cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.14181283
title: A catalog of genes, genomes and species of the cat (Felis catus) intestinal microbiota

description: Data sourcesThis dataset was constructed using two different bioprojects:PRJNA758898 from Ma et al. 2022. 16 samples from 16 animals.PRJEB9357 from Deusch et al. 2015. 88 samples from 30 animals.PRJEB4391 from Deusch et al. 2014. 36 samples from 18 animals.PRJNA944553. 30 samples from 30 animals.PRJNA908260 from Bai et al. 2023. 8 samples from 8 animals.PRJNA923753 from Ho et al. 2023. 1 sample.
Metagenomic assemblyDe novo metagenomic assembly was performed on samples listed above. First, sequencing adapters removal and read trimming was performed with fastp. Reads mapped on the host genome (GCF_018350175.1) with bowtie2 were removed with samtools. Finally, Metagenomic assembly was performed with metaSPAdes. Contigs of less than 1500 bp were removed.
MAGs recoveryMAGs were generated with COMEBin (multi-coverage mode) and MAGs quality was assessed with CheckM2. MAGs with completeness < 70% or contamination > 5% or N50 < 5Kb were discarded. Pairwise Average Nucleotide Identity (ANI) was computed for all recovered MAGs with fastANI and dereplication at species level (ANI cutoff = 95%).
Non-redundant gene catalogGenes were predicted on all contigs from metagenomic assemblies with Prodigal (parameters : -m -p meta). Genes were pooled and 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 multiple cohorts (listed above + PRJNA906124 from Lee et al. 2022) were aligned against the non-redundant gene catalog with the Meteor software suite to produce a raw gene abundance table (1,3M genes quantified in 212 samples). Then, co-abundant genes were binned in 344 Metagenomic Species Pan-genomes (MSPs, i.e. gene clusters that likely belong to the same microbial species) using MSPminer.
MAGs and MSPs taxonomic annotationDereplicated MAGs were annotated with GTDB-Tk based on GTDB r214. Then, MAGs taxonomic annotation was propagated to the corresponding MSPs.
Construction of the phylogenetic tree39 universal phylogenetic markers genes were extracted from the dereplicated MAGs 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).
DOI: 10.5281/zenodo.11490375
title: A catalog of genes, genomes and species of the cat (Felis catus) intestinal microbiota

description: Data sourcesThis dataset was constructed using two different bioprojects:PRJNA758898 from Ma et al. 2022. 16 samples from 16 animals.PRJEB9357 from Deusch et al. 2015. 88 samples from 30 animals.PRJEB4391 from Deusch et al. 2014. 36 samples from 18 animals.PRJNA944553. 30 samples from 30 animals.PRJNA908260 from Bai et al. 2023. 8 samples from 8 animals.PRJNA923753 from Ho et al. 2023. 1 sample.
Metagenomic assemblyDe novo metagenomic assembly was performed on samples listed above. First, sequencing adapters removal and read trimming was performed with fastp. Reads mapped on the host genome (GCF_018350175.1) with bowtie2 were removed with samtools. Finally, Metagenomic assembly was performed with metaSPAdes. Contigs of less than 1500 bp were removed.
MAGs recoveryMAGs were generated with COMEBin (multi-coverage mode) and MAGs quality was assessed with CheckM2. MAGs with completeness < 70% or contamination > 5% or N50 < 5Kb were discarded. Pairwise Average Nucleotide Identity (ANI) was computed for all recovered MAGs with fastANI and dereplication at species level (ANI cutoff = 95%).
Non-redundant gene catalogGenes were predicted on all contigs from metagenomic assemblies with Prodigal (parameters : -m -p meta). Genes were pooled and 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 multiple cohorts (listed above + PRJNA906124 from Lee et al. 2022) were aligned against the non-redundant gene catalog with the Meteor software suite to produce a raw gene abundance table (1,3M genes quantified in 212 samples). Then, co-abundant genes were binned in 344 Metagenomic Species Pan-genomes (MSPs, i.e. gene clusters that likely belong to the same microbial species) using MSPminer.
MAGs and MSPs taxonomic annotationDereplicated MAGs were annotated with GTDB-Tk based on GTDB r214. Then, MAGs taxonomic annotation was propagated to the corresponding MSPs.
Construction of the phylogenetic tree39 universal phylogenetic markers genes were extracted from the dereplicated MAGs 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).
DOI: 10.5281/zenodo.10679309
title: A catalog of genes and species of the human skin microbiota

description:  

