iMGMC - integrated Mouse Gut Metagenomic Catalog v2

- preview for alpha test (so far only for internal use, updates will follow)

Description

Creation of an updated mouse gut gene catalog

• MAGs representing n species
• more diverse samples from new studies (~n) and public studies (~n)
• integrated isolated species
• improved pipelines

Data

Metagenome-assembled genomes (MAGs) :

- The data is still protected at the moment, we will change this as soon as possible.

Description	Size	Link
representative mMAGs (n=#,###)	# GB	iMGMCv2-mMAGs-dereplicated_genomes.tar.gz
representative hqMAGs (n=#,###)	# GB	iMGMCv2-hqMAGs-dereplicated_genomes.tar.gz
all mMAGs (n=###,###)	## GB	iMGMCv2-mMAGs.tar.gz
Annotations by CheckM, dRep-Clustering, GTDB-Tk	# MB	MAG-annotation_CheckM_dRep_GTDB-Tk.tar.gz
Functional annotations (hqMAGs by eggNOG mapper v2)	### MB	hqMAGs.emapper.annotations.gz
preprocess mapping-index	## GB	hqMAGs.emapper.annotations.gz

Pipelines

Genome based abundance profilling

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

conda create -n iMGMCv2 bwa2 bbmap
conda activate iMGMCv2

2. Download or create bwa2 index form iMGMCv2-DU6-mMAGs.fasta

2a. Download bwa2-index (Warning 25,7GB but you can use option 2b as an alternative)

download [iMGMCv2-DU6.tar.gz](https://1drv.ms/f/s!Am-fED1L6602hcVH65QUhQZse5vOzA) 
tar -xzf iMGMCv2-DU6.tar.gz

2b. Download mMAG-fasta and run bwa2-index (700 MB, will take some hours to process)

download [iMGMCv2-DU6-mMAGs.fasta.gz](https://1drv.ms/f/s!Am-fED1L6602hcVH65QUhQZse5vOzA) 
gzip -d iMGMCv2-DU6-mMAGs.fasta.gz
bwa-mem2 index iMGMCv2-DU6-mMAGs.fasta

3. Map the samples with bwa2 to the iMGMCv2-DU6-mMAGs.fasta

Cores=24                      # please check your server
RefFasta=DU6.fasta            # 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

3a. 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

3b. Mapping and pipe direct to pipeup (need more memory ~100GB insteat of 60GB, about 10% faster)

bwa-mem2 mem -t ${Cores} ${RefFasta} ${FastqPathR1} ${FastqPathR2} | \
pileup.sh in=stdin.sam covstats=${SampleName}.covstats 32bit=t 2> ${SampleName}.log

4. Convert covstats to TPM (normalize count data to genome size and relative to 1 million reads)

bash TPM-Script ${SampleName}.covstats # create TPM-${SampleName}.txt
bash create-abundance-table.sh         # summarizing all Samples into one matrix file

5. [Optimal] join abundance-table with more samples

We are using a close reference profiling, so we can join other samples to the abundance-table. This make it easy to analyze your samples in a bigger context.

YourTable=abundance-table.txt              # table form step before
OtherTable=other-abundance-table.txt       # another table
SampleList=RefSamplesMircobiotas.txt       # File with names of samples you want to join (alternative all)

bash add-samples.sh ${YourTable} ${RunTable} ${SampleList} # add selected samples
bash add-samples.sh ${YourTable} ${RunTable}               # add all samples

6. Plot data

First create a mapping files with all needed samples and the metadata (like a simple group):

Sample Factor Trt1 Treatment Trt2 Treatment Trt3 Treatment Con1 Control Con2 Control Con3 Control

This data can used for downstream processing like ploting in R:

See our paper for details.

An integrated metagenome catalog enables new insights into the murine gut microbiome
Till R. Lesker, Abilash C. Durairaj, Eric. J.C. Gálvez, Ilias Lagkouvardos, John F. Baines, Thomas Clavel, Alexander Sczyrba, Alice C. McHardy, Till Strowig. Cell reports 30, no. 9 (2020): 2909-2922. https://doi.org/10.1016/j.celrep.2020.02.036