Choice of assembly software has a critical impact on virome characterisation View Full Text


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Article Info

DATE

2019-01-28

AUTHORS

Thomas D. S. Sutton, Adam G. Clooney, Feargal J. Ryan, R. Paul Ross, Colin Hill

ABSTRACT

BackgroundThe viral component of microbial communities plays a vital role in driving bacterial diversity, facilitating nutrient turnover and shaping community composition. Despite their importance, the vast majority of viral sequences are poorly annotated and share little or no homology to reference databases. As a result, investigation of the viral metagenome (virome) relies heavily on de novo assembly of short sequencing reads to recover compositional and functional information. Metagenomic assembly is particularly challenging for virome data, often resulting in fragmented assemblies and poor recovery of viral community members. Despite the essential role of assembly in virome analysis and difficulties posed by these data, current assembly comparisons have been limited to subsections of virome studies or bacterial datasets.DesignThis study presents the most comprehensive virome assembly comparison to date, featuring 16 metagenomic assembly approaches which have featured in human virome studies. Assemblers were assessed using four independent virome datasets, namely, simulated reads, two mock communities, viromes spiked with a known phage and human gut viromes.ResultsAssembly performance varied significantly across all test datasets, with SPAdes (meta) performing consistently well. Performance of MIRA and VICUNA varied, highlighting the importance of using a range of datasets when comparing assembly programs. It was also found that while some assemblers addressed the challenges of virome data better than others, all assemblers had limitations. Low read coverage and genomic repeats resulted in assemblies with poor genome recovery, high degrees of fragmentation and low-accuracy contigs across all assemblers. These limitations must be considered when setting thresholds for downstream analysis and when drawing conclusions from virome data. More... »

PAGES

12

References to SciGraph publications

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  • Identifiers

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    88 members
    89 metagenomes
    90 metagenomic assembly
    91 metagenomic assembly approaches
    92 microbial communities
    93 mock communities
    94 novo assembly
    95 nutrient turnover
    96 performance
    97 phages
    98 poor recovery
    99 program
    100 range
    101 range of datasets
    102 reads
    103 recovery
    104 reference database
    105 repeats
    106 results
    107 role
    108 sequence
    109 sequencing reads
    110 short sequencing reads
    111 simulated reads
    112 software
    113 study
    114 subsections
    115 test dataset
    116 turnover
    117 vast majority
    118 vicuna
    119 viral components
    120 viral metagenomes
    121 viral sequences
    122 virome
    123 virome analysis
    124 virome data
    125 virome studies
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