Dataset overview

This dataset provides:a non-redundant high-quality catalog of 2.9 million genes392 Metagenomic Species Pangenomes (MSPs)This dataset can be used to analyze shotgun sequencing data of the human skin microbiota.

How to use this dataset

Create a gene abundance table by aligning reads from each sample against the catalog. For this purpose, you can use Meteor or NGLess. Then, normalize raw counts by gene length.Taxonomic profiling: the abundance of each species can be estimated as the average abundance of its 100 first core genes. To reduce the false positive rate, only consider that a species is present if at least 10/100 marker genes are detected.

Methods

 

Data sources

This dataset was built using the following data sources:118 isolate-derived genomes from the HMRGD246 isolate-derived genomes from the Skin Microbial Genome Collection (SMGC)1,407 skin metagenome assemblies from the Skin Microbial Genome Collection (SMGC)

Non-redundant gene catalog

After filtering out short contigs (<1500 bp), genes were predicted with Prodigal on genomes (mode: single) and metagenome assemblies (mode: meta). Complete genes (partial=00) were pooled and 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.

Functional annotation

KOs assignments were obtained with KofamScan using the KEGG 107 database.

MSPs recovery

Reads from the 1,120 skin metagenomes available in the bioproject PRJNA46333 were aligned against the non-redundant gene catalog with the Meteor software suite to produce a raw gene abundance table (2.9M genes quantified in 1,120samples). Then, co-abundant genes were binned in 392 Metagenomic Species Pan-genomes (MSPs, i.e. clusters of co-abundant genes that likely belong to the same microbial species) using MSPminer.

MSPs taxonomic annotation

Taxonomic annotation was performed by alignment of all core and accessory genes against representative genomes of the GTDB database (release r214) 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 tree

39 universal phylogenetic markers genes were extracted from the MSPs (or the corresponding genome 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).
DOI: 10.5281/zenodo.10790012
title: A catalog of genes and species of the human skin microbiota

description: Dataset overviewThis dataset provides:a non-redundant high-quality catalog of 2.9 million genes392 Metagenomic Species Pangenomes (MSPs)This dataset can be used to analyze shotgun sequencing data of the human skin microbiota.
How to use this datasetCreate a gene abundance table by aligning reads from each sample against the catalog. For this purpose, you can use Meteor or NGLess. Then, normalize raw counts by gene length.Taxonomic profiling: the abundance of each species can be estimated as the average abundance of its 100 first core genes. To reduce the false positive rate, only consider that a species is present if at least 10/100 marker genes are detected.
Methods
Data sourcesThis dataset was built using the following data sources:118 isolate-derived genomes from the HMRGD246 isolate-derived genomes from the Skin Microbial Genome Collection (SMGC)1,407 skin metagenome assemblies from the Skin Microbial Genome Collection (SMGC)
Non-redundant gene catalogAfter filtering out short contigs (<1500 bp), genes were predicted with Prodigal on genomes (mode: single) and metagenome assemblies (mode: meta). Complete genes (partial=00) were pooled and 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.
Functional annotationKOs assignments were obtained with KofamScan using the KEGG 107 database.
MSPs recoveryReads from the 1,120 skin metagenomes available in the bioproject PRJNA46333 were aligned against the non-redundant gene catalog with the Meteor software suite to produce a raw gene abundance table (2.9M genes quantified in 1,120samples). Then, co-abundant genes were binned in 392 Metagenomic Species Pan-genomes (MSPs, i.e. clusters of co-abundant genes that likely belong to the same microbial species) using MSPminer.
MSPs taxonomic annotationTaxonomic annotation was performed by alignment of all core and accessory genes against representative genomes of the GTDB database (release r214) 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 MSPs (or the corresponding genome 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).
DOI: 10.5281/zenodo.11493269
title: A catalog of genes and species of the human skin microbiota

