DNA Sequence Determination by Hybridization: a Strategy for Efficient Large-Scale Sequencing

Drmanac, Radoje; Drmanac, Snezana; Strezoska, Žaklina; Paunesku, Tatjana; Labat, Ivan; Zeremski, Marija; Snoddy, Jay; Funkhouser, William K.; Koop, Ben F.; Hood, Leroy; Crkvenjakov, Radomir

doi:10.1126/science.8503011

Cited by 217 publications

(106 citation statements)

References 19 publications

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“…Degenerate bases could also be useful in nucleic acid sequence determination by hybridisation to oligonucleotides (15,16 Figure 2. a. Protocol for applying base precursors during the synthesis of array 1, to produce six copies of the sixteen oligonucleotides of formula 3' GGGX1X1AAA on the surface of a microscope slide.…”

Section: Introductionmentioning

confidence: 99%

Studies on the base pairing properties of deoxyinosine by solid phase hybridisation to oligonucleotides

Case-Green

Southern

1994

Nucl Acids Res

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Section: Introductionmentioning

confidence: 99%

Studies on the base pairing properties of deoxyinosine by solid phase hybridisation to oligonucleotides

Case-Green

Southern

1994

Nucl Acids Res

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“…There are DNA sequencing methods that do not use size separation. One such approach is sequencing by hybridization (SBH), which probes arrayed DNA sequences with oligonucleotides in order to ascertain information about the sequence [Drmanac et al, 1993;Southern et al, 1991]. Hyseq s system probes oligos against arrayed samples, whereas Affymetrix chips [Fodor et al, 1991] probe the sample against arrayed oligos.…”

Section: Other Gel-free Methodsmentioning

confidence: 99%

Determining sequence length or content in zero, one, and two dimensions

Perlin

Szabady

2002

Hum. Mutat.

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High-throughput assays are essential for the practical application of mutation detection in medicine and research. Moreover, such assays should produce informative data of high quality that have a low-error rate and a low cost. Unfortunately, this is not currently the case. Instead, we typically witness legions of people reviewing imperfect data at astronomical expense yielding uncertain results. To address this problem, for the past decade we have been developing methods that exploit the inherent quantitative nature of DNA experiments. By generating high-quality data, careful DNA-signal quantification permits robust analysis for determining true alleles and certainty measures. We will explore several assays and methods. In a one-dimensional readout, short tandem repeat (STR) data display interesting artifacts. Even with high-quality data, PCR artifacts such as stutter and relative amplification can confound correct or automated scoring. However, by appropriate mathematical analysis, these artifacts can be essentially removed from the data. The result is fully automated data scoring, quality assessment, and new types of DNA analysis. These approaches enable the accurate analysis of pooled DNA samples, for both genetic and forensic applications. On a two-dimensional surface (comprised of zero-dimensional spots) one can perform assays of extremely high-throughput at low cost. The question is how to determine DNA sequence length or content from nonelectrophoretic intensity data. Here again, mathematical analysis of highly quantitative data provides a solution. We will discuss new lab assays that can produce data containing such information; mathematical transformation then determines DNA length or content. Hum Mutat 19:361 373, 2002.

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“…The problem of resequencing is important because modern high-throughput sequencing technologies determine only very short subsequences, or reads. For example, the technology known as Sequencing By Hybridization (SBH) determines the subsequence content of an unknown DNA by identifying all probes of a given length (often around 10 nucleotides) that bind to it [1]. More recently, a number of groups have developed fast, highthroughput technologies that use short reads [12,16].…”

Section: Resequencingmentioning

confidence: 99%

The Generalized Gibbs Sampler and the Neighborhood Sampler

Keith

Sofronov

Kroese

2008

Monte Carlo and Quasi-Monte Carlo Methods 2006

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The Generalized Gibbs Sampler (GGS) is a recently proposed Markov chain Monte Carlo (MCMC) technique that is particularly useful for sampling from distributions defined on spaces in which the dimension varies from point to point or in which points are not easily defined in terms of co-ordinates. Such spaces arise in problems involving model selection and model averaging and in a number of interesting problems in computational biology. Such problems have hitherto been the domain of the Reversible-jump Sampler, but the method described here, which generalizes the well-known conventional Gibbs Sampler, provides an alternative that is easy to implement and often highly efficient.The GGS provides a very general framework for MCMC simulation. Not only the conventional Gibbs Sampler, but also a variety of other well known samplers emerge as special cases. These include the Metropolis-Hastings sampler, the Reversible-jump Sampler and the Slice Sampler. We also present a new special case of the GGS, called the Neighborhood Sampler (NS), which does not conform to any of the other existing MCMC frameworks. We illustrate use of the GGS and the NS with a number of examples. In particular, we use the NS to sample from a discrete state space represented as a graph in which nodes have varying degree. Finally, we introduce a technique for improving convergence and mixing between sub-spaces of different dimension.

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DNA Sequence Determination by Hybridization: a Strategy for Efficient Large-Scale Sequencing

Cited by 217 publications

References 19 publications

Studies on the base pairing properties of deoxyinosine by solid phase hybridisation to oligonucleotides

Studies on the base pairing properties of deoxyinosine by solid phase hybridisation to oligonucleotides

Determining sequence length or content in zero, one, and two dimensions

The Generalized Gibbs Sampler and the Neighborhood Sampler

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