Sequence errors described in GenBank: a means to determine the accuracy of DNA sequence interpretation

Krawetz, Stephen A.

doi:10.1093/nar/17.10.3951

Cited by 31 publications

(11 citation statements)

References 11 publications

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“…Much work remains to be done both in the analytic aspects of modeling and in the empirical realm of cataloging sequence errors (see Krawetz, 1989). The procedures described here can be implemented as a first step.…”

Section: Discussionmentioning

confidence: 99%

The accuracy of DNA sequences: Estimating sequence quality

Churchill

Waterman

1992

Genomics

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In this paper we describe a method for the statistical reconstruction of a large DNA sequence from a set of sequenced fragments. We assume that the fragments have been assembled and address the problem of determining the degree to which the reconstructed sequence is free from errors, i.e. its accuracy. A consensus distribution is derived from the assembled fragment configuration based upon the rates of sequencing errors in the individual fragments. The consensus distribution can be used to find a minimally redundant consensus sequence which meets a prespecified confidence level, either base by base or across any region of the sequence. A likelihood based procedure for the estimation of the sequencing error rates is described which utilizes an iterative EM algorithm. Prior knowledge of the error rates is easily incorporated into the estimation procedure. The methods are applied to a set of assembled sequence fragments from the human G6PD locus. We close the paper with a brief discussion of the considerations involved in maintaining a database of sequence accuracy information.

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Section: Discussionmentioning

confidence: 99%

The accuracy of DNA sequences: Estimating sequence quality

Churchill

Waterman

1992

Genomics

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“…This is similar to the error rates of 3.6 and 3.2% reported by Kristensen et al (1992) and Lamperti et al (1992), respectively, in vector sequences that had contaminated the sequence databases. Krawetz (1989), however, reports an error rate of only 0.29% for GenBank on the basis of the frequency of annotated conflicts and revisions, and Beck (1993) reports error rates averaging 0.46% for resequencing of cosmids containing human genomic DNA by independent laboratories.…”

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confidence: 99%

DNA Sequence Error Rates in Genbank Records Estimated using the Mouse Genome as a Reference

Wesche¹,

Gaffney²,

Keightley³

2004

DNA Sequence

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We estimate DNA sequence error rates in Genbank records containing protein-coding and non-coding DNA sequences by comparing sequences of the inbred mouse strain C57BL/6J, sequenced as part of the mouse genome project and independently by other laboratories. C57BL/6J was produced by more than 100 generations of brother-sister mating, and can be assumed to be virtually free of residual polymorphism and mutational variation, so differences between independent sequences can be attributed to error. The estimated single nucleotide error rate for coding DNA is 0.10% (SE 0.012%), which is substantially lower than previous estimates for error rates in Genbank accessions. The estimated single nucleotide error rate for intronic DNA sequences (0.22%; SE 0.051%) is significantly higher than the rate for coding DNA. Since error rates for the mouse genome sequence are very low, the vast majority of the errors we detected are likely to be in individual Genbank accessions. The frequency of insertion-deletion (indel) errors in non-coding DNA approaches that of single nucleotide errors in non-coding DNA, whereas indel errors are uncommon in coding sequences.

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“…Over the years, the use and development of genetic approaches have resulted in the generation of large amounts of genetic data, which has been made publically available in repositories such as GenBank [2], providing a unique and very valuable resource for the research community. The utility of such public data, produced by others and from several different researchers, relies heavily on the assumption of high data quality [3]. However, although much has been accomplished in terms of minimizing their prevalence, sequence errors are still an important issue for both Sanger and next generation sequencing data [4–7].…”

Section: Introductionmentioning

confidence: 99%

Control Control Control: A Reassessment and Comparison of GenBank and Chromatogram mtDNA Sequence Variation in Baltic Grey Seals (Halichoerus grypus)

2013

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Genetic data can provide a powerful tool for those interested in the biology, management and conservation of wildlife, but also lead to erroneous conclusions if appropriate controls are not taken at all steps of the analytical process. This particularly applies to data deposited in public repositories such as GenBank, whose utility relies heavily on the assumption of high data quality. Here we report on an in-depth reassessment and comparison of GenBank and chromatogram mtDNA sequence data generated in a previous study of Baltic grey seals. By re-editing the original chromatogram data we found that approximately 40% of the grey seal mtDNA haplotype sequences posted in GenBank contained errors. The re-analysis of the edited chromatogram data yielded overall similar results and conclusions as the original study. However, a significantly different outcome was observed when using the uncorrected dataset based on the GenBank haplotypes. We therefore suggest disregarding the existing GenBank data and instead using the correct haplotypes reported here. Our study serves as an illustrative example reiterating the importance of quality control through every step of a research project, from data generation to interpretation and submission to an online repository. Errors conducted in any step may lead to biased results and conclusions, and could impact management decisions.

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Sequence errors described in GenBank: a means to determine the accuracy of DNA sequence interpretation

Cited by 31 publications

References 11 publications

The accuracy of DNA sequences: Estimating sequence quality

The accuracy of DNA sequences: Estimating sequence quality

DNA Sequence Error Rates in Genbank Records Estimated using the Mouse Genome as a Reference

Control Control Control: A Reassessment and Comparison of GenBank and Chromatogram mtDNA Sequence Variation in Baltic Grey Seals (Halichoerus grypus)

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