Pig-Gut-Genomes

Genome collection of isolates and MAGs from the pig gut

Description

This collection include genomes (isolates and MAGs) from following studies:

PIBAC: Isolate and MAG collection: Wylensek, 2020, "A collection of bacterial isolates from the pig intestine reveals functional and taxonomic diversity." Nature communications https://doi.org/10.1038/s41467-020-19929-w https://github.com/tillrobin/PIBAC https://www.dsmz.de/pibac

Chen, 2021, "Expanded catalog of microbial genes and metagenome-assembled genomes from the pig gut microbiome." Nature communications https://doi.org/10.1038/s41467-021-21295-0

Holman, 2022, "Novel insights into the pig gut microbiome using metagenome-assembled genomes" bioRxiv https://doi.org/10.1101/2022.05.19.492759

Pipelines for creating the collection

Collection

Download all genomes and create a checkM-data.txt including all CheckM information of all genomes you want to cluster:

genome,completeness,contamination
PIG0001.fa,97.07,1.27
PIG0002.fa,58.9,0.67
PIG0003.fa,79.84,0.23
[...]

Dereplication

Run dRep via bioconda using all mMAGs (comp >= 50, con < 10) and cluster to 95% ANI

# create dRep environment
conda create -n dRep dRep
# run for mMAGs
conda activate dRep
dRep dereplicate drep-outout \
--genomeInfo checkM-data.txt \
-comp 50 -con 10 -nc 0.3 \
-sa 0.95 -p 32 \
-g FileListPath.txt

This will resulting in 3,532 clusters, see contributions of the studies in the venn plot:

Taxonomic classification for final genome set

Run gtdbtk for all dereplicated genomes

# create gtdbtk environment
conda create -n gtdbtk gtdbtk
# run for dereplicated_genomes
conda activate gtdbtk
gtdbtk classify_wf \
--genome_dir dereplicated_genomes \
--out_dir gtdbtk-dereplicated_genomes \
--cpus 64 -x fa 

The results you can find in the summary files: Classifications archaeal genomes Classifications bacterial genomes

Here a visualisation of the top families:

Pipelines for using the collection

Genome based taxnomic abundance profilling to representative genomes

1. We recommend the use of Bioconda eg create bioconda environment with bwa2 and bbmap with:

conda create -n PIGrun bwa2 bbmap
conda activate PIGrun

2. Download bwa2-index (Warning 22GB but you can use option 2b as an alternative)

download Pig_genomes_dRep9095-mMAGs-dereplicated_genomes_v01.tar.gz

wget -O "Pig_genomes_dRep9095-mMAGs-dereplicated_genomes_v01.tar.gz" "https://onedrive.live.com/download?cid=36ADEB4B3D109F6F&resid=36ADEB4B3D109F6F%21114785&authkey=AMy3k92ykHzmXwk"
tar xzf Pig_genomes_dRep9095-mMAGs-dereplicated_genomes_v01.tar.gz

2b. Download mMAG-fasta and run bwa2-index (2GB, will take 2-3 hours to process)

download Pig_genomes_dRep9095-mMAGs-dereplicated_genomes_v01.fasta.gz

wget -O "Pig_genomes_dRep9095-mMAGs-dereplicated_genomes_v01.fasta.gz" "https://onedrive.live.com/download?cid=36ADEB4B3D109F6F&resid=36ADEB4B3D109F6F%21114785&authkey=AMy3k92ykHzmXwk"
gzip -d Pig_genomes_dRep9095-mMAGs-dereplicated_genomes_v01.fasta.gz
bwa-mem2 index Pig_genomes_dRep9095-mMAGs-dereplicated_genomes_v01.fasta

3. Map the samples with bwa2 to the dereplicated genomes

Cores=24                      # please check your server
RefFasta=/path/to/index/      # bwa2 index
FastqPathR1=/path/to/file/R1  # set path to read 1
FastqPathR2=/path/to/file/R2  # set path to read 2
SampleName=MySampleName       # create name for the samples

4. Mapping to sam-file and sumup via pipeup from bbmap-tools (create sam-file I/O-weighted)

bwa-mem2 mem -t ${Cores} ${RefFasta} ${FastqPathR1} ${FastqPathR2} | pigz --fast > /tmp/${SampleName}.sam.gz
pileup.sh in=/tmp/${SampleName}.sam.gz covstats=${SampleName}.covstats 32bit=t 2> ${SampleName}.log
rm /tmp/${SampleName}.sam.gz

5. Convert covstats to TPM

• filter out genomes with less than 20% genomic coverage
• normalize count data to genome size and relative to 1 million reads
• download bash script [make-TPM-cov20-biom.sh] to process your covstats files

this script will grep all *.covstats files in the working folder and create TPM and biom-files

bash make-TPM-cov20-biom.sh

6. Use otu-table/biom-file for analyse the data

• create mapping file containing matching "#SampleID" with a group assignment
• Local: Import biom-file and metadata mapping file into R phyloseq https://github.com/joey711/phyloseq
• Online: Analyse with Microbiomeanalyst
  • Import biom-file and metadata mapping file via Marker Data Profiling https://www.microbiomeanalyst.ca/
  • Data Upload -> biom-format
    • use TPM or raw-count table in biom format
  • Data filtering
    • please note that the TPM version already include normalication to genomesize and a filtering of low covarge genomes