Fast estimation of genetic relatedness between members of heterogeneous populations of closely related genomic variants

Abstract

BackgroundMany biological analysis tasks require extraction of families of genetically similar sequences from large datasets produced by Next-generation Sequencing (NGS). Such tasks include detection of viral transmissions by analysis of all genetically close pairs of sequences from viral datasets sampled from infected individuals or studying of evolution of viruses or immune repertoires by analysis of network of intra-host viral variants or antibody clonotypes formed by genetically close sequences. The most obvious naïeve algorithms to extract such sequence families are impractical in light of the massive size of modern NGS datasets.ResultsIn this paper, we present fast and scalable k-mer-based framework to perform such sequence similarity queries efficiently, which specifically targets data produced by deep sequencing of heterogeneous populations such as viruses. It shows better filtering quality and time performance when comparing to other tools. The tool is freely available for download at https://github.com/vyacheslav-tsivina/signature-sjConclusionThe proposed tool allows for efficient detection of genetic relatedness between genomic samples produced by deep sequencing of heterogeneous populations. It should be especially useful for analysis of relatedness of genomes of viruses with unevenly distributed variable genomic regions, such as HIV and HCV. For the future we envision, that besides applications in molecular epidemiology the tool can also be adapted to immunosequencing and metagenomics data.

Limited evolution of the yellow fever virus 17d in a mouse infection model

“…Viral diversity was measured by (i) the mutation frequency (mutant clones divided by the total number of clones analyzed) (ii) the Shannon entropy [39][40][41], (iii) the Simpson index of diversity (1-D) [42] and (iv) the Hamming distances [43][44][45]. Shannon entropy of each brain was calculated using the following formula [40]:…”

“…The Hamming distance [43][44][45] ( Figure 3A,B) measures the number of nucleotide substitutions in viral genomes and group variants based on the number of substitutions when compared with the reference sequence (GenBank X03700). Clones with the same number of mutations are grouped together.…”

Epidemiological data analysis of viral quasispecies in the next-generation sequencing era

“…Despite the simplicity of the metric, its calculation is challenging for extremely large NGS datasets, since its naive implementation requires a pairwise comparison of sequences from all pairs of patients. To address this challenge, several filtering techniques have been proposed [149,150]. In consecutive studies [43,44,131,151], more sophisticated distance measures for quasispecies populations have been proposed.…”

“…Both systems can work with haplotypes obtained from NGS data and are scalable for extremely large datasets produced by Illumina MiSeq and other sequencing platforms. In particular, GHOST employs several efficient k-merbased filtering techniques for viral sequence similarity queries, which allow for the elimination of an exhaustive comparison of all pairs of viral haplotypes and allow processing of NGS data from a given HCV outbreak in minutes [150].…”