MAAWf: A Multifunctional and Visual Tool for Microbiomic Data Analyses

Abstract Background: Microbiomic research has grown in popularity in recent decades. The widespread use of next-generation sequencing technologies, including 16S rRNA gene-based and metagenomic shotgun-based methods, has produced a wealth of microbiome data. At present, most software and analysis workflows for analysis and processing of microbiomic data are command line-based, which requires considerable computing time and makes interaction difficult. Results: To provide a command-line free, multifunctional, user interface friendly and online/local deployable microbiome analysis tool, we developed Microbiome Automated Analysis Workflows (MAAWf). MAAWf is composed of a whole metagenomic shotgun workflow (WMS) and a 16S Sequencing Workflow. The WMS analysis workflow assesses taxonomy, protein-coding genes, metabolic pathways, carbohydrate-active enzymes (CAZy) and antibiotic resistance genes (ARGs). The 16S ribosomal RNA (rRNA) analysis workflow counts and clusters operational taxonomic units (OTUs), estimates alpha- and beta-diversity and inter-group differences, and performs functional analysis. We also compared MAAWf with other commonly avaiable analysis tools using two public datasets. The MAAWf pipeline was established using the Ubuntu 16.04.6 LTS kernel with primary sequence files such as FASTQ format and taxonomic format such as OTU or BIOM formats. Following analysis of public 16S and WMS datasets, MAAWf obtained similar results to DIAMOND-MEGAN6, MG-RAST, DADA2 and QIIME2, but the running time was much shorter. Conclusions: MAAWf is a visual, integrated, rapid analysis tool that enables remote and local computing of microbiome data.

Download Full-text

MAAWf: An Integrated and Visual Tool for Microbiome Data Analyses

10.21203/rs.2.23215/v1 ◽

2020 ◽

Author(s):

Sibo Zhu ◽

Tao Sun ◽

Chengkai Zhu ◽

Tao Qing ◽

Yanfeng Jiang ◽

...

Keyword(s):

Computing Time ◽

Antibiotic Resistance Genes ◽

Automated Analysis ◽

Rrna Gene ◽

Analysis Tool ◽

Command Line ◽

Protein Coding ◽

Functional Annotations ◽

Sequencing Technologies ◽

Microbiome Data

Abstract BackgroundMicrobiomic research has grown in popularity in recent decades. The widespread use of next-generation sequencing technologies, including 16S rRNA gene-based and metagenomic shotgun-based methods, has produced a wealth of microbiome data. At present, most software and analysis workflows for analysis and processing of microbiomic data are command line-based, which requires considerable computing time and makes interaction difficult.ResultsTo provide a command-line free, integrated, user interface friendly and online/local deployable microbiome analysis tool, we developed Microbiome Automated Analysis Workflows (MAAWf). The MAAWf assesses taxonomy, protein-coding genes, metabolic pathways, carbohydrate-active enzymes (CAZy) and antibiotic resistance genes (ARGs) for WMS, and clusters operational taxonomic units, alpha-/beta-diversity and functional annotations for 16S. MAAWf generates similar results to currently popular tools, but the running time was much shorter. MAAWf is freely accessible and source code available at http://www.maawf.com.ConclusionsMAAWf is a visual, integrated, rapid analysis tool that enables remote and local computing of microbiome data.

Download Full-text

ReFDash - A Repository of Functional Dashboards providing comprehensive functional insights inferred from 16S microbiome data sets

10.20944/preprints201909.0070.v1 ◽

2019 ◽

Author(s):

Sunil Nagpal ◽

Mohammed Monzoorul Haque ◽

Sharmila S. Mande

Keyword(s):

