Journal: Genes

Article Title: Identification of Major Rhizobacterial Taxa Affected by a Glyphosate-Tolerant Soybean Line via Shotgun Metagenomic Approach

doi: 10.3390/genes9040214

Figure Lengend Snippet: Comparison of the relative abundances of top 10 dominant genera in the rhizospheric soil between N698 and MD12. ( A ) Results revealed by 16S rDNA V5–V7 hypervariable region amplicon deep sequencing. ( B ) Results revealed by 16S rDNA V4 hypervariable region amplicon deep sequencing. ( C ) Results revealed by shotgun metagenomic approaches (analyzed by One Codex). Error bars indicate standard errors; * p < 0.05; ** p < 0.01. MRh1–3 and NRh1–3 represent three biological rhizospheric soil replicates of MD12 (MRh) and those of N698 (NRh), respectively. MGMRh and MGNRh represent pooled rhizospheric DNA sample of MRh and NRh for shotgun metagenome sequencing, respectively.

Article Snippet: The following are available online at http://www.mdpi.com/2073-4425/9/4/214/s1 , Figure S1: Rarefaction curves of alpha diversity indices of MRh and NRh samples (16S rDNA V5–V7), File S1: OTU_final sequence in MRh and NRh samples (16S rDNA V5–V7), Table S1: Summary of reads, tags and OTUs of MRh and NRh samples (16S rDNA V5–V7), Table S2: Normalized OTU table for biom format of MRh and NRh samples at the flowering stage (16S rDNA V5–V7), Table S3: Comparison of alpha diversity between MRh and NRh based on normalized OTU table (16S rDNA V5–V7), Table S4: Differentially relative abundances of bacterial phyla between MRh and NRh (16S rDNA V5–V7), Table S5: Absolute abundances of bacterial genera in MRh and NRh samples (16S rDNA V5–V7), Table S6: Differentially relative abundances of characterized genera between MRh and NRh (16S rDNA V5–V7), Table S7: Absolute abundances of bacterial species in MRh and NRh samples (16S rDNA V5–V7), Table S8: Top 30 dominant genera in MRh and NRh samples revealed by 16S rDNA amplicon deep sequencing, Table S9: Quality control report and statistical summary of assembled metagenome sequencing data (IDBA + One codex), Table S10: Comparison of mapped reads between MGMRh and MGNRh at all taxonomic levels by SOAPaligner, Table S11: Computation and comparison of mapped reads between MGMRh and MGNRh at all taxonomic levels by One Codex based on One Codex DB, Table S12: Computation and comparison of mapped reads between MGMRh and MGNRh at the genus level by One Codex based on One Codex DB, Table S13: Computation and comparison of mapped reads between MGMRh and MGNRh at all taxonomic levels by One Codex based on RefSeq DB, Table S14: Computation and comparison of mapped reads between MGMRh and MGNRh at the genus level by One Codex based on RefSeq DB, Table S15: Computation and comparison of mapped reads between MGMRh and MGNRh at the species level by One Codex based on One Codex DB, Table S16: Computation and comparison of mapped reads between MGMRh and MGNRh at the species level by One Codex based on RefSeq DB, Table S17: Comparison of mapped read count (%) of top 30 genera in the MGMRh and MGNRh samples by shotgun metagenomic approaches.

Techniques: Amplification, Sequencing

Journal: bioRxiv

Article Title: Genomics-Based Identification of Microorganisms in Human Ocular Body Fluid

doi: 10.1101/176529

Figure Lengend Snippet: Bacterial isolates were obtained at the hospital laboratory (1 st cultivation) from vitreous from endophthalmitis patients following cataract surgery (C1-7) and intravitreal injection (I1-7) and the taxonomic affiliation of the isolates were determined by MALDI-TOF mass spectrometry (MS). Vitreous was analyzed through metagenomics at the research laboratory using two DNA isolation methods (QIAamp DNA Mini Kit, QIA; QIAamp UCP Pathogen Mini kit, UCP) and the taxonomic affiliation of reads was determined. The detected amount of human DNA sequences in percent (%) is provided in the first column of the Metagenomics tab. In the horizontal bar charts, the taxonomic identity and relative fraction of microbial reads for the most abundant identified organisms based on the Kraken+Bracken analysis is indicated for both DNA isolation methods. The read counts for the most abundant organism according to the Kraken+Bracken (all reads) and BLASTN (forward read) analyses are indicated to the right. The read counts for the most abundant organisms per sample as determined by Kraken, Bracken, and BLASTn analyses are available through figshare at https://figshare.com/s75feabfad1d8c495bf7a3 . Bacterial isolates for some samples were obtained in a second round of cultivation at the research laboratory (2 nd cultivation), and one representative per colony morphotype per vitreous sample was subjected to MS and whole genome sequencing (WGS). The taxonomic affiliation of isolates was determined through classification of assembled genomes using a k-mer based approach and genomic MLST, and antibiotic resistance genes were identified using ResFinder. Furthermore, metagenomic assemblies were generated from the shotgun metagenomic reads and analyzed with regards to taxonomic affiliation and selected functional characteristics (Supplementary Table S6). A video summary is available from figshare at https://figshare.com/s/38fe043f6a8ef1710444 .

Article Snippet: Species identification was performed using MALDI-TOF mass spectrometry analysis (MALDI Biotyper 3.1, Bruker Daltonics Microflex LT, database MBT DB-5627) from colony material.