description: Dataset overviewThis dataset provides:a non-redundant high-quality catalog of 2.9 million genes392 Metagenomic Species Pangenomes (MSPs)This dataset can be used to analyze shotgun sequencing data of the human skin microbiota.
How to use this datasetCreate a gene abundance table by aligning reads from each sample against the catalog. For this purpose, you can use Meteor or NGLess. Then, normalize raw counts by gene length.Taxonomic profiling: the abundance of each species can be estimated as the average abundance of its 100 first core genes. To reduce the false positive rate, only consider that a species is present if at least 10/100 marker genes are detected.
Methods
Data sourcesThis dataset was built using the following data sources:118 isolate-derived genomes from the HMRGD246 isolate-derived genomes from the Skin Microbial Genome Collection (SMGC)1,407 skin metagenome assemblies from the Skin Microbial Genome Collection (SMGC)
Non-redundant gene catalogAfter filtering out short contigs (<1500 bp), genes were predicted with Prodigal on genomes (mode: single) and metagenome assemblies (mode: meta). Complete genes (partial=00) were pooled and 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.
Functional annotationKOs assignments were obtained with KofamScan using the KEGG 107 database.
MSPs recoveryReads from the 1,120 skin metagenomes available in the bioproject PRJNA46333 were aligned against the non-redundant gene catalog with the Meteor software suite to produce a raw gene abundance table (2.9M genes quantified in 1,120samples). Then, co-abundant genes were binned in 392 Metagenomic Species Pan-genomes (MSPs, i.e. clusters of co-abundant genes that likely belong to the same microbial species) using MSPminer.
MSPs taxonomic annotationTaxonomic annotation was performed by alignment of all core and accessory genes against representative genomes of the GTDB database (release r214) 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 MSPs (or the corresponding genome 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).
DOI: 10.5281/zenodo.12820845
title: A catalog of genes and species of the human skin microbiota

description: Dataset overviewThis dataset provides:a non-redundant high-quality catalog of 2.9 million genes392 Metagenomic Species Pangenomes (MSPs)This dataset can be used to analyze shotgun sequencing data of the human skin microbiota.
How to use this datasetCreate a gene abundance table by aligning reads from each sample against the catalog. For this purpose, you can use Meteor or NGLess. Then, normalize raw counts by gene length.Taxonomic profiling: the abundance of each species can be estimated as the average abundance of its 100 first core genes. To reduce the false positive rate, only consider that a species is present if at least 10/100 marker genes are detected.
Methods
Data sourcesThis dataset was built using the following data sources:118 isolate-derived genomes from the HMRGD246 isolate-derived genomes from the Skin Microbial Genome Collection (SMGC)1,407 skin metagenome assemblies from the Skin Microbial Genome Collection (SMGC)
Non-redundant gene catalogAfter filtering out short contigs (<1500 bp), genes were predicted with Prodigal on genomes (mode: single) and metagenome assemblies (mode: meta). Complete genes (partial=00) were pooled and 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.
Functional annotationKOs assignments were obtained with KofamScan using the KEGG 107 database.
MSPs recoveryReads from the 1,120 skin metagenomes available in the bioproject PRJNA46333 were aligned against the non-redundant gene catalog with the Meteor software suite to produce a raw gene abundance table (2.9M genes quantified in 1,120samples). Then, co-abundant genes were binned in 392 Metagenomic Species Pan-genomes (MSPs, i.e. clusters of co-abundant genes that likely belong to the same microbial species) using MSPminer.
MSPs taxonomic annotationTaxonomic annotation was performed by alignment of all core and accessory genes against representative genomes of the GTDB database (release r214) 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 MSPs (or the corresponding genome 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).
DOI: 10.5281/zenodo.13786083
title: A catalog of genes and species of the human skin microbiota