Cost Effective ◽

Amplicon Sequencing ◽

Rrna Gene ◽

Data Sets ◽

16S Sequencing ◽

Function Data ◽

Effective Manner ◽

Microbiome Research ◽

Significant Extension ◽

Microbiome Data

Motivation: 16S rRNA gene amplicon based sequencing has significantly expanded the scope of metagenomics research by enabling microbial community analyses in a cost-effective manner. The possibility to infer functional potential of a microbiome through amplicon sequencing derived taxonomic abundance profiles has further strengthened the utility of 16S sequencing. In fact, a surge in 'inferred function metagenomic analysis' has recently taken place, wherein most 16S microbiome studies include inferred functional insights in addition to taxonomic characterization. Tools like PICRUSt, Tax4Fun, Vikodak and iVikodak have significantly eased the process of inferring function potential of a microbiome using the taxonomic abundance profile. A platform that can enable hosting of inferred function 'metagenomic studies' with comprehensive metadata driven search utilities (of a typical database), coupled with on-the-fly comparative analytics between studies of interest, can be a major improvement to the state of art. ReFDash represents an effort in the proposed direction. Methods: This work introduces ReFDash - a Repository of Functional Dashboards. ReFDash, developed as a significant extension of iVikodak (function inference tool), provides three broad unique offerings in inferred function space - (i) a platform that hosts a database of inferred function data being continously updated using public 16S metagenomic studies (ii) a tool to search studies of interest and compare upto three metagenomic environments on the fly (iii) a community initiative wherein users can contribute their own inferred function data to the platform. ReFDash therefore provides a first-of-its-kind community-driven frame-work for scientific collaboration, data analytics, and sharing in this area of microbiome research. Results: Overall, the ReFDash database is aimed at compiling together a global ensemble of 16S-derived Functional Metagenomics projects. ReFDash currently hosts close to 50 ready-to-use, re-analyzable functional dashboards representing data from approximately 18,000 microbiome samples sourced from various published studies. Each entry also provides direct downloadable links to associated taxonomic files and metadata employed for analysis. Conclusion: The vision behind ReFDash is creation of a framework, wherein users can not only analyze their microbiome datasets in functional terms, but also contribute towards building an information base by submitting their functional analyses to ReFDash database. ReFDash web-server may be freely accessed at https://web.rniapps.net/iVikodak/refdash/

Download Full-text

Retinitis pigmentosa is associated with shifts in the gut microbiome

Scientific Reports ◽

10.1038/s41598-021-86052-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Oksana Kutsyr ◽

Lucía Maestre-Carballa ◽

Mónica Lluesma-Gomez ◽

Manuel Martinez-Garcia ◽

Nicolás Cuenca ◽

...

Keyword(s):

Retinitis Pigmentosa ◽

Gut Microbiome ◽

Functional Decline ◽

Retinal Disease ◽

Photoreceptor Cells ◽

Amplicon Sequencing ◽

Rrna Gene ◽

Sequencing Data ◽

Alpha And Beta Diversity ◽

Potential Biomarker

AbstractThe gut microbiome is known to influence the pathogenesis and progression of neurodegenerative diseases. However, there has been relatively little focus upon the implications of the gut microbiome in retinal diseases such as retinitis pigmentosa (RP). Here, we investigated changes in gut microbiome composition linked to RP, by assessing both retinal degeneration and gut microbiome in the rd10 mouse model of RP as compared to control C57BL/6J mice. In rd10 mice, retinal responsiveness to flashlight stimuli and visual acuity were deteriorated with respect to observed in age-matched control mice. This functional decline in dystrophic animals was accompanied by photoreceptor loss, morphologic anomalies in photoreceptor cells and retinal reactive gliosis. Furthermore, 16S rRNA gene amplicon sequencing data showed a microbial gut dysbiosis with differences in alpha and beta diversity at the genera, species and amplicon sequence variants (ASV) levels between dystrophic and control mice. Remarkably, four fairly common ASV in healthy gut microbiome belonging to Rikenella spp., Muribaculaceace spp., Prevotellaceae UCG-001 spp., and Bacilli spp. were absent in the gut microbiome of retinal disease mice, while Bacteroides caecimuris was significantly enriched in mice with RP. The results indicate that retinal degenerative changes in RP are linked to relevant gut microbiome changes. The findings suggest that microbiome shifting could be considered as potential biomarker and therapeutic target for retinal degenerative diseases.

Download Full-text

Impact of Diagnosed and Undiagnosed Respiratory Pseudomonas on VAP and VAE During Long-Term Acute Care

Infection Control and Hospital Epidemiology ◽

10.1017/ice.2020.823 ◽

2020 ◽

Vol 41 (S1) ◽

pp. s258-s259

Author(s):

James Harrigan ◽

Ebbing Lautenbach ◽

Emily Reesey ◽

Magda Wernovsky ◽

Pam Tolomeo ◽

...

