Journal: GigaScience

Article Title: LRTK: a platform agnostic toolkit for linked-read analysis of both human genome and metagenome

doi: 10.1093/gigascience/giae028

Figure Lengend Snippet: Evaluation of metagenome assemblies on linked-read sequencing. (A) Evaluation of the assembly performance for MEGAHIT, metaSPAdes, Athena, cloudSPAdes, and Pangaea on 10x Genomics, stLFR, and TELL-seq linked-read sequencing data from ATCC-MSA-1003. Pangaea does not support 10x Genomics linked reads. The left panel demonstrates the calculated NA50 values while the right panel shows the calculated N50 values. (B) Illustration of the 2 assembled circular contigs. (C) The distribution of completeness and contamination for reconstructed bins. (D) The number of contigs in each contig group. (E) The number of detected transfer RNAs in each contig group.

Article Snippet: Comparing different SNV callers implemented in LRTK, we discovered that approximately 176,891 SNVs were jointly detected by FreeBayes [22], SAMtools [ ], and inStrain [ ] on stLFR linked reads for ATCC-MSA-1003 (Fig. ).

Techniques: Sequencing

Journal: GigaScience

Article Title: LRTK: a platform agnostic toolkit for linked-read analysis of both human genome and metagenome

doi: 10.1093/gigascience/giae028

Figure Lengend Snippet: Evaluation of linked-read based detection of variation in the human genome. (A, B) Performance metrics on the detection of SNVs and INDELs using FreeBayes, GATK, and SAMtools for 10x Genomics, stLFR, and TELL-seq. (C, D) The performance on phasing of small variants using HapCUT2 and WhatsHap for 10x Genomics, stLFR, and TELL-seq. (E) Illustration of the performance on IBD detection. (F) The performance on detection of deletions using Aquila, LinkedSV, and LongRanger. (G) The performance on detection of insertions using Aquila, Pamir, and PopIns2.

Article Snippet: Comparing different SNV callers implemented in LRTK, we discovered that approximately 176,891 SNVs were jointly detected by FreeBayes [22], SAMtools [ ], and inStrain [ ] on stLFR linked reads for ATCC-MSA-1003 (Fig. ).

Techniques:

Journal: Frontiers in cellular and infection microbiology

Article Title: How to Count Our Microbes? The Effect of Different Quantitative Microbiome Profiling Approaches.

doi: 10.3389/fcimb.2020.00403

Figure Lengend Snippet: FIGURE 1 | Microbiome profile comparisons. Genus-level fecal microbial composition of both replicates of all 16 healthy study subjects (n = 32 samples) based upon (A) relative microbiome profiling (RMP), (B) quantitative microbiome profiling (QMP, cells per gram feces), (C) QMP after PMAxx-treatment of fecal samples (QMP-PMA, cells per gram feces), and (D) QMP using qPCR for quantification of bacterial load (QMP-qPCR, cells per gram feces).

Article Snippet: We used the Gut Microbiome Whole cell Mix (ATCC R© MSA2006TM) containing an even mixture of whole bacterial cells (12 different species) in order to assess whether cell-based or molecular-based quantification was more accurate.

Techniques:

Journal: Frontiers in cellular and infection microbiology

Article Title: How to Count Our Microbes? The Effect of Different Quantitative Microbiome Profiling Approaches.

doi: 10.3389/fcimb.2020.00403

Figure Lengend Snippet: FIGURE 2 | Within method dissimilarity of sample replicates and between methods dissimilarity of samples. Fecal microbial community structure variation based upon Bray–Curtis (BC) dissimilarity between samples and sample replicates. (A) Principal coordinates analysis of the study cohort based upon BC dissimilarity. Each segment connects the two replicates of the same sample as profiled by QMP (blue), QMP-PMA (green), and QMP-qPCR (red), (B) Box-plot of BC distance between sample replicates for all quantitative profiling methods (within-method variability) and BC distance in microbial community structure from the same sample profiled with different quantitative methods (between-method variability). The significance was checked pairwise using the Wilcoxon test and then adjusted for multiple comparisons using the FDR correction. The significance coding is indicated as ***p < 0.005, **p < 0.01, *p < 0.05 and N.S. for p ≥0.05. For clarity only significance of the comparisons between within QMP-method dissimilarity and all other within- and between-method dissimilarities are indicated (all FDR-corrected p-values are presented in Table S5).

Article Snippet: We used the Gut Microbiome Whole cell Mix (ATCC R© MSA2006TM) containing an even mixture of whole bacterial cells (12 different species) in order to assess whether cell-based or molecular-based quantification was more accurate.

