Next-generation sequencing in clinical virology: Discovery of new viruses

Table 1 A comparative evaluation of the different virus discovery approaches showing advantages and disadvantages associated with them

	Classical approaches(Cell culture and infection based)	Nucleic acid sequence-dependent amplification approaches	Nucleic acid sequence-independent amplification approaches	Next-generation sequencers-based metagenomic approaches
Requirement of cell culture systems	Yes, required for virus particle enrichment	Not required	Not required	Not required
Information about the cytopathic effects of the virus	Yes, could be achieved through cell changes	No information could be achieved	No information could be achieved	No information could be achieved
Requirement of special equipments for purification	Yes, Ultracentrifuge/high speed centrifuges, density gradient is required for preparing pure virus	Not necessary, semi pure preparations obtained through low speed centrifuges are suitable	Not necessary, semi pure preparations obtained through low speed centrifuges are suitable	Not necessary, semi pure preparations obtained through low speed centrifuges are suitable
Information about detailed morphological/structural features of the virus	Yes, could be achieved through Electron/Atomic Force microscopy	No information on virus morphology/structure could be achieved directly	No information on virus morphology/structure could be achieved directly	No information on virus morphology/structure could be achieved directly
Time required for virus identification	Long time is required for identification, ranging from days to weeks	Comparatively faster, days required if cloning and sequencing is involved. Faster with microarray based approaches	Comparatively faster, virus could be identified within few days	Fastest available approach, identification could be done within days and even some times within hours
Requirement of prior knowledge about the virus	Not required	Some information is required regarding genus/family to design primers/probes	Being sequence independent technique, no information is required	Being sequence independent technique, no information is required
Dynamic detection range	Very narrow	Narrow	Wide	Extremely wide
Tolerance to non-viral materials	Vulnerable to other pathogens capable of infecting cell	Being sequence dependent, less vulnerable to other sequences from host and other pathogens	Being sequence in-dependent, more vulnerable to other sequences from host and other pathogens. Virus enrichment techniques required before analysis	Being sequence in-dependent, more vulnerable to other sequences from host and other pathogens. Virus enrichment techniques required before analysis
Suitability for discovery of new viruses	Yes	Less suitable, good at discovery of genotypes/variants of known viruses	Yes	Yes
Suitability during outbreaks	Not suitable due to requirement of long time	Not suitable due to requirement of prior sequence information	Yes, but still considerable time is required during outbreaks	Being fast, very much suitable in detecting pathogens in an outbreak scenario

Table 2 Important bioinformatics challenges associated with application of next-generation sequencers in viral diagnostics action taken or proposed to overcome challenges

Bioinformatics challenges associated with application of NGS in viral diagnostics	Action taken or proposed to overcome challenges
Generation of huge volumes of data by NGS platforms-“data deluge”	Advancement in storage and computation facilities, availability of computer with greater storage and highly powerful processors, cluster/grid computing and cloud computing. Computation facilities needs to be updated with emergence of newer platforms delivering larger datasets
Challenges in uploading data for submission to databases and supercomputing servers for analysis	Requirement of uninterrupted and extremely fast networks
Challenges in storage, public archival and ease of access	Creation of specialized data archive such as the Sequence Read Archive by NIH and ENA (European nucleotide Archive) by EBI. Sharing of data within the three major databases (NIH, EBI and DDBJ) for public accessibility
Challenges in analysis and visualization of large volumes of data, beyond the scope of computation facilities available in molecular biology laboratories	Creation of metagenomic or NGS data analysis pipelines and integrated tool kits, such as those available at NIH-NCBI, EMBL-EBI, MGRAST, CASAVA, MetaVir, Megan, UCSC Genome Browser, BioLinux, etc., availability of cloud computing based servers such as Galaxy
Challenges in alignment, de novo assembly, gene prediction and phylogenetic analyses NGS datasets, especially short read datasets	Availability of alignment algorithms/programs such as ABySS, ELAND, SOAP, Bowtie, Cloudburst, Zoom, BWA, SHRiMP, MOM, SeqMap, Metagene, Velvet, QSRA, ALLPATHS, EDENA, VCAKE, FragGeneScan, BLAST, GLIMMER, EULER-SR, Avadis, Eagle View, etc.
Interpretation of huge amount of data generated in metagenomic analyses by NGS platforms	Proper interpretation of analyzed data is of utmost importance to identify newer pathogens as well as their clinical significance

NGS: Next-generation sequencers.