Keyword(s):

Bacterial Community ◽

Acute Care ◽

Statistical Significance ◽

Rrna Gene ◽

Care Hospital ◽

16S Sequencing ◽

Increased Risk ◽

Wide Range ◽

The Impact

Background: Clinically diagnosed ventilator-associated pneumonia (VAP) is common in the long-term acute-care hospital (LTACH) setting and may contribute to adverse ventilator-associated events (VAEs). Pseudomonas aeruginosa is a common causative organism of VAP. We evaluated the impact of respiratory P. aeruginosa colonization and bacterial community dominance, both diagnosed and undiagnosed, on subsequent P. aeruginosa VAP and VAE events during long-term acute care. Methods: We enrolled 83 patients on LTACH admission for ventilator weaning, performed longitudinal sampling of endotracheal aspirates followed by 16S rRNA gene sequencing (Illumina HiSeq), and bacterial community profiling (QIIME2). Statistical analysis was performed with R and Stan; mixed-effects models were fit to relate the abundance of respiratory Psa on admission to clinically diagnosed VAP and VAE events. Results: Of the 83 patients included, 12 were diagnosed with P. aeruginosa pneumonia during the 14 days prior to LTACH admission (known P. aeruginosa), and 22 additional patients received anti–P. aeruginosa antibiotics within 48 hours of admission (suspected P. aeruginosa); 49 patients had no known or suspected P. aeruginosa (unknown P. aeruginosa). Among the known P. aeruginosa group, all 12 patients had P. aeruginosa detectable by 16S sequencing, with elevated admission P. aeruginosa proportional abundance (median, 0.97; IQR, 0.33–1). Among the suspected P. aeruginosa group, all 22 patients had P. aeruginosa detectable by 16S sequencing, with a wide range of admission P. aeruginosa proportional abundance (median, 0.0088; IQR, 0.00012–0.31). Of the 49 patients in the unknown group, 47 also had detectable respiratory Psa, and many had high P. aeruginosa proportional abundance at admission (median, 0.014; IQR, 0.00025–0.52). Incident P. aeruginosa VAP was observed within 30 days in 4 of the known P. aeruginosa patients (33.3%), 5 of the suspected P. aeruginosa patients (22.7%), and 8 of the unknown P. aeruginosa patients (16.3%). VAE was observed within 30 days in 1 of the known P. aeruginosa patients (8.3%), 2 of the suspected P. aeruginosa patients (9.1%), and 1 of the unknown P. aeruginosa patients (2%). Admission P. aeruginosa abundance was positively associated with VAP and VAE risk in all groups, but the association only achieved statistical significance in the unknown group (type S error <0.002 for 30-day VAP and <0.011 for 30-day VAE). Conclusions: We identified a high prevalence of unrecognized respiratory P. aeruginosa colonization among patients admitted to LTACH for weaning from mechanical ventilation. The admission P. aeruginosa proportional abundance was strongly associated with increased risk of incident P. aeruginosa VAP among these patients.Funding: NoneDisclosures: None

Download Full-text

Effect of Bovine MFGM and LF in Infant Formula on Gut Microbiome and Metabolome at Four Months of Age

Current Developments in Nutrition ◽

10.1093/cdn/nzab027 ◽

2021 ◽

Author(s):

Maciej Chichlowski ◽

Nicholas Bokulich ◽

Cheryl L Harris ◽

Jennifer L Wampler ◽

Fei Li ◽

...

Keyword(s):

