title: Metagenome-assembled genome: SRP278969_co1_385
organism: uncultured Candidatus Saccharibacteria bacterium
description: This sample represents a Third Party Annotation (TPA) Metagenome-assembled genome assembled from the metagenomic runs SRR12529037,SRR12529044 of study SRP278969.
attributes.tag: ENA first public
attributes.value: 2024-12-11
attributes.tag: INSDC center name
attributes.value: CMR
attributes.tag: INSDC status
attributes.value: public
attributes.tag: Submitter Id
attributes.value: SRP278969_co1_385
attributes.tag: assembly quality
attributes.value: Many fragments with little to no review of assembly other than reporting of standard assembly statistics
attributes.tag: assembly software
attributes.value: metaSPAdes;3.15.4;default
attributes.tag: binning parameters
attributes.value: coverage and kmer
attributes.tag: binning software
attributes.value: MetaBAT;2.15;default
attributes.tag: broad-scale environmental context
attributes.value: host
attributes.tag: collection date
attributes.value: 2018-10-12
attributes.tag: completeness score
attributes.value: 98.77
attributes.tag: completeness software
attributes.value: CheckM2;1.0.2;default
attributes.tag: contamination score
attributes.value: 0.45
attributes.tag: environmental medium
attributes.value: host
attributes.tag: geographic location (country and/or sea)
attributes.value: not provided
attributes.tag: geographic location (latitude)
attributes.value: not provided
attributes.tag: geographic location (longitude)
attributes.value: not provided
attributes.tag: investigation type
attributes.value: metagenome-assembled genome
attributes.tag: isolation_source
attributes.value: gut metagenome
attributes.tag: local environmental context
attributes.value: gut biome
attributes.tag: metagenomic source
attributes.value: gut metagenome
attributes.tag: project name
attributes.value: Gastrointestinal tract (GIT) microbiomes in ruminant play major roles in host health and thus animal production. However, an integrated characterization of microbial community structure and function is yet to be obtained as prior studies are predominantly biased towards the rumen. Therefore, to acquire a microbiota inventory of the discrete GIT compartments, we generated shotgun metagenomic data from 370 samples that represent 10 GIT regions of seven ruminant species. Our analyses reconstructed a GIT microbial reference catalog with >154 million nonredundant gene, and identified 8,745 uncultured candidate species from over 10,000 metagenome-assembled genomes. The integrated gene catalog across the GIT regions demonstrate spatial associations between the microbiome and physiological adaptations, and 8,745 newly characterized genomes substantially expand the genomic landscape of ruminant microbiota, particularly microbiomes from the lower gut. This substantially expands the previously known set of endogenous microbial diversity and the taxonomic classification rate of GIT microbiome. These candidate species encode hundreds of enzymes and novel biosynthetic gene clusters that improves our understanding concerning methane production and feed efficiency in ruminants. Overall, our study expands the characterization of the ruminant GIT microbiota at unprecedented spatial resolution, and offers clues for improving ruminant livestock production in the future. Having access to comprehensive gene catalog and collections of microbial genomes provide the ability to perform efficiently genome-based analysis to achieve a detailed classification of GIT microbial ecosystem composition. Our study will bring unprecedented power in future association studies to investigate the impact of the GIT microbiota in ruminant health and production.
attributes.tag: sample derived from
attributes.value: SAMN15829918,SAMN15829912
attributes.tag: sample name
attributes.value: SRP278969_co1_385
attributes.tag: scientific_name
attributes.value: uncultured Candidatus Saccharibacteria bacterium
attributes.tag: sequencing method
attributes.value: Illumina NovaSeq 6000
attributes.tag: taxonomic identity marker
attributes.value: multi-marker approach