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Frequency-based haplotype reconstruction from deep sequencing data of bacterial populations.

ABSTRACT: Clonal populations accumulate mutations over time, resulting in different haplotypes. Deep sequencing of such a population in principle provides information to reconstruct these haplotypes and the frequency at which the haplotypes occur. However, this reconstruction is technically not trivial, especially not in clonal systems with a relatively low mutation frequency. The low number of segregating sites in those systems adds ambiguity to the haplotype phasing and thus obviates the reconstruction of genome-wide haplotypes based on sequence overlap information.Therefore, we present EVORhA, a haplotype reconstruction method that complements phasing information in the non-empty read overlap with the frequency estimations of inferred local haplotypes. As was shown with simulated data, as soon as read lengths and/or mutation rates become restrictive for state-of-the-art methods, the use of this additional frequency information allows EVORhA to still reliably reconstruct genome-wide haplotypes. On real data, we show the applicability of the method in reconstructing the population composition of evolved bacterial populations and in decomposing mixed bacterial infections from clinical samples.

SUBMITTER: Pulido-Tamayo S

PROVIDER: S-EPMC4652744 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature

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Frequency-based haplotype reconstruction from deep sequencing data of bacterial populations.

Pulido-Tamayo Sergio S Sánchez-Rodríguez Aminael A Swings Toon T Van den Bergh Bram B Dubey Akanksha A Steenackers Hans H Michiels Jan J Fostier Jan J Marchal Kathleen K

Nucleic acids research 20150518 16

Clonal populations accumulate mutations over time, resulting in different haplotypes. Deep sequencing of such a population in principle provides information to reconstruct these haplotypes and the frequency at which the haplotypes occur. However, this reconstruction is technically not trivial, especially not in clonal systems with a relatively low mutation frequency. The low number of segregating sites in those systems adds ambiguity to the haplotype phasing and thus obviates the reconstruction ...[more]

PMID: 25990729

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Project description:BackgroundShort read sequencing has been used extensively to decipher the genome diversity of human cytomegalovirus (HCMV) strains, but falls short to reveal individual genomes in mixed HCMV strain populations. Novel third-generation sequencing platforms offer an extended read length and promise to resolve how distant polymorphic sites along individual genomes are linked. In the present study, we established a long amplicon PacBio sequencing workflow to identify the absolute and relative quantities of unique HCMV haplotypes spanning over multiple hypervariable sites in mixtures. Initial validation of this approach was performed with defined HCMV DNA templates derived from cell-culture enriched viruses and was further tested for its suitability on patient samples carrying mixed HCMV infections.ResultsTotal substitution and indel error rate of mapped reads ranged from 0.17 to 0.43% depending on the stringency of quality trimming. Artificial HCMV DNA mixtures were correctly determined down to 1% abundance of the minor DNA source when the total HCMV DNA input was 4 × 104 copies/ml. PCR products of up to 7.7 kb and a GC content < 55% were efficiently generated when DNA was directly isolated from patient samples. In a single sample, up to three distinct haplotypes were identified showing varying relative frequencies. Alignments of distinct haplotype sequences within patient samples showed uneven distribution of sequence diversity, interspersed by long identical stretches. Moreover, diversity estimation at single polymorphic regions as assessed by short amplicon sequencing may markedly underestimate the overall diversity of mixed haplotype populations.ConclusionsQuantitative haplotype determination by long amplicon sequencing provides a novel approach for HCMV strain characterisation in mixed infected samples which can be scaled up to cover the majority of the genome by multi-amplicon panels. This will substantially improve our understanding of intra-host HCMV strain diversity and its dynamic behaviour.

Project description:BackgroundDeep-sequencing allows for an in-depth characterization of sequence variation in complex populations. However, technology associated errors may impede a powerful assessment of low-frequency mutations. Fortunately, base calls are complemented with quality scores which are derived from a quadruplet of intensities, one channel for each nucleotide type for Illumina sequencing. The highest intensity of the four channels determines the base that is called. Mismatch bases can often be corrected by the second best base, i.e. the base with the second highest intensity in the quadruplet. A virus variant model-based clustering method, ViVaMBC, is presented that explores quality scores and second best base calls for identifying and quantifying viral variants. ViVaMBC is optimized to call variants at the codon level (nucleotide triplets) which enables immediate biological interpretation of the variants with respect to their antiviral drug responses.ResultsUsing mixtures of HCV plasmids we show that our method accurately estimates frequencies down to 0.5%. The estimates are unbiased when average coverages of 25,000 are reached. A comparison with the SNP-callers V-Phaser2, ShoRAH, and LoFreq shows that ViVaMBC has a superb sensitivity and specificity for variants with frequencies above 0.4%. Unlike the competitors, ViVaMBC reports a higher number of false-positive findings with frequencies below 0.4% which might partially originate from picking up artificial variants introduced by errors in the sample and library preparation step.ConclusionsViVaMBC is the first method to call viral variants directly at the codon level. The strength of the approach lies in modeling the error probabilities based on the quality scores. Although the use of second best base calls appeared very promising in our data exploration phase, their utility was limited. They provided a slight increase in sensitivity, which however does not warrant the additional computational cost of running the offline base caller. Apparently a lot of information is already contained in the quality scores enabling the model based clustering procedure to adjust the majority of the sequencing errors. Overall the sensitivity of ViVaMBC is such that technical constraints like PCR errors start to form the bottleneck for low frequency variant detection.

Dataset Information

Frequency-based haplotype reconstruction from deep sequencing data of bacterial populations.

Publications

Frequency-based haplotype reconstruction from deep sequencing data of bacterial populations.

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