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            DNA Polymerase, FS Dye-Terminator Sequencing: Analysis of Peak Height Patterns

Parker, Lisa; Zakeri, Hamideh; Deng, Qiang; Spurgeon, Sandra L.; Kwok, Pui Yan; Nickerson, Deborah A.

doi:10.2144/96214rr02

Cited by 83 publications

(67 citation statements)

References 6 publications

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“…These were mostly attributable to peak dropouts reflecting poor incorporation of a particular dyelabeled dideoxy base by the polymerase in certain sequence contexts; in such cases the base-caller may call a small noise peak instead of the correct base, and if that peak is in the expected location and there are no other noise peaks nearby, the parameter values may still all be in the good range. About 85% of the high-quality dye terminator errors resulted from a missing G peak following an A (Lee et al 1992;Parker et al 1996), or a missing A following a T; a missing T following an A occurred five times, but in only one cosmid of the 11 examined. There was also a missing T peak following a G peak, and an apparent AA compression.…”

Section: Validity Testsmentioning

confidence: 99%

Base-Calling of Automated Sequencer Traces Using Phred. II. Error Probabilities

1998

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Elimination of the data processing bottleneck in high-throughput sequencing will require both improved accuracy of data processing software and reliable measures of that accuracy. We have developed and implemented in our base-calling program phred the ability to estimate a probability of error for each base-call, as a function of certain parameters computed from the trace data. These error probabilities are shown here to be valid (correspond to actual error rates) and to have high power to discriminate correct base-calls from incorrect ones, for read data collected under several different chemistries and electrophoretic conditions. They play a critical role in our assembly program phrap and our finishing program consed.Read data from automated sequencers varies significantly in quality for a number of reasons (for review, see Ewing et al. 1998), and making the most effective use of such data requires having some measure of its reliability. Position-specific error probabilities (Lawrence and Solovyev 1994) are particularly useful for this purpose. In conjunction with appropriate assembly software they can: improve the accuracy and completeness of assembly by allowing better discrimination of repeats and by making it possible to use full read lengths; permit a more accurate consensus sequence to be derived; provide an objective criterion for guiding the finishing (deciding where additional data or editing are needed); and provide an objective measure useful in monitoring data quality and in setting the quality standard for the final sequence.A number of developers of base-calling algorithms (Giddings et al. 1993;Golden et al. 1993;Berno 1996) have developed confidence measures for the base-calls, but do not report studies of their validity or discrimination power. The most thorough study of which we are aware is that of Lawrence and Solovyev (1994), who defined a large number of trace parameters and carried out an extensive discriminant analysis to determine the ones most effective at distinguishing accurate base calls from errors and assigning error probabilities. Here, we describe a different procedure for estimating error probabilities and investigate its properties. The main distinguishing features of our work are (1) a novel algorithm for deriving the probabilities, that does not require the multivariate normality distributional assumptions that are needed for discriminant analysis but are very far from being true in the case of the parameters we consider; (2) use of parameters computed from windows of the trace, that appear more effective at discrimination than the single-peak measures considered by Lawrence and Solovyev; and (3) an emphasis on optimizing discrimination ability in the high-quality part of the trace (error rates <0.01) rather than over the entire range, as it is this part of the trace that tends to be more important in practice.

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Section: Validity Testsmentioning

confidence: 99%

Base-Calling of Automated Sequencer Traces Using Phred. II. Error Probabilities

1998

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“…GC-rich sequence tends to be more compression prone than AT-rich sequence because of the greater likelihood of stable hairpins. Dye-terminator chemistry appears to resolve most compressions (Lee et al 1992), possibly because the dye on the terminal nucleotide interferes with base-pairing or because of the use of deoxyinosine triphosphate in place of deoxyguanosine triphosphate; but this chemistry has its own data quality problems caused by reduced polymerase affinity for the dye-labeled terminal nucleotide, one particular problem being a substantial decrease in the signal for a G base following an A (Lee et al 1992;Parker et al 1996).…”

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confidence: 99%

Base-Calling of Automated Sequencer Traces UsingPhred. I. Accuracy Assessment

et al. 1998

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The availability of massive amounts of DNA sequence information has begun to revolutionize the practice of biology. As a result, current large-scale sequencing output, while impressive, is not adequate to keep pace with growing demand and, in particular, is far short of what will be required to obtain the 3-billion-base human genome sequence by the target date of 2005. To reach this goal, improved automation will be essential, and it is particularly important that human involvement in sequence data processing be significantly reduced or eliminated. Progress in this respect will require both improved accuracy of the data processing software and reliable accuracy measures to reduce the need for human involvement in error correction and make human review more efficient. Here, we describe one step toward that goal: a base-calling program for automated sequencer traces, phred, with improved accuracy. phred appears to be the first base-calling program to achieve a lower error rate than the ABI software, averaging 40%-50% fewer errors in the data sets examined independent of position in read, machine running conditions, or sequencing chemistry.

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“…Peak height patterns are strongly repeatable [5] , and correlate with sequence motif to exhibit distinct expectations and variance [10,11]. In [14] we suggest that an incorrect hypothesis of base composition will predict patterns which differ from the trace data we see, and hence allow us to reject such hypotheses.…”

Section: Abductive Basecallingmentioning

confidence: 85%

Machine learned regression for abductive DNA sequencing

Thornley¹,

Zverev²,

Petridis³

2007

Sixth International Conference on Machine Learning and Applications (ICMLA 2007)

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We construct machine learned regressors to predict the behaviour of DNA sequencing data from the fluorescent labelled Sanger method. These predictions are used to assess hypotheses for sequence composition through calculation of likelihood or deviation evidence from the comparison of predictions from the hypothesized sequence with target trace data. We machine learn a means for comparing the measures taken from competing hypotheses for the sequence. This is a machine learned implementation of our proposal for abductive DNA basecalling. The results of the present experiments suggest that neural nets are a more effective means for predicting peak sizes than decision tree regressors, and for assembling evidence for competing hypotheses in this context. This is despite the availability of variance estimates in our decision tree regressors.

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AmpliTaq ^® DNA Polymerase, FS Dye-Terminator Sequencing: Analysis of Peak Height Patterns

Abstract: Taq DNA polymerases in which the phenylalanine is substituted by a tyrosine at position 667 (Taq F667Y)

Cited by 83 publications

References 6 publications

Base-Calling of Automated Sequencer Traces Using Phred. II. Error Probabilities

Base-Calling of Automated Sequencer Traces Using Phred. II. Error Probabilities

Base-Calling of Automated Sequencer Traces UsingPhred. I. Accuracy Assessment

Machine learned regression for abductive DNA sequencing

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AmpliTaq ® DNA Polymerase, FS Dye-Terminator Sequencing: Analysis of Peak Height Patterns

Abstract: Taq DNA polymerases in which the phenylalanine is substituted by a tyrosine at position 667 (Taq F667Y)

Cited by 83 publications

References 6 publications

Base-Calling of Automated Sequencer Traces Using Phred. II. Error Probabilities

Base-Calling of Automated Sequencer Traces Using Phred. II. Error Probabilities

Base-Calling of Automated Sequencer Traces UsingPhred. I. Accuracy Assessment

Machine learned regression for abductive DNA sequencing

Contact Info

Product

Resources

About

AmpliTaq ^® DNA Polymerase, FS Dye-Terminator Sequencing: Analysis of Peak Height Patterns