Parallel-META 2.0: Enhanced Metagenomic Data Analysis with Functional Annotation, High Performance Computing and Advanced Visualization

Su, Xiaoquan; Pan, Weihua; Song, Baoxing; Xu, Jian; Ning, Kang

doi:10.1371/journal.pone.0089323

Cited by 68 publications

(42 citation statements)

References 36 publications

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“…To generate initial microbial community taxonomic profiles of the metagenomic samples, we used both a marker gene-based identification strategy using MetaPhlAn version 2.0 (40) and a 16S rRNA-based identification using Parallel-META (41). Metagenomic reads encoding 16S rRNA gene fragments were clustered into operational taxonomic units (OTUs) with QIIME (42), using the Uclust algorithm (43), and taxonomically assigned using the RDP Classifier (44).…”

Section: Methodsmentioning

confidence: 99%

Metagenomics of Two Severe Foodborne Outbreaks Provides Diagnostic Signatures and Signs of Coinfection Not Attainable by Traditional Methods

Huang

Luo

Pena‐Gonzalez

et al. 2017

Appl Environ Microbiol

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Diagnostic testing for foodborne pathogens relies on culture-based techniques that are not rapid enough for real-time disease surveillance and do not give a quantitative picture of pathogen abundance or the response of the natural microbiome. Powerful sequence-based culture-independent approaches, such as shotgun metagenomics, could sidestep these limitations and potentially reveal a pathogenspecific signature on the microbiome that would have implications not only for diagnostics but also for better understanding disease progression and pathogen ecology. However, metagenomics have not yet been validated for foodborne pathogen detection. Toward closing these gaps, we applied shotgun metagenomics to stool samples collected from two geographically isolated (Alabama and Colorado) foodborne outbreaks, where the etiologic agents were identified by culture-dependent methods as distinct strains of Salmonella enterica subsp. enterica serovar Heidelberg. Metagenomic investigations were consistent with the culture-based findings and revealed, in addition, the in situ abundance and level of intrapopulation diversity of the pathogen, the possibility of coinfections with Staphylococcus aureus, overgrowth of commensal Escherichia coli, and significant shifts in the gut microbiome during infection relative to reference healthy samples. Additionally, we designed our bioinformatics pipeline to deal with several challenges associated with the analysis of clinical samples, such as the high frequency of coeluting human DNA sequences and assessment of the virulence potential of pathogens. Comparisons of these results to those of other studies revealed that in several, but not all, cases of diarrheal outbreaks, the disease and healthy states of the gut microbial community might be distinguishable, opening new possibilities for diagnostics.IMPORTANCE Diagnostic testing for enteric pathogens has relied for decades on culture-based techniques, but a total of 38.4 million cases of foodborne illness per year cannot be attributed to specific causes. This study describes new cultureindependent metagenomic approaches and the associated bioinformatics pipeline to detect and type the causative agents of microbial disease with unprecedented accuracy, opening new possibilities for the future development of health technologies and diagnostics. Our tools and approaches should be applicable to other microbial diseases in addition to foodborne diarrhea.KEYWORDS Salmonella, diagnostics, diarrhea, human gut, metagenomics D iagnostic testing for enteric pathogens has relied for decades on culture-based techniques, and culture-derived isolates currently form the foundation for public health surveillance. However, the clinical landscape is rapidly changing as culture-

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Section: Methodsmentioning

confidence: 99%

Metagenomics of Two Severe Foodborne Outbreaks Provides Diagnostic Signatures and Signs of Coinfection Not Attainable by Traditional Methods

Huang

Luo

Pena‐Gonzalez

et al. 2017

Appl Environ Microbiol

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“…The sample taxonomic profile was also determined directly from the metagenome dataset using two online software, i.e. Parallel-META31 and MetaPhlAn32, following the instructions of the two software developers.…”

Section: Methodsmentioning

confidence: 99%

Differential fecal microbiota are retained in broiler chicken lines divergently selected for fatness traits

