Literature DB >> 9521928

Estimation of errors in "raw" DNA sequences: a validation study.

P Richterich1.   

Abstract

As DNA sequencing is performed more and more in a mass-production-like manner, efficient quality control measures become increasingly important for process control, but so also does the ability to compare different methods and projects. One of the fundamental quality measures in sequencing projects is the position-specific error probability at all bases in each individual sequence. Accurate prediction of base-specific error rates from "raw" sequence data would allow immediate quality control as well as benchmarking different methods and projects while avoiding the inefficiencies and time delays associated with resequencing and assessments after "finishing" a sequence. The program PHRED provides base-specific quