Estimation of Errors in “Raw” DNA Sequences: A Validation Study

Richterich, Peter

doi:10.1101/gr.8.3.251

Cited by 115 publications

(75 citation statements)

References 6 publications

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“…Assembly certification. Although 'quality scores' have been developed to indicate the nucleotide accuracy of a draft genome sequence 36 , no analogous measures have been developed to reflect the long-range assembly accuracy. We therefore sought to develop such a measure on the basis of two types of internal inconsistencies (see Supplementary Information).…”

Section: Generating a Draft Genome Sequencementioning

confidence: 99%

Genome sequence, comparative analysis and haplotype structure of the domestic dog

Lindblad‐Toh¹,

Wade²,

Mikkelsen³

et al. 2005

Nature

2,234

2,079

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Here we report a high-quality draft genome sequence of the domestic dog (Canis familiaris), together with a dense map of single nucleotide polymorphisms (SNPs) across breeds. The dog is of particular interest because it provides important evolutionary information and because existing breeds show great phenotypic diversity for morphological, physiological and behavioural traits. We use sequence comparison with the primate and rodent lineages to shed light on the structure and evolution of genomes and genes. Notably, the majority of the most highly conserved non-coding sequences in mammalian genomes are clustered near a small subset of genes with important roles in development. Analysis of SNPs reveals long-range haplotypes across the entire dog genome, and defines the nature of genetic diversity within and across breeds. The current SNP map now makes it possible for genome-wide association studies to identify genes responsible for diseases and traits, with important consequences for human and companion animal health.

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Section: Generating a Draft Genome Sequencementioning

confidence: 99%

Genome sequence, comparative analysis and haplotype structure of the domestic dog

Lindblad‐Toh¹,

Wade²,

Mikkelsen³

et al. 2005

Nature

2,234

2,079

View full text Add to dashboard Cite

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“…The simulations with 16 sequences and both missing data and errors remain unavailable, since they proved too computationally intensive (see text). rate of 0.0015 was $15 times greater than the accepted cutoff of 0.0001 for finished sequences (Richterich 1998).…”

Section: New Model For Experimental Design and Errormentioning

confidence: 99%

Incorporating Experimental Design and Error Into Coalescent/Mutation Models of Population History

Knudsen

Miyamoto

2007

Genetics

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Coalescent theory provides a powerful framework for estimating the evolutionary, demographic, and genetic parameters of a population from a small sample of individuals. Current coalescent models have largely focused on population genetic factors (e.g., mutation, population growth, and migration) rather than on the effects of experimental design and error. This study develops a new coalescent/mutation model that accounts for unobserved polymorphisms due to missing data, sequence errors, and multiple reads for diploid individuals. The importance of accommodating these effects of experimental design and error is illustrated with evolutionary simulations and a real data set from a population of the California sea hare. In particular, a failure to account for sequence errors can lead to overestimated mutation rates, inflated coalescent times, and inappropriate conclusions about the population. This current model can now serve as a starting point for the development of newer models with additional experimental and population genetic factors. It is currently implemented as a maximum-likelihood method, but this model may also serve as the basis for the development of Bayesian approaches that incorporate experimental design and error.T HE genealogy for a small random sample of sequences is influenced by a large number of evolutionary, demographic, and genetic factors for its population. By making a few basic assumptions, coalescent theory provides the framework to estimate the probabilities of these genealogies and their associated population parameters (Hudson 1990;Donnelly and Tavaré 1995;Hein et al. 2004). Current coalescent models continue to emphasize population genetic factors such as mutation, varying population size, migration, and divergence time. These models are implemented with both maximum-likelihood (ML) and sampling-based (e.g., Markov chain Monte Carlo, MCMC) approaches. Although exact, the former is generally practical or even possible only for the simpler models (i.e., those that account for a single factor) and smaller data sets. In turn, although approximate, the latter can usually accommodate more complex models and larger data sets. The sampling-based methods often rely on a Bayesian setting, where parameters are integrated over their ranges and expected values are obtained (rather than ML estimates).In contrast to this emphasis on population genetic factors, the effects of experimental design and error on a coalescent study have been largely ignored (Felsenstein 2004). Most current coalescent models assume that haplotype data are available for diploids and that sequence variation is sampled in an unbiased manner. However, haplotypes are not always available, particularly for nuclear markers, and single-nucleotide polymorphisms (SNPs), for example, are often ascertained in ways that can bias their subsequent analysis. In light of these facts, new coalescent models have been introduced to account for these effects of experimental design Nielsen 2000).This study develops a new coalescent/muta...

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“…The quality values obtained in phred have been calibrated extensively, so they can be used to give reliable estimates of error rates; this calibration is the subject of one of the papers in this issue (Ewing and Green 1998), and it has been verified across data from a variety of sites by Richterich (1998). The resulting objectivity has had important consequences, both in terms of establishing standards that can be used meaningfully by others and also in allowing the quality values to be used for many other purposes.…”

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confidence: 99%