DOI: 10.5281/zenodo.17092353
title: MIMIC2: Murine Intestinal Microbiota Integrated Catalog v2

description: Dataset overviewThe MIMIC2 dataset provides:a non-redundant high-quality catalog of 5.0 million genes6,967 Metagenome-Assembled Genomes (MAGs)1,252 Metagenomic Species Pangenomes (MSPs)This dataset can be used to analyze shotgun sequencing data of the murine gut microbiota.
Methods
Data sourcesThe MIMIC2 dataset was constructed using two different data sources:Source 1: the Mouse Gastrointestinal Bacterial Catalogue (MGBC) which is a compilation of 276 genomes from cultured isolates and 45,218 metagenome-assembled genomes (MAGs) from 1,960 publicly available mouse metagenomesSource 2: 68 samples of Messaoudene et al. (PRJNA783624) and 85 deeply sequenced samples from bioproject CNP0000619 published by Xiao et al.
Metagenomic assemblyDe novo metagenomic assembly was performed on the 153 samples from the data Source 2. First, sequencing adapters removal and read trimming was performed with fastp. Reads mapped on the host genome (GCF_000001635.27) with bowtie2 were removed with samtools. Finally, Metagenomic assembly was performed with metaSPAdes. Contigs of less than 1500 bp were removed.
MAGs recoveryReads of each sample from the data Source 2 were aligned to their respective assembly with bowtie2 and results were indexed in sorted bam files with samtools. Then, contigs coverage was computed in each sample with jgi_summarize_bam_contig_depths. MAGs were generated with MetaBAT 2 and MAGs quality was assessed with checkM. MAGs with completeness < 70% or contamination > 5% or N50 < 8Kb were discarded.
Non-redundant gene catalogGenes were predicted on all contigs from the data Source 2 with Prodigal (parameters : -m -p meta ). Likewise, genes were predicted on all genomes from the data Source 1 (MGBC) with Prodigal (parameters : -m -p single ). Genes from the two data sources were pooled and those shorter than 90 bp or incomplete were discarded. Finally, genes were clustered with cd-hit-est (parameters -c 0.95 -aS 0.90 -G 0 -d 0 -M 0 -T 0 ) by choosing those from the longest contigs as representatives.
MSPs recoverySamples from 19 cohorts (see below) were aligned against the non-redundant gene catalog with the Meteor software suite to produce a raw gene abundance table (5M genes quantified in 1374 samples). Then, co-abundant genes were binned in 1,252 Metagenomic Species Pan-genomes (MSPs, i.e. clusters of > 500 co-abundant genes that likely belong to the same microbial species) using MSPminer.The 19 cohorts used to recover the MSPs are:PRJNA783624CNP0000619PRJEB15095PRJEB22007PRJEB22710PRJEB31298PRJEB32790PRJEB32890PRJEB3374PRJEB36943PRJEB44286PRJEB7759PRJNA293255PRJNA390686PRJNA397886PRJNA515074PRJNA540893PRJNA549182PRJEB40719
MSPs taxonomic annotationRepresentative genomes of the MMGC collection were annotated with GTDB-Tk based on GTDB r202. Then, taxonomic annotation of MMGC genomes was propagated to the corresponding MSPs.For the MSPs without any corresponding MAG, taxonomic annotation was performed by alignment of all core and accessory genes against representative genomes of the GTDB database (release r202) using blastn (version 2.7.1, task = megablast, word_size = 16). A species-level assignment was given if > 50% of the genes matched the representative genome of a given species, with a mean nucleotide identity ≥ 95% and mean gene length coverage ≥ 90%. The remaining MSPs were assigned to a higher taxonomic level (genus to superkingdom), if more than 50% of their genes had the same annotation.
Construction of the phylogenetic tree39 universal phylogenetic markers genes were extracted from the 1,252 MSPs (or the corresponding MAGs if available) with fetchMGs. Then, the markers were separately aligned with MUSCLE. The 40 alignments were merged and trimmed with trimAl (parameters: -automated1). Finally, the phylogenetic tree was computed with FastTreeMP (parameters: -gamma -pseudo -spr -mlacc 3 -slownni).
Mapping rate distribution across public cohortsWe generated mapping rate distribution plots using Meteor2 (default parameters), comparing performance between: CNP0000619, PRJEB15095, PRJEB22007, PRJNA783624 (cohort used in catalogue assembly) and PRJNA760892 (independent cohort not used in assembly).