Assessing Species Diversity Using Metavirome Data: Methods and Challenges

Herath, Damayanthi; Jayasundara, Duleepa; Ackland, David C.; Saeed, Isaam; Tang, Sen‐Lin; Halgamuge, Saman K.

doi:10.1016/j.csbj.2017.09.001

Cited by 14 publications

(11 citation statements)

References 89 publications

(144 reference statements)

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“…A sequence similarity-dependent approach for detecting microbes, such as this, contributes to the rapid detection of known microbes while also allowing for the quantification of biodiversity which similarity-independent approaches lack [ 38 ]. For each individual sample, the reads that mapped to each microbe were normalized based on the genome length of both microbe and reference per 100,000 host cells using the following equation [ 39 ]:

…”

Section: Methodsmentioning

confidence: 99%

Metagenomic Analysis of the Respiratory Microbiome of a Broiler Flock from Hatching to Processing

et al. 2021

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Elucidating the complex microbial interactions in biological environments requires the identification and characterization of not only the bacterial component but also the eukaryotic viruses, bacteriophage, and fungi. In a proof of concept experiment, next generation sequencing approaches, accompanied by the development of novel computational and bioinformatics tools, were utilized to examine the evolution of the microbial ecology of the avian trachea during the growth of a healthy commercial broiler flock. The flock was sampled weekly, beginning at placement and concluding at 49 days, the day before processing. Metagenomic sequencing of DNA and RNA was utilized to examine the bacteria, virus, bacteriophage, and fungal components during flock growth. The utility of using a metagenomic approach to study the avian respiratory virome was confirmed by detecting the dysbiosis in the avian respiratory virome of broiler chickens diagnosed with infection with infectious laryngotracheitis virus. This study provides the first comprehensive analysis of the ecology of the avian respiratory microbiome and demonstrates the feasibility for the use of this approach in future investigations of avian respiratory diseases.

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Section: Methodsmentioning

confidence: 99%

Metagenomic Analysis of the Respiratory Microbiome of a Broiler Flock from Hatching to Processing

et al. 2021

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“…The large number of contigs generated due to VI may be overwhelming for most researchers, and for viral ecology studies, could lead to over-estimation of species richness for methods that use contig spectra to infer richness, such as PHACCS or CatchAll. 24, 25, 26 This phenomenon may also impact studies differently depending on the overall goal for generating viral sequence data. For example, some researchers may only be concerned with generating a single major consensus genome, even when variants are detected in the data.…”

Section: Discussionmentioning

confidence: 99%

The effect of variant interference on de novo assembly for viral deep sequencing

Castro

Marine

Ramos

et al. 2019

Preprint

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Viruses have high mutation rates and generally exist as a mixture of variants in biological samples. Next-generation sequencing (NGS) approach has surpassed Sanger for generating long viral sequences, yet how variants affect NGS de novo assembly remains largely unexplored. Our results from >15,000 simulated experiments showed that presence of variants can turn an assembly of one genome into tens to thousands of contigs. This “variant interference” (VI) is highly consistent and reproducible by ten most used de novo assemblers, and occurs independent of genome length, read length, and GC content. The main driver of VI is pairwise identities between viral variants. These findings were further supported by in silico simulations, where selective removal of minor variant reads from clinical datasets allow the “rescue” of full viral genomes from fragmented contigs. These results call for careful interpretation of contigs and contig numbers from de novo assembly in viral deep sequencing.

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“…A sequence similarity-dependent approach for detecting viruses contributes to the rapid detection of known viruses while also allowing for the quantification of biodiversity, which similarity-independent approaches lack (Herath et al ., 2017). This approach can be applied to bacteria, fungi and bacteriophage as well.…”

Section: Methodsmentioning

confidence: 99%

BiomeSeq: A Tool for the Characterization of Animal Microbiomes from Metagenomic Data

Mulholland¹,

Keeler²

2019

Preprint

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18The complete characterization of a microbiome is critical in elucidating the complex ecology of 19 the microbial composition within healthy and diseased animals. Many microbiome studies 20 characterize only the bacterial component, for which there are several well-developed 21 sequencing methods, bioinformatics tools and databases available. The lack of comprehensive 22 bioinformatics workflows and databases have limited efforts to characterize the other 23 components existing in a microbiome. BiomeSeq is a tool for the analysis of the complete animal 24 microbiome using metagenomic sequencing data. With its comprehensive workflow, 25 customizable parameters and microbial databases, BiomeSeq can rapidly quantify the viral, 26 fungal, bacteriophage and bacterial components of a sample and produce informative tables for 27 analysis. BiomeSeq was employed in detecting and quantifying the respiratory microbiome of a 28 commercial poultry broiler flock throughout its grow-out cycle from hatching to processing. It 29 successfully processed 780 million reads, of which 5,163 aligned to avian DNA viral genomes, 30 71,936 aligned to avian RNA viral genomes, 469,937 aligned to bacterial genomes, 504,682 31 aligned to bacteriophage genomes and 1,964 aligned to fungal genomes. For each microbial 32 species detected, BiomeSeq calculated the normalized abundance, percent relative abundance, 33 and coverage as well as the diversity for each sample. BiomeSeq provides for the detection and 34 quantification of the microbiome from next-generation metagenomic sequencing data. This tool 35 is implemented into a user-friendly container that requires one command and generates a table 36 consisting of taxonomical information for each microbe detected as well as normalized 37 abundance, percent relative abundance, coverage and diversity calculations.38 3

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Assessing Species Diversity Using Metavirome Data: Methods and Challenges

Cited by 14 publications

References 89 publications

Metagenomic Analysis of the Respiratory Microbiome of a Broiler Flock from Hatching to Processing

Metagenomic Analysis of the Respiratory Microbiome of a Broiler Flock from Hatching to Processing

The effect of variant interference on de novo assembly for viral deep sequencing

BiomeSeq: A Tool for the Characterization of Animal Microbiomes from Metagenomic Data

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