CAME 2024

01 Sep 2024

Reading time ~2 minutes

VirtualBox

The Virtual Machine is available from USB sticks, or for download here: https://disc-genomics.uibk.ac.at/data/CAME2024_v5.ova

The user is vmuser and the password is vmuser.

16S Ribosomal RNA Gene Amplicons

0. Download the data

wget 'https://disc-genomics.uibk.ac.at/data/CAME_SSU.tar.gz' -O - | tar -zx
cd VM_CAME

1. Download the LTP database

wget 'https://disc-genomics.uibk.ac.at/data/LTP_10_2024.arb'

Genome-Resolved Metagenomics

0. Download the data

wget 'https://disc-genomics.uibk.ac.at/data/came24.tar.gz' -O - | tar -zx
cd SLMU

You can start by running Nonpareil locally:

mkdir -p 00_nonpareil
nonpareil -T kmer -s SLMU.1.fastq.gz -f fastq -b 00_nonpareil/SLMU
NonpareilCurves.R --pdf 00_nonpareil/SLMU.pdf --no-observed 00_nonpareil/SLMU.npo
open 00_nonpareil/SLMU.pdf

1. Assembly

# IMPORTANT: This step might use significant RAM and take a very long time
spades.py --meta -1 SLMU.1.fastq.gz -2 SLMU.2.fastq.gz -o 01_assembly

If this is taking too long, you can kill the process by using Ctrl + C, and instead copy the scaffolds we provide:

rm -rf 01_assembly
mkdir -p 01_assembly
cp SLMU.scaffolds.fasta 01_assembly/scaffolds.fasta

2. Mapping reads to the assembly

mkdir -p 02_mapping
bowtie2-build 01_assembly/scaffolds.fasta 02_mapping/SLMU.idx
bowtie2 -1 SLMU.1.fastq.gz -2 SLMU.2.fastq.gz \
      -S 02_mapping/SLMU.sam -x 02_mapping/SLMU.idx --no-unal
samtools view -b 02_mapping/SLMU.sam | samtools sort -o 02_mapping/SLMU.bam -
ls 02_mapping

3. Binning

mkdir -p 03_binning
jgi_summarize_bam_contig_depths --outputDepth 03_binning/SLMU.abund \
      02_mapping/SLMU.bam
metabat2 -i 01_assembly/scaffolds.fasta -a 03_binning/SLMU.abund \
      -o 03_binning/SLMU_bin
ls 03_binning/SLMU_bin.*.fa

4. Comparing a MAG against a collection of close relatives

This step is optional, and it’s intended to demonstrate the capabilities of MiGA in the command line. Feel free to skip it.

miga new -P 04_classification/Sal -t genomes
miga gtdb_get -P 04_classification/Sal -T g__Salinibacter --ref -v
miga add -P 04_classification/Sal -i assembly -t popgenome \
      03_binning/SLMU_bin.*.fa
miga index_wf -o 04_classification/Sal -v

That last command will take some time, but you can continue to part 5 and revisit the results from MiGA later.

Once the run is complete, you can open the file:

04_classification/Sal/index.html

5. Mapping reads to the genome(s)

mkdir -p 05_genome_mapping
bowtie2-build 03_binning/SLMU_bin.5.fa 05_genome_mapping/SLMU_bin5.idx
bowtie2 -1 SLMU.1.fastq.gz -2 SLMU.2.fastq.gz \
      -S 05_genome_mapping/SLMU_bin5.sam \
      -x 05_genome_mapping/SLMU_bin5.idx --no-unal

6. Recruitment plots

mkdir -p 06_recplot
rpe build -d 06_recplot/SLMU_5.db -r 05_genome_mapping/SLMU_bin5.sam \
    -g 03_binning/SLMU_bin.5.fa --mag
rpe plot -d 06_recplot/SLMU_5.db
mv recruitment_plots 06_recplot/

Open the file manager and open the following file to explore some of the results locally:

06_recplot/recruitment_plots/SLMU_bin5_sam/SLMU_bin_recruitment_plot.html

Software Used

And lots of Software used by those above!