Dataset Information

Haplotype assignment of longitudinal viral deep sequencing data using covariation of variant frequencies.

ABSTRACT: Longitudinal deep sequencing of viruses can provide detailed information about intra-host evolutionary dynamics including how viruses interact with and transmit between hosts. Many analyses require haplotype reconstruction, identifying which variants are co-located on the same genomic element. Most current methods to perform this reconstruction are based on a high density of variants and cannot perform this reconstruction for slowly evolving viruses. We present a new approach, HaROLD (HAplotype Reconstruction Of Longitudinal Deep sequencing data), which performs this reconstruction based on identifying co-varying variant frequencies using a probabilistic framework. We illustrate HaROLD on both RNA and DNA viruses with synthetic Illumina paired read data created from mixed human cytomegalovirus (HCMV) and norovirus genomes, and clinical datasets of HCMV and norovirus samples, demonstrating high accuracy, especially when longitudinal samples are available.

SUBMITTER: Venturini C

PROVIDER: S-EPMC9719071 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Haplotype assignment of longitudinal viral deep sequencing data using covariation of variant frequencies.

Venturini Cristina C Pang Juanita J Tamuri Asif U AU Roy Sunando S Atkinson Claire C Griffiths Paul P Breuer Judith J Goldstein Richard A RA

Virus evolution 20221006 2

Longitudinal deep sequencing of viruses can provide detailed information about intra-host evolutionary dynamics including how viruses interact with and transmit between hosts. Many analyses require haplotype reconstruction, identifying which variants are co-located on the same genomic element. Most current methods to perform this reconstruction are based on a high density of variants and cannot perform this reconstruction for slowly evolving viruses. We present a new approach, HaROLD (HAplotype ...[more]

PMID: 36478783

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Project description:BackgroundIn addition to heterogeneity and artificial selection, natural selection is one of the forces used to combat climate change and improve agrobiodiversity in evolutionary plant breeding. Accurate identification of the specific genomic effects of natural selection will likely accelerate transfer between populations. Thus, insights into changes in allele frequency, adequate population size, gene flow and drift are essential. However, observing such effects often involves a trade-off between costs and resolution when a large sample of genotypes for many loci is analysed. Pool genotyping approaches achieve high resolution and precision in estimating allele frequency when sequence coverage is high. Nevertheless, high-coverage pool sequencing of large genomes is expensive.ResultsThree pool samples (n = 300, 300, 288) from a barley backcross population were generated to assess the population's allele frequency. The tested population (BC2F21) has undergone 18 generations of natural adaption to conventional farming practice. The accuracies of estimated pool-based allele frequencies and genome coverage yields were compared using three next-generation sequencing genotyping methods. To achieve accurate allele frequency estimates with low sequence coverage, we employed a haplotyping approach. Low coverage allele frequencies of closely located single polymorphisms were aggregated into a single haplotype allele frequency, yielding 2-to-271-times higher depth and increased precision. When we combined different haplotyping tactics, we found that gene and chip marker-based haplotype analyses performed equivalently or better compared with simple contig haplotype windows. Comparing multiple pool samples and referencing against an individual sequencing approach revealed that whole-genome pool re-sequencing (WGS) achieved the highest correlation with individual genotyping (≥ 0.97). In contrast, transcriptome-based genotyping (MACE) and genotyping by sequencing (GBS) pool replicates were significantly associated with higher error rates and lower correlations, but are still valuable to detect large allele frequency variations.ConclusionsThe proposed strategy identified the allele frequency of populations with high accuracy at low cost. This is particularly relevant to evolutionary plant breeding of crops with very large genomes, such as barley. Whole-genome low coverage re-sequencing at 0.03 × coverage per genotype accurately estimated the allele frequency when a loci-based haplotyping approach was applied. The implementation of annotated haplotypes capitalises on the biological background and statistical robustness.

Dataset Information

Haplotype assignment of longitudinal viral deep sequencing data using covariation of variant frequencies.

Publications

Haplotype assignment of longitudinal viral deep sequencing data using covariation of variant frequencies.

Similar Datasets

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