Infant Formula ◽

Beta Diversity ◽

Milk Fat ◽

Alpha Diversity ◽

Illumina Miseq ◽

Rrna Gene ◽

Term Infants ◽

Linear Discriminant ◽

Alpha And Beta Diversity ◽

Metabolite Profiles

Abstract Background Milk fat globule membrane (MFGM) and lactoferrin (LF) are human milk bioactive components demonstrated to support gastrointestinal (GI) and immune development. Significantly fewer diarrhea and respiratory-associated adverse events through 18 months of age were previously reported in healthy term infants fed a cow's milk-based infant formula with added source of bovine MFGM and bovine LF through 12 months of age. Objectives To compare microbiota and metabolite profiles in a subset of study participants. Methods Stool samples were collected at Baseline (10–14 days of age) and Day 120 (MFGM + LF: 26, Control: 33). Bacterial community profiling was performed via16S rRNA gene sequencing (Illumina MiSeq) and alpha and beta diversity were analyzed (QIIME 2). Differentially abundant taxa were determined using Linear discriminant analysis effect size (LefSE) and visualized (Metacoder). Untargeted stool metabolites were analyzed (HPLC/mass spectroscopy) and expressed as the fold-change between group means (Control: MFGM + LF ratio). Results Alpha diversity increased significantly in both groups from baseline to 4 months. Subtle group differences in beta diversity were demonstrated at 4 months (Jaccard distance; R2 = 0.01, P = 0.042). Specifically, Bacteroides uniformis and Bacteroides plebeius were more abundant in the MFGM + LF group at 4 months. Metabolite profile differences for MFGM + LF vs Control included: lower fecal medium chain fatty acids, deoxycarnitine, and glycochenodeoxycholate, and some higher fecal carbohydrates and steroids (P < 0.05). After applying multiple test correction, the differences in stool metabolomics were not significant. Conclusions Addition of bovine MFGM and LF in infant formula was associated with subtle differences in stool microbiome and metabolome by four months of age, including increased prevalence of Bacteroides species. Stool metabolite profiles may be consistent with altered microbial metabolism. Trial registration: https://clinicaltrials.gov/ct2/show/NCT02274883).

Download Full-text

Interannual comparison of core taxa and community composition of the blow microbiota from East Australian humpback whales

FEMS Microbiology Ecology ◽

10.1093/femsec/fiz102 ◽

2019 ◽

Vol 95 (8) ◽

Cited By ~ 5

Author(s):

C Vendl ◽

B C Ferrari ◽

T Thomas ◽

E Slavich ◽

E Zhang ◽

...

Keyword(s):

Multiple Stressors ◽

Physiological State ◽

Temporal Stability ◽

Individual Variability ◽

Habitat Destruction ◽

Humpback Whales ◽

Large Individual ◽

Rrna Gene ◽

Alpha And Beta Diversity ◽

Transient Nature

ABSTRACT Cetacean represent vulnerable species impacted by multiple stressors, including reduction in prey species, habitat destruction, whaling and infectious disease. The composition of blow microbiota has been claimed to provide a promising tool for non-invasive health monitoring aiming to inform conservation management. Still, little is known about the temporal stability and composition of blow microbiota in whales. We used East Australian humpback whales (Megaptera novaeangliae) as a model species and collected blow and control samples in August 2016 and 2017 for an interannual comparison. We analysed the blow by barcode tag sequencing of the bacterial 16S rRNA gene. We found that the microbial communities in 2016 and 2017 were statistically similar regarding alpha and beta diversity but distinct to seawater. Zero-radius operational taxonomic units (zOTUs) shared by both groups accounted for about 50% of all zOTUs present. Still, the large individual variability in the blow microbiota resulted in a small number of core taxa (defined as present in at least 60% of whales). We conclude that the blow microbiota of humpback whales is either generally limited and of transient nature or the reduced airway microbiota is the symptom of a compromised physiological state potentially due to the challenges of the whales‘ annual migration.

Download Full-text

The Fecal Bacterial Microbiota in Horses with Equine Recurrent Uveitis

Animals ◽

10.3390/ani11030745 ◽

2021 ◽

Vol 11 (3) ◽

pp. 745

Author(s):

Michelle Martin de Bustamante ◽

Diego Gomez ◽

Jennifer MacNicol ◽

Ralph Hamor ◽

Caryn Plummer

Keyword(s):

Beta Diversity ◽

High Throughput Sequencing ◽

Illumina Miseq ◽

Diversity Indices ◽

Rrna Gene ◽

Linear Discriminant ◽

Alpha And Beta Diversity ◽

Bacterial Microbiota ◽

Healthy Control ◽

Equine Recurrent Uveitis

The objective of this study was to describe and compare the fecal bacterial microbiota of horses with equine recurrent uveitis (ERU) and healthy horses using next-generation sequencing techniques. Fecal samples were collected from 15 client-owned horses previously diagnosed with ERU on complete ophthalmic examination. For each fecal sample obtained from a horse with ERU, a sample was collected from an environmentally matched healthy control with no evidence of ocular disease. The Illumina MiSeq sequencer was used for high-throughput sequencing of the V4 region of the 16S rRNA gene. The relative abundance of predominant taxa, and alpha and beta diversity indices were calculated and compared between groups. The phyla Firmicutes, Bacteroidetes, Verrucomicrobia, and Proteobacteria predominated in both ERU and control horses, accounting for greater than 60% of sequences. Based on linear discriminant analysis effect size (LEfSe), no taxa were found to be enriched in either group. No significant differences were observed in alpha and beta diversity indices between groups (p > 0.05 for all tests). Equine recurrent uveitis is not associated with alteration of the gastrointestinal bacterial microbiota when compared with healthy controls.

