Assembly Based Metagenomics
Assembling reads into contigs and mapping reads to the assembled contigs are the primary steps in assembly-based metagenomics. This tutorial walks you through the various steps involved in the process of genome assembly and mapping.
Prior to 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.
1-1. Create a conda environment
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 .
1-3. Soft link the input files from collaborator’s raw-data directory.
mkdir data
cd data
ln -vs /project/**/**/*.fastq.gz .
1-4. 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
1-5. Run 02_arsqc_filer.sh to run qc on fastq.
#!/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
1-6. Run 03_parse_json.py – create 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")
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.
2-1. 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
2-2. Run genome assembly 04_megahit.sh
#!/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
Output obtained after the assembly is also a fasta file, assembled contig. This assembled contigs will be used for mapping the reads. Prior we mapped the reads, we can concatenate/merge reads from technical replicates. In our case for each experimental unit, we have 13 fastq files.
3-1. Run 05_merge_interleaved_fastq.sh to merge fastq by technical replicates.
#!/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
3-2. Also, prior to mapping reads, the genome needs to be indexed. This can be done using bowtie2-build function form bowtie2. Run 06_index_genome.sh.
#!/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
3-3. Run 07_mapping.sh to map reads to assembled genome using bowtie2.
#!/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/"$bname".sam
At this point, we have obtained a large genomic fragment (genomic mess) representing microbial communities. We need to separate them into individual genomes/bins, for which we are using automated software called MetaBAT. MetaBAT accurately bins genomes using probabilistic distances of genome abundance and tetranucleotide frequency. More on MetaBAT is available at their repo and accompanied paper.
4-1. Run 08_metabat.sh
#!/bin/bash
#SBATCH --job-name=metabat
#SBATCH --output=anvi-metabat_%A_%a.out
#SBATCH --error=anvi-metabat_%A_%a.err
#SBATCH --time=04:00:00
#SBATCH -p short
#SBATCH -N 1
#SBATCH -n 40
module load miniconda
source activate metabat
runMetaBat.sh -m 5000 /project/megahit_output/final.contigs.fa /project/mapping/*.bam
5-1. Run 09_checkM.sh
#!/bin/bash
#SBATCH --job-name=CHECKM
#SBATCH --output=anvi-metabat_%A_%a.out
#SBATCH --error=anvi-metabat_%A_%a.err
#SBATCH --time=04:00:00
#SBATCH -p short
#SBATCH -N 1
#SBATCH -n 40
module load miniconda
module load checkm
checkm lineage_wf -t 40 -x fa final.contigs.fa.metabat-bins5000/ /projectq/checkM
Filtering bins is not an easy task, as criteria to define/filter bins is not hard and fast. We follow the guidelines for MIMAG such that the bins with completeness > 95 % and contamination < 5% were selected for downstream analyses. 
To retrieve the relevant genomic features from the MAGs, we use prokka
6-1. Run 10_porka_annotation.sh, which usages prokka
#!/bin/bash
#SBATCH --job-name=PORKA
#SBATCH --output=porka_bybins_%A_%a.out
#SBATCH --error=porka_bybins_%A_%a.err
#SBATCH --time=04:00:00
#SBATCH --array=1-19
#SBATCH -p short
#SBATCH -N 1
#SBATCH -n 40
module load miniconda
source activate megan
RUN=${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 $infile .fa)
outdirect=porka_bybins/${outbase}
echo "$outdirect"
prokka $infile --outdir $outdirect --prefix $outbase --addgenes --addmrna --centre X --compliant --genus --species --strain --kingdom --cpus 40
For each MAGs, prokka provides the following files as output. These outputs 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