Hou

Kwok

Zheng

et al. 2016

Sci Rep

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Our study combined 16S rRNA-pyrosequencing and whole genome sequencing to analyze the fecal metagenomes of the divergently selected lean (LL) and fat (FL) line chickens. Significant structural differences existed in both the phylogenic and functional metagenomes between the two chicken lines. At phylum level, the FL group had significantly less Bacteroidetes. At genus level, fourteen genera of different relative abundance were identified, with some known short-chain fatty acid producers (including Subdoligranulum, Butyricicoccus, Eubacterium, Bacteroides, Blautia) and a potentially pathogenic genus (Enterococcus). Redundancy analysis identified 190 key responsive operational taxonomic units (OTUs) that accounted for the structural differences between the phylogenic metagenome of the two groups. Four Cluster of Orthologous Group (COG) categories (Amino acid transport and metabolism, E; Nucleotide transport and metabolism, F; Coenzyme transport and metabolism, H; and Lipid transport and metabolism, I) were overrepresented in LL samples. Fifteen differential metabolic pathways (Biosynthesis of amino acids, Pyruvate metabolism, Nitrotoluene degradation, Lipopolysaccharide biosynthesis, Peptidoglycan biosynthesis, Pantothenate and CoA biosynthesis, Glycosaminoglycan degradation, Thiamine metabolism, Phosphotransferase system, Two-component system, Bacterial secretion system, Flagellar assembly, Bacterial chemotaxis, Ribosome, Sulfur relay system) were identified. Our data highlighted interesting variations between the gut metagenomes of these two chicken lines.

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“…Progress in eDNA-based functional genomics will likely follow two complementary paths: (1) using a priori knowledge of functional genomic loci to target specific DNA or RNA fragments in environmental samples (Jaenicke-Després et al 2003) and (2) using shotgun sequencing of total eDNA or environmental RNA (eRNA) with bioinformatic assignment to taxa, genomes, and functions (Su et al 2014). The first path is immediately accessible to studies using genomic model organisms and sampling designs with predictable and robust differences in genome function (e.g., Robinson et al 2012).…”

Section: Functional Genetics and Genomicsmentioning

confidence: 99%

The ecology of environmental DNA and implications for conservation genetics

2015

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Environmental DNA (eDNA) refers to the genetic material that can be extracted from bulk environmental samples such as soil, water, and even air. The rapidly expanding study of eDNA has generated unprecedented ability to detect species and conduct genetic analyses for conservation, management, and research, particularly in scenarios where collection of whole organisms is impractical or impossible. While the number of studies demonstrating successful eDNA detection has increased rapidly in recent years, less research has explored the ''ecology'' of eDNA-myriad interactions between extraorganismal genetic material and its environment-and its influence on eDNA detection, quantification, analysis, and application to conservation and research. Here, we outline a framework for understanding the ecology of eDNA, including the origin, state, transport, and fate of extraorganismal genetic material. Using this framework, we review and synthesize the findings of eDNA studies from diverse environments, taxa, and fields of study to highlight important concepts and knowledge gaps in eDNA study and application. Additionally, we identify frontiers of conservation-focused eDNA application where we see the most potential for growth, including the use of eDNA for estimating population size, population genetic and genomic analyses via eDNA, inclusion of other indicator biomolecules such as environmental RNA or proteins, automated sample collection and analysis, and consideration of an expanded array of creative environmental samples. We discuss how a more complete understanding of the ecology of eDNA is integral to advancing these frontiers and maximizing the potential of future eDNA applications in conservation and research.

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Parallel-META 2.0: Enhanced Metagenomic Data Analysis with Functional Annotation, High Performance Computing and Advanced Visualization

Cited by 68 publications

References 36 publications

Metagenomics of Two Severe Foodborne Outbreaks Provides Diagnostic Signatures and Signs of Coinfection Not Attainable by Traditional Methods

Metagenomics of Two Severe Foodborne Outbreaks Provides Diagnostic Signatures and Signs of Coinfection Not Attainable by Traditional Methods

Differential fecal microbiota are retained in broiler chicken lines divergently selected for fatness traits

The ecology of environmental DNA and implications for conservation genetics

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