Download Full-text

Citrobacter tructae sp. nov. Isolated from Kidney of Diseased Rainbow Trout (Oncorhynchus mykiss)

Microorganisms ◽

10.3390/microorganisms9020275 ◽

2021 ◽

Vol 9 (2) ◽

pp. 275

Author(s):

Won Joon Jung ◽

Hyoun Joong Kim ◽

Sib Sankar Giri ◽

Sang Guen Kim ◽

Sang Wha Kim ◽

...

Keyword(s):

Rainbow Trout ◽

Oncorhynchus Mykiss ◽

Biochemical Characterization ◽

Antibiotic Resistance Genes ◽

Housekeeping Genes ◽

Rrna Gene ◽

Bacterial Identification ◽

Rainbow Trout Oncorhynchus Mykiss ◽

The 16S Rrna Gene ◽

Genome Distance

A novel Citrobacter species was isolated from the kidney of diseased rainbow trout (Oncorhynchus mykiss) reared on a trout farm. Biochemical characterization and phylogenetic analysis were performed for bacterial identification. Sequencing of the 16S rRNA gene and five housekeeping genes indicated that the strain belongs to the Citrobacter genus. However, multilocus sequence analysis, a comparison of average nucleotide identity, and genome-to-genome distance values revealed that strain SNU WT2 is distinct and forms a separate clade from other Citrobacter species. Additionally, the phenotype characteristics of the strain differed from those of other Citrobacter species. Quinone analysis indicated that the predominant isoprenoid quinone is Q-10. Furthermore, strain virulence was determined by a rainbow trout challenge trial, and the strain showed resistance to diverse antibiotics including β-lactams, quinolone, and aminoglycosides. The complete genome of strain SNU WT2 is 4,840,504 bp with a DNA G + C content of 51.94% and 106,068-bp plasmid. Genome analysis revealed that the strain carries virulence factors on its chromosome and antibiotic resistance genes on its plasmid. This strain represents a novel species in the genus Citrobacter for which the name C. tructae has been proposed, with SNU WT2 (=KCTC 72517 = JCM 33612) as the type strain.

Download Full-text

AB1232 ORAL DYSBIOSIS REFLECTS THE IMMUNOLOGICAL ALTERATION OF RA REGARDING TO ACPA AND HLA DRB1*SE: NAGASAKI ISLAND STUDY

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2020-eular.5147 ◽

2020 ◽

Vol 79 (Suppl 1) ◽

pp. 1907.2-1907

Author(s):

Y. Tsuji ◽

M. Tamai ◽

S. Morimoto ◽

D. Sasaki ◽

M. Nagayoshi ◽

...

Keyword(s):