Techniques:

Journal: International Journal of Molecular Sciences

Article Title: Cervicovaginal Microbiome and HPV: A Standardized Approach to 16S/ITS NGS and Microbial Community Profiling for Viral Association

doi: 10.3390/ijms26168090

Figure Lengend Snippet: Taxonomic profiles of the ATCC vaginal microbiome standard and six cytology samples representing Community State Types (CST) I-V. ( A ) Six microbial species in the ATCC standard (each at 16.7%) were sequenced in replicate across V-regions and shown as stacked bars. Species-level specificity varied by region: only V1V2 detected L. gasseri , while V4V5 missed L. jensenii . V3V4 showed the most balanced representation across replicates. ( B ) Stacked bar plots show taxonomic profiles from six cytology samples representing CST I–V, each dominated, respectively, by L. crispatus , L. gasseri , L. iners , mixed anaerobic bacteria, and L. jensenii . Species-level detection varied by V-region: L. crispatus (CST I) was identified only by V1V2 and V7V9; L. gasseri (CST II) only by V1V2; L. iners (CST III) was consistently detected across all regions; and L. jensenii (CST IV-A and V) was absent in V4V5.

Article Snippet: The ATCC Vaginal Microbiome Genomic Mix (ATCC MSA-1007) (American Type Culture Collection, Manassas, VA, USA) was used as a mock standard to mimic metagenomic samples and assess PCR and/or sequencing bias [ ].

Techniques: Bacteria

Journal: International Journal of Molecular Sciences

Article Title: Cervicovaginal Microbiome and HPV: A Standardized Approach to 16S/ITS NGS and Microbial Community Profiling for Viral Association

doi: 10.3390/ijms26168090

Figure Lengend Snippet: Cervicovaginal microbiome profiles of 66 cytology samples. ( A ). The dot plot shows Lactobacillus abundance per sample. Clustering identified three Lactobacillus states: L. -dominant (CST I–III, V), L. -diminished (IV-A), and L. -depleted (IV-B) based on abundance levels. Mean values with 95% CI and red reference lines distinguish these states. ( B ). The stacked bar chart shows microbial composition (%) per sample, based on the proportion of mapped reads. Of 30 species identified by 16S sequencing, the top 17 are shown in the legend. For each sample, the V-region with the best species-level resolution is displayed. ( C ) The merged stacked bar chart shows microbial composition by CST, highlighting the dominant species within each group. ( D ) The Venn diagram compares species abundance across CST, using CST I as the reference. Unique species (n) in CST II–V appear in the arms of the diagram, with those showing an absolute fold change > 1.5 noted in parentheses. ( E ) The bar chart highlights the significant shift in species (n = 27) for CST IV-B compared to CST I, marked by an enrichment of facultative and/or anaerobic organisms (green bars) and depletion of L. crispatus , gasseri , and jensenii (gray bars).

Article Snippet: The ATCC Vaginal Microbiome Genomic Mix (ATCC MSA-1007) (American Type Culture Collection, Manassas, VA, USA) was used as a mock standard to mimic metagenomic samples and assess PCR and/or sequencing bias [ ].

Techniques: Sequencing

Journal: International Journal of Molecular Sciences

Article Title: Cervicovaginal Microbiome and HPV: A Standardized Approach to 16S/ITS NGS and Microbial Community Profiling for Viral Association

doi: 10.3390/ijms26168090

Figure Lengend Snippet: Cervicovaginal microbial community structures. ( A ) 3D PCoA plot of 66 samples (color-coded by species) highlights L. crispatus , G. vaginalis , and L. iners as key drivers of microbial variation. ( B ) 3D PCoA plot grouped by CST shows CST I, IV-B, and III as major contributors to variance (36%, 17%, and 9%, respectively). β-diversity was assessed using Bray–Curtis (PERMANOVA, p < 0.05).

Article Snippet: The ATCC Vaginal Microbiome Genomic Mix (ATCC MSA-1007) (American Type Culture Collection, Manassas, VA, USA) was used as a mock standard to mimic metagenomic samples and assess PCR and/or sequencing bias [ ].

Techniques:

Journal: Genes

Article Title: Offline Next Generation Metagenomics Sequence Analysis Using MinION Detection Software (MINDS)

doi: 10.3390/genes10080578

Figure Lengend Snippet: Percentage of ATCC MSA-2002 reads mapped to strain by ONT’s cloud-based “What’s In My Pot” (WIMP) and offline Kraken taxonomic classifier. Strains are listed as they are referred to in NCBI’s RefSeq genome database. Reads and percentages in boldface had the highest number of strain reads mapped per species.

Article Snippet: Using ATCC MSA-2002 as a metagenomic mock community allowed us to test the MINDS pipeline with a variety of gram-positive and gram-negative bacteria.

Techniques: