --error=anvi-metabat_%A_%a.err #SBATCH --time=48:00:00 #SBATCH -p mem #SBATCH -N 1 #SBATCH -n 40
module load miniconda #source activate metabat module load checkm
checkm lineage_wf -t 40 -x fa final.contigs.fa.metabat-bins5000/ /projectq/checkM
Filtering bins is not easy, as the criteria to define them is not hard and fast. We follow the guidelines for [MIMAG](https://www.nature.com/articles/nbt.3893) such that the bins with completeness > 95 % and contamination < 5% were selected for downstream analyses. [[https://github.com/ravinpoudel/metagenomics/blob/master/MIMAG.png|alt=octocat]]
# Step 6: Functional annotation of MAGs.
To retrieve the relevant genomic features from the MAGs, we use [prokka](https://github.com/tseemann/prokka)
#!/bin/bash #SBATCH --job-name=PORKA #SBATCH --output=porka_bybins_%A_%a.out #SBATCH --error=porka_bybins_%A_%a.err #SBATCH --time=04:00:00 #SBATCH --array=1-19 #SBATCH -p mem #SBATCH -N 1 #SBATCH -n 40
module load miniconda source activate megan
RUN=${SLURM_ARRAY_TASK_ID} echo "My Slurm RUN_ID: '${RUN}'" echo "My TMPDIR IS: " $TMPDIR
infile=$(ls filter_bins_metabat_checkM/*.fa | sed -n ${RUN}p) echo "$infile"
outbase=$(basename
prokka $infile --outdir $outdirect --prefix $outbase --addgenes --addmrna --centre X --compliant --genus --species --strain --kingdom --cpus 40
For each MAGs, prokka provides following files as ouput. These output can be explored for other downstream analyses such as metabolic pathways.
PROKKA_11302018.daa ## useful to explore using megan PROKKA_11302018.err PROKKA_11302018.faa PROKKA_11302018.ffn PROKKA_11302018.fna ## fasta file PROKKA_11302018.fsa PROKKA_11302018.gbk ## genebank file PROKKA_11302018.gff PROKKA_11302018.log PROKKA_11302018.sqn PROKKA_11302018.tbl PROKKA_11302018.tsv PROKKA_11302018.txt