Beta Diversity ◽

Healthy Subjects ◽

Microbial Community Composition ◽

Alpha Diversity ◽

Enzyme Linked Immunosorbent Assay ◽

Rrna Gene ◽

Oral Microbiota ◽

Unifrac Distance ◽

Alpha And Beta Diversity ◽

The Difference

Background:Anti-citrullinated protein antibody (ACPA) production is observed in several organs even prior to the onset of rheumatoid arthritis (RA), and oral mucosa is considered to be one of the important tissues. The presence of HLA-DRB1*SE closely associates with ACPA production. Saliva is considered to reflect the oral microbiota including periodontal disease. Alteration of oral microbiota of RA becomes to be normalized by DMARDs treatment, however, the interaction of HLA-DRB1*SE, ACPA and oral microbiota of RA patients remains to be elucidated.Objectives:The Nagasaki Island Study, which had started in 2014 collaborating with Goto City, is intended for research of the preclinical stage of RA, including ACPA/HLA genotype screening and ultrasound and magnetic resonance imaging examinations in high-risk subjects. Using the samples accumulated in this cohort, we have tried to investigate the difference of oral microbiota among RA patients and healthy subjects regarding to ACPA and HLA-DRB1*SE.Methods:Blood and salivary samples were obtained from 1422 subjects out of 4276 who have participated in the Nagasaki Island Study from 2016 to 2018. ACPA positivity was 1.7 % in total. Some of RA patients resided in Goto City participated in the Nagasaki Island Study. At this point, we selected 291 subjects, who were ACPA positive non-RA healthy subjects (n=22) and patients with RA (n=33, 11 subjects were ACPA positive and 22 ACPA negative respectively) as the case, age and gender matched ACPA negative non-RA healthy subjects (n=236) as the control. ACPA was measured by an enzyme-linked immunosorbent assay, and HLA genotyping was quantified by next-generation sequencing (Ref.1). The operational taxonomic unit (OUT) analysis using 16S rRNA gene sequencing were performed. The richness of microbial diversity within-subject (alpha diversity) was scaled via Shannon entropy. The dissimilarity between microbial community composition was calculated using Bray-Curtis distance as a scale, and differences between groups (beta diversity) were tested by permutational multivariate analysis of variance (PERMANOVA). In addition, UniFrac distance calculated in consideration of the distance on the phylogenetic tree were performed.Results:Median age 70 y.o., % Female 58.8 %. Among RA and non-RA subjects, not alpha diversity but beta diversity was statistically significance (p=0.022, small in RA). In RA subjects, both alpha and beta diversity is small (p<0.0001), especially significant in ACPA positive RA (Figure 1). Amongt RA subjects, presence of HLA-DRB1*SE did not show the difference but the tendency of being small of alpha diversity (p=0.29).Conclusion:Our study has suggested for the first time the association of oral microbiota alteration with the presence of ACPA and HLA-DRB1*SE. Oral dysbiosis may reflect the immunological status of patients with RA.References:[1]Kawaguchi S, et al. Methods Mol Biol 2018;1802: 22Disclosure of Interests:None declared

Download Full-text

Utilizing the Microbiota and Machine Learning Algorithms to Assess Risk of Salmonella Contamination in Poultry Rinsate

Journal of Food Protection ◽

10.4315/jfp-20-367 ◽

2021 ◽

Author(s):

Hannah Bolinger ◽

David Tran ◽

Kenneth Harary ◽

George C. Paoli ◽

Giselle Guron ◽

...

Keyword(s):

Machine Learning ◽

Machine Learning Algorithms ◽

Diagnostic Tools ◽

Sequencing Data ◽

Testing Methods ◽

16S Sequencing ◽

Sequencing Technologies ◽

Microbiological Testing ◽

Microbiome Data ◽

Larger Sample

Traditional microbiological testing methods are slow, and many molecular-based techniques rely on culture-based enrichment to overcome low limits of detection. Recent advancements in sequencing technologies may make it possible to utilize machine learning (ML) to identify patterns in microbiome data to potentially predict the presence or absence of pathogens. In this study, 299 poultry rinsate samples from various points in the processing chain were analyzed to determine if microbiota could inform about a sample’s risk for containing Salmonella . Samples were culture confirmed as Salmonella -positive or -negative following modified USDA MLG protocols. The culture confirmation result was used as a reference to compare with 16S sequencing data. Pre-chill samples tested positive (71/82) at a higher frequency than post-chill samples (30/217) and contained greater microbial diversity. Due to their larger sample size, post-chill samples were analyzed more deeply. Analysis of variance (ANOVA) identified a significant effect of chilling on the number of genera (p<0.001), but analysis of similarities (ANOSIM) failed to provide evidence for microbial dissimilarity between pre- and post-chill samples (p=0.001, R=0.443). Various ML models were trained using post-chill samples to predict if a sample contained Salmonella based on the samples’ microbiota pre-enrichment. The optimal model was a Random Forest-based model with a performance as follows: accuracy (88%), sensitivity (85%), specificity (90%). While the algorithms described in this paper are prototypes, these risk-based algorithms demonstrate the potential and need for further studies to provide insight alongside diagnostic tests. Combining risk-based information with diagnostic tools can help poultry processors make informed decisions to help identify and prevent the spread of Salmonella . These data add to the growing body of literature exploring novel ways to utilize microbiome data for predictive food safety.

Download Full-text