Computational framework for next-generation sequencing of heterogeneous viral populations using combinatorial pooling

Incorporating and addressing testing bias within estimates of epidemic dynamics for SARS-CoV-2

BACKGROUND

The disease burden of SARS-CoV-2 as measured by tests from various localities, and at different time points present varying estimates of infection and fatality rates. Models based on these acquired data may suffer from systematic errors and large estimation variances due to the biases associated with testing. An unbiased randomized testing to estimate the true fatality rate is still missing.

METHODS

Here, we characterize the effect of incidental sampling bias in the estimation of epidemic dynamics. Towards this, we explicitly modeled for sampling bias in an augmented compartment model to predict epidemic dynamics. We further calculate the bias from differences in disease prediction from biased, and randomized sampling, proposing a strategy to obtain unbiased estimates.

RESULTS

Our simulations demonstrate that sampling biases in favor of patients with higher disease manifestation could significantly affect direct estimates of infection and fatality rates calculated from the numbers of confirmed cases and deaths, and serological testing can partially mitigate these biased estimates.

CONCLUSIONS

The augmented compartmental model allows the explicit modeling of different testing policies and their effects on disease estimates. Our calculations for the dependence of expected confidence on a randomized sample sizes, show that relatively small sample sizes can provide statistically significant estimates for SARS-CoV-2 related death rates.

High-throughput sequencing (HTS) for the analysis of viral populations

The development of High-Throughput Sequencing (HTS) technologies is having a major impact on the genomic analysis of viral populations. Current HTS platforms can capture nucleic acid variation across millions of genes for both selected amplicons and full viral genomes. HTS has already facilitated the discovery of new viruses, hinted new taxonomic classifications and provided a deeper and broader understanding of their diversity, population and genetic structure. Hence, HTS has already replaced standard Sanger sequencing in basic and applied research fields, but the next step is its implementation as a routine technology for the analysis of viruses in clinical settings. The most likely application of this implementation will be the analysis of viral genomics, because the huge population sizes, high mutation rates and very fast replacement of viral populations have demonstrated the limited information obtained with Sanger technology. In this review, we describe new technologies and provide guidelines for the high-throughput sequencing and genetic and evolutionary analyses of viral populations and metaviromes, including software applications. With the development of new HTS technologies, new and refurbished molecular and bioinformatic tools are also constantly being developed to process and integrate HTS data. These allow assembling viral genomes and inferring viral population diversity and dynamics. Finally, we also present several applications of these approaches to the analysis of viral clinical samples including transmission clusters and outbreak characterization.

s-dePooler: determination of polymorphism carriers from overlapping DNA pools

Background

Samples pooling is a method widely used in studies to reduce costs and labour. DNA sample pooling combined with massive parallel sequencing is a powerful tool for discovering DNA variants (polymorphisms) in large analysing populations, which is the base of such research fields as Genome-Wide Association Studies, evolutionary and population studies, etc. Usage of overlapping pools where each sample is present in multiple pools can enhance the accuracy of polymorphism detection and allow identifying carriers of rare-variants. Surprisingly there is a lack of tools for result interpretation and carrier identification, i.e. for “depooling”.

Results

Here we present s-dePooler, the application for analysis of pooling experiments data. s-dePooler uses the variants information (VCF-file) and the pooling scheme to produce a list of candidate carriers for each polymorphism. We incorporated s-dePooler into a pipeline (dePoP) for automation of pooling analysis. The performance of the pipeline was tested on a synthetic dataset built using the 1000 Genomes Project data, resulting in the successful identification 97% of carriers of polymorphisms present in fewer than ~ 10% of carriers.

Conclusions

s-dePooler along with dePoP can be used to identify carriers of polymorphisms in overlapping pools, and is compatible with any pooling scheme with equivalent molar ratios of pooled samples. s-dePooler and dePoP with usage instructions and test data are freely available at https://github.com/lab9arriam/depop.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2616-9) contains supplementary material, which is available to authorized users.

PVsiRNAdb: a database for plant exclusive virus-derived small interfering RNAs

Ribonucleic acids (RNA) interference mechanism has been proved to be an important regulator of both transcriptional and post-transcription controls of gene expression during biotic and abiotic stresses in plants. Virus-derived small interfering RNAs (vsiRNAs) are established components of the RNA silencing mechanism for incurring anti-viral resistance in plants. Some databases like siRNAdb, HIVsirDB and VIRsiRNAdb are available online pertaining to siRNAs as well as vsiRNAs generated during viral infection in humans; however, currently there is a lack of repository for plant exclusive vsiRNAs. We have developed `PVsiRNAdb (http://www.nipgr.res.in/PVsiRNAdb)', a manually curated plant-exclusive database harboring information related to vsiRNAs found in different virus-infected plants collected by exhaustive data mining of published literature so far. This database contains a total of 322 214 entries and 282 549 unique sequences of vsiRNAs. In PVsiRNAdb, detailed and comprehensive information is available for each vsiRNA sequence. Apart from the core information consisting of plant, tissue, virus name and vsiRNA sequence, additional information of each vsiRNAs (map position, length, coordinates, strand information and predicted structure) may be of high utility to the user. Different types of search and browse modules with three different tools namely BLAST, Smith-Waterman Align and Mapping are provided at PVsiRNAdb. Thus, this database being one of its kind will surely be of much use to molecular biologists for exploring the complex viral genetics and genomics, viral-host interactions and beneficial to the scientific community and can prove to be very advantageous in the field of agriculture for producing viral resistance transgenic crops.

