Metagenomics

Overview & Terminology

• collected genomic information of a sample
• unbiased targeting (no special marker like metabarcoding)
• Metabarcoding (16S) is used for large number of samples, i.e., multiple patients, longitudinal studies, etc. but offers limited taxonomical and functional resolution in comparision
• Whole Metagenome sequencing (WMS), or shotgun metagenome sequencing, provides insight into community biodiversity and function in contrast to Metabarcoding, where only a specific region of the bacterial community (the 16s rRNA) is sequenced
• WMS aims at sequencing all the genomic material present in the environment
• WMS is more expensive but offers increased resolution, and allows the discovery of viruses as well as other mobile genetic elements

Microbiota organism within a set environment mainly focused on bacteria * exluding viruses & mobile elements

Microbiome includes all factors of an environment (organism, genetic elements, metabolites, proteins)

Holobiont assemblages of different species that form a ecological unit divided into: virome, microbiome and macrobiological hosts

Whole Metagenome Sequencing - practical

• comparing samples from the Pig Gut Microbiome against the Human Gut Microbiome

Software used: - FastQC - Kraken - R - Pavian

Data used:

curl -O -J -L https://osf.io/h9x6e/download
tar xvf subset_wms.tar.gz

• we ssh using additionally the -X flag to allow a graphical interface
• we used fastqc do check the quality of our data

1. Taxonomic read assignment with Kraken

• first we need the Kraken database

#create a databases directory and download the database
wget https://ccb.jhu.edu/software/kraken/dl/minikraken_20171019_4GB.tgz
tar xzf minikraken_20171019_4GB.tgz

• running kraken on the reads
• produces a tab-delimited file with an assigned TaxID for each read

# for the script you need to declare the KRAKEN_DB path
KRAKEN_DB="/mnt/databases/minikraken_20171013_4GB"
for i in *_1.fastq
do
    prefix=$(basename $i _1.fastq)
    # print which sample is being processed
    echo $prefix
    kraken --db $KRAKEN_DB --threads 2 --fastq-input ${prefix}_1_corr.fastq ${prefix}_2_corr.fastq > /home/student/wms/results/${prefix}.tab
    kraken-report --db $KRAKEN_DB /home/student/wms/results/${prefix}.tab > /home/student/wms/results/${prefix}_tax.txt
done

2. Visualization with Pavian in R

• Installing and run Pavian in R with those commands:

#one time only installations and setup
options(repos = c(CRAN = "http://cran.rstudio.com"))
if (!require(remotes)) { install.packages("remotes") }
remotes::install_github("fbreitwieser/pavian")
#Start up command
pavian::runApp(port=5000)

• creates a new R-tab were you can open the kraken files
• creates a nice sankey chart of your organism within your sample
• its scalable, allows comparision between samples (as a table) and indepth analysis of samples (see figure)

Example results in one sample as a sankey chart using pavian

• KRONA chart is also for visualization of metagenomic data
• good for explanation of your sample
• but PAVIAN is way better

Metagenome assembly and binning - practical

AIM: inspect and assemble metagenomic data and retrieve draft genomes

• using a mock community of 20 bacteria simulating Illumina HiSeq created with InSilicoSeq
• selected from the Tara Ocean study that recovered 957 distinct Metagenome-assembled-genomes (or MAGs) that were previsouly unknown

1. Correction and assembly of the illumina reads

• check the fastq data with fastqc
• do sickle and scythe as needed; see HTS
• assembly here with megahit

sickle pe -f tara_reads_R1.fastq.gz -r tara_reads_R2.fastq.gz -t sanger -o tara_trimmed_R1.fastq -p tara_trimmed_R2.fastq -s /dev/null
megahit -1 tara_trimmed_R1.fastq -2 tara_trimmed_R2.fastq -o tara_assembly

2. Binning

• First map the reads back against the assembly (for coverage information)
• bowtie2 is a ultra fast short read mapper/aligner

ln -s tara_assembly/final.contigs.fa . #link contigs to current folder
bowtie2-build final.contigs.fa final.contigs #index
bowtie2 -x final.contigs -1 tara_trimmed_R1.fastq -2 tara_trimmed_R2.fastq | samtools view -bS -o tara_to_sort.bam #alignment and bam conversion
samtools sort tara_to_sort.bam -o tara.bam
samtools index tara.bam

• then we run metabat

runMetaBat.sh -m 1500 final.contigs.fa tara.bam
mv final.contigs.fa.metabat-bins1500 metabat

3. QC of the bins

• first time you run checkm you have to create the database

sudo checkm data setRoot ~/.local/data/checkm

checkm lineage_wf -x fa metabat checkm/
checkm bin_qa_plot -x fa checkm metabat plots

4. Get organism information and plot it

#tells you which organism, saves under checkm/lineage needs folder checkm
checkm qa checkm/lineage.ms checkm
#plots its
checkm bin_qa_plot -x fa checkm metabat plots

• you get something like this, which should represent 10 different species (theoretically):
• now you can annotate each bin (fasta files are in metabat dir)
• more informations can be found here and here

5. Barrnap

• BAsic Rapid Ribosomal RNA Predictor
• isnt tsuitable for novel stuff since it has to be in the database, but seems still to work (see figure above of unknown marine bacteria)
• you can download it here