description: Dataset overviewThis dataset provides:a non-redundant high-quality catalog of 2.9 million genes392 Metagenomic Species Pangenomes (MSPs)This dataset can be used to analyze shotgun sequencing data of the human skin microbiota.
How to use this datasetCreate a gene abundance table by aligning reads from each sample against the catalog. For this purpose, you can use Meteor or NGLess. Then, normalize raw counts by gene length.Taxonomic profiling: the abundance of each species can be estimated as the average abundance of its 100 first core genes. To reduce the false positive rate, only consider that a species is present if at least 10/100 marker genes are detected.
Methods
Data sourcesThis dataset was built using the following data sources:118 isolate-derived genomes from the HMRGD246 isolate-derived genomes from the Skin Microbial Genome Collection (SMGC)1,407 skin metagenome assemblies from the Skin Microbial Genome Collection (SMGC)
Non-redundant gene catalogAfter filtering out short contigs (<1500 bp), genes were predicted with Prodigal on genomes (mode: single) and metagenome assemblies (mode: meta). Complete genes (partial=00) were pooled and 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.
Functional annotationKOs assignments were obtained with KofamScan using the KEGG 107 database.
MSPs recoveryReads from the 1,120 skin metagenomes available in the bioproject PRJNA46333 were aligned against the non-redundant gene catalog with the Meteor software suite to produce a raw gene abundance table (2.9M genes quantified in 1,120samples). Then, co-abundant genes were binned in 392 Metagenomic Species Pan-genomes (MSPs, i.e. clusters of co-abundant genes that likely belong to the same microbial species) using MSPminer.
MSPs taxonomic annotationTaxonomic annotation was performed by alignment of all core and accessory genes against representative genomes of the GTDB database (release r214) 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 MSPs (or the corresponding genome 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).
DOI: 10.5281/zenodo.14181341
title: A catalog of genes and species of the human skin microbiota

description: Dataset overviewThis dataset provides:a non-redundant high-quality catalog of 2.9 million genes392 Metagenomic Species Pangenomes (MSPs)This dataset can be used to analyze shotgun sequencing data of the human skin microbiota.
How to use this datasetCreate a gene abundance table by aligning reads from each sample against the catalog. For this purpose, you can use Meteor or NGLess. Then, normalize raw counts by gene length.Taxonomic profiling: the abundance of each species can be estimated as the average abundance of its 100 first core genes. To reduce the false positive rate, only consider that a species is present if at least 10/100 marker genes are detected.
Methods
Data sourcesThis dataset was built using the following data sources:118 isolate-derived genomes from the HMRGD246 isolate-derived genomes from the Skin Microbial Genome Collection (SMGC)1,407 skin metagenome assemblies from the Skin Microbial Genome Collection (SMGC)
Non-redundant gene catalogAfter filtering out short contigs (<1500 bp), genes were predicted with Prodigal on genomes (mode: single) and metagenome assemblies (mode: meta). Complete genes (partial=00) were pooled and 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.
Functional annotationKOs assignments were obtained with KofamScan using the KEGG 107 database.
MSPs recoveryReads from the 1,120 skin metagenomes available in the bioproject PRJNA46333 were aligned against the non-redundant gene catalog with the Meteor software suite to produce a raw gene abundance table (2.9M genes quantified in 1,120samples). Then, co-abundant genes were binned in 392 Metagenomic Species Pan-genomes (MSPs, i.e. clusters of co-abundant genes that likely belong to the same microbial species) using MSPminer.
MSPs taxonomic annotationTaxonomic annotation was performed by alignment of all core and accessory genes against representative genomes of the GTDB database (release r214) 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 MSPs (or the corresponding genome 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).