Inference of genetic relatedness between viral quasispecies from sequencing data

BACKGROUND

RNA viruses such as HCV and HIV mutate at extremely high rates, and as a result, they exist in infected hosts as populations of genetically related variants. Recent advances in sequencing technologies make possible to identify such populations at great depth. In particular, these technologies provide new opportunities for inference of relatedness between viral samples, identification of transmission clusters and sources of infection, which are crucial tasks for viral outbreaks investigations.

RESULTS

We present (i) an evolutionary simulation algorithm Viral Outbreak InferenCE (VOICE) inferring genetic relatedness, (ii) an algorithm MinDistB detecting possible transmission using minimal distances between intra-host viral populations and sizes of their relative borders, and (iii) a non-parametric recursive clustering algorithm Relatedness Depth (ReD) analyzing clusters' structure to infer possible transmissions and their directions. All proposed algorithms were validated using real sequencing data from HCV outbreaks.

CONCLUSIONS

All algorithms are applicable to the analysis of outbreaks of highly heterogeneous RNA viruses. Our experimental validation shows that they can successfully identify genetic relatedness between viral populations, as well as infer transmission clusters and outbreak sources.

Long Single-Molecule Reads Can Resolve the Complexity of the Influenza Virus Composed of Rare, Closely Related Mutant Variants

As a result of a high rate of mutations and recombination events, an RNA-virus exists as a heterogeneous "swarm" of mutant variants. The long read length offered by single-molecule sequencing technologies allows each mutant variant to be sequenced in a single pass. However, high error rate limits the ability to reconstruct heterogeneous viral population composed of rare, related mutant variants. In this article, we present two single-nucleotide variants (2SNV), a method able to tolerate the high error rate of the single-molecule protocol and reconstruct mutant variants. 2SNV uses linkage between single-nucleotide variations to efficiently distinguish them from read errors. To benchmark the sensitivity of 2SNV, we performed a single-molecule sequencing experiment on a sample containing a titrated level of known viral mutant variants. Our method is able to accurately reconstruct clone with frequency of 0.2% and distinguish clones that differed in only two nucleotides distantly located on the genome. 2SNV outperforms existing methods for full-length viral mutant reconstruction.

Biogenesis, Function, and Applications of Virus-Derived Small RNAs in Plants

RNA silencing, an evolutionarily conserved and sequence-specific gene-inactivation system, has a pivotal role in antiviral defense in most eukaryotic organisms. In plants, a class of exogenous small RNAs (sRNAs) originating from the infecting virus called virus-derived small interfering RNAs (vsiRNAs) are predominantly responsible for RNA silencing-mediated antiviral immunity. Nowadays, the process of vsiRNA formation and the role of vsiRNAs in plant viral defense have been revealed through deep sequencing of sRNAs and diverse genetic analysis. The biogenesis of vsiRNAs is analogous to that of endogenous sRNAs, which require diverse essential components including dicer-like (DCL), argonaute (AGO), and RNA-dependent RNA polymerase (RDR) proteins. vsiRNAs trigger antiviral defense through post-transcriptional gene silencing (PTGS) or transcriptional gene silencing (TGS) of viral RNA, and they hijack the host RNA silencing system to target complementary host transcripts. Additionally, several applications that take advantage of the current knowledge of vsiRNAs research are being used, such as breeding antiviral plants through genetic engineering technology, reconstructing of viral genomes, and surveying viral ecology and populations. Here, we will provide an overview of vsiRNA pathways, with a primary focus on the advances in vsiRNA biogenesis and function, and discuss their potential applications as well as the future challenges in vsiRNAs research.

Mutational bias of Turnip Yellow Mosaic Virus in the context of host anti-viral gene silencing

Plant Dicer-like (DCL) enzymes exhibit a GC-preference during anti-viral post-transcriptional gene silencing (PTGS), delivering an evolutionary selection pressure resulting in plant viruses with GC-poor genomes. However, some viruses, e.g. Turnip Yellow Mosaic Virus (TYMV, genus Tymovirus) have GC-rich genomes, raising the question as to whether or not DCL derived selection pressure affects these viruses. In this study we analyzed the virus-derived small interfering RNAs from TYMV-infected leaves of Brassica juncea showed that the TYMV population accumulated a mutational bias with AU replacing GC (GC-AU), demonstrating PTGS pressure. Interestingly, at the highly polymorphic sites the GC-AU bias was no longer observed. This suggests the presence of an unknown mechanism preventing mutational drift of the viral population and maintaining viral genome stability, despite the host PTGS pressure.