Techniques: Injection, Mass Spectrometry, Metagenomics, DNA Extraction, Sequencing, Generated, Functional Assay

Journal: Microbiome

Article Title: The impact of rumen and hindgut microbiomes on the persistent productivity of long-lived dairy cows

doi: 10.1186/s40168-025-02309-1

Figure Lengend Snippet: Integrated omics analysis based on metagenomics and metabolomics. A Multiple correlation analysis of rumen, serum, and milk metabolites. Edges between nodes represent Spearman correlations: green lines indicate positive correlations and gray lines indicate negative correlations (| r |> 0.50, P < 0.05). B Multiple correlation analysis of rectum, serum, and milk metabolites. Edges between nodes represent Spearman correlations: green lines indicate positive correlations and gray lines indicate negative correlations (| r |> 0.50, P < 0.05). C The PLS-PM module provided an integrated analysis of the relationships among rumen microbial module 6, rumen GPEtn(12:0/18:3), serum EPA, milk Pe(20:5/0:0), and MY. Numbers above arrows represent standardized effect coefficients. R . 2 = coefficient of determination; GoF = goodness-of-fit index. EPA = eicosapentaenoic acid; MY = milk yield. * P < 0.05; ** P < 0.01

Article Snippet: The metagenomic raw sequencing data have been deposited in the National Center for Biotechnology Information (NCBI) under the BioProject accession number PRJNA1288150.

Techniques: Metagenomics

Journal: Biotechnology for Biofuels and Bioproducts

Article Title: Comparative metagenomics reveals the metabolic flexibility of coastal prokaryotic microbiomes contributing to lignin degradation

doi: 10.1186/s13068-025-02605-w

Figure Lengend Snippet: The reconstructed MAGs from L6 ( A ) and L18 ( B ) consortia. Sankey diagrams showed recovered archaeal and bacterial MAG information at different taxonomic levels (relative abundance > 1%), based on the GTDB classification. Numbers indicate the relative abundance of MAGs recovered for that lineage. Relative abundance was determined by mapping each MAG against quality-filtered metagenome reads using CoverM. Significant differences between L6 and L18 consortia are marked with asterisks ( p < 0.05). Data are from three biological replicates

Article Snippet: The metagenome sequencing data were deposited in the NCBI SRA database under the accession number PRJNA1113784, as well as in the NODE ( https://www.biosino.org/node/ ) under project ID OEP005460 (Experimental ID OEX00028629).

Techniques:

Journal: Frontiers in Veterinary Science

Article Title: Metagenomic insights into isolable bacterial communities and antimicrobial resistance in airborne dust from pig farms

doi: 10.3389/fvets.2024.1362011

Figure Lengend Snippet: Metagenomic classifications of bacterial community compositions at Phylum level ( A) and Genus level ( B) of airborne dust inside seven pig farms by Proportion (percentage of sequencing reads that align or map to a specific phyla and genus to the total number of reads) of top 5 phyla and genus.

Article Snippet: The mcr 6.1 gene was detected in Klebsiella species, while Pseudomonas and Enterobacter species did not host any resistance genes as indicated by Bugseq metagenomic sequencing analysis data ( ).

Techniques: Sequencing

Journal: Frontiers in Veterinary Science

Article Title: Metagenomic insights into isolable bacterial communities and antimicrobial resistance in airborne dust from pig farms

doi: 10.3389/fvets.2024.1362011

Figure Lengend Snippet: Antimicrobial resistance genes found in airborne dust inside pig farms ( n = 7) by metagenomic sequencing. ( A) Proportions of the number of detected genes for each antibiotic class. Proportion represents the percentage of number of detected genes for each antibiotic class to the total number of detected genes of resistance genes of different antibiotic classes from airborne dust collected inside the 7 pig farms. ( B) Resistance gene profile in airborne dust inside the 7 pig farms. The stacked bar chart represents the number of detected resistance genes in each antibiotic class in each pig farm. Each colored segment within the bars corresponds to a specific resistance gene.

Article Snippet: The mcr 6.1 gene was detected in Klebsiella species, while Pseudomonas and Enterobacter species did not host any resistance genes as indicated by Bugseq metagenomic sequencing analysis data ( ).

Techniques: Sequencing

Journal: Cell systems

Article Title: Minimizer-space de Bruijn graphs: Whole-genome assembly of long reads in minutes on a personal computer

doi: 10.1016/j.cels.2021.08.009

Figure Lengend Snippet: KEY RESOURCES TABLE

Article Snippet: Highly efficient assembly of real HiFi metagenomes using mdBG We performed an assembly of two real HiFi metagenome datasets (mock communities Zymo D6331 and ATCC MSA-1003, accessions GenBank: SRX9569057 and GenBank: SRX8173258).

Techniques: Software

Journal: Cell systems

Article Title: Minimizer-space de Bruijn graphs: Whole-genome assembly of long reads in minutes on a personal computer

doi: 10.1016/j.cels.2021.08.009

Figure Lengend Snippet: Metagenome assembly statistics of the Zymo D6331 dataset (left) and the ATCC MSA-1003 dataset (right) using hifiasm-meta and rust-mdbg

Article Snippet: Highly efficient assembly of real HiFi metagenomes using mdBG We performed an assembly of two real HiFi metagenome datasets (mock communities Zymo D6331 and ATCC MSA-1003, accessions GenBank: SRX9569057 and GenBank: SRX8173258).

Techniques:

Journal: Cell systems

Article Title: Minimizer-space de Bruijn graphs: Whole-genome assembly of long reads in minutes on a personal computer

doi: 10.1016/j.cels.2021.08.009

Figure Lengend Snippet: KEY RESOURCES TABLE

Article Snippet: Highly efficient assembly of real HiFi metagenomes using mdBG We performed an assembly of two real HiFi metagenome datasets (mock communities Zymo D6331 and ATCC MSA-1003, accessions GenBank: SRX9569057 and GenBank: SRX8173258).

Techniques: Software