Choice of assembly software has a critical impact on virome characterisation

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.Electronic supplementary materialThe online version of this article (10.1186/s40168-019-0626-5) contains supplementary material, which is available to authorized users.

Coming-of-Age Characterization of Soil Viruses: A User’s Guide to Virus Isolation, Detection within Metagenomes, and Viromics

“…Sequence analysis: Virus detection within metagenomes is further hampered by the often vast diversity of viruses present, which can make de novo assembly of viral contiguous sequences (contigs) challenging [131], that is, assembly without employing already sequenced viral genomes as templates. Specifically, the main and best assembly algorithms are based on overlapping stretches of sequenced nucleotides (i.e., De Bruijn graph assembly [124,131]), and overlapping stretches become rarer the lower the number of copies of specific viral DNA sequences that is originally present in a sample. Indeed, less than 2% of assembled sequences are typically of virus origin [34,132].…”

Microbial and Viral Communities and Their Antibiotic Resistance Genes Throughout a Hospital Wastewater Treatment System

“…Our study only targeted dsDNA viral sequences, which further limits the scope of diversity that can be characterized. Viral reads can be difficult to assemble, as viral genomes contain repeat regions and exhibit substantial intrapopulation diversity and variation (Smits et al, 2014;Rose et al, 2016;Sutton et al, 2019). Uneven coverage of sequences can pose an additional assembly challenge (Smits et al, 2014;Rose et al, 2016).…”

Highly matching Coronavirus-like short sequences can be retrieved from environmental metagenomes

“…Additionally, 33.5% (A53) and 42.8% (A102) of SARS-CoV-2 test-sequence reads had reported hits in the nucleotide collection (nt) database while the remaining reads were unknown. Because of the low proportion of viral sequences in the datasets, which is not surprising for metagenomic datasets, it was not possible to assemble longer sequences for more reliable database alignments as previously recommended (9). Nevertheless, we also examined the anking regions on the short reads identi ed as SARS-CoV-2 by the Kaiju-MEM approach hit by the SARS-CoV-2 test-sequences and found that they could not be aligned further to SARS-CoV-2 genomes available at GISAID (17).…”

“…This is particularly relevant for natural ecosystems, containing complex microbiomes, where viruses can be abundant and diverse (8). Foregoing approaches have demonstrated the importance of assembly strategies for short reads to facilitate reliable virome analyses (9). In the context of the current pandemic, detection of Coronavirus-like sequences, not necessarily belonging to SARS-CoV-2, may compromise the study of the natural distribution of this virus, a feature with relevance for understanding the viral dissemination from a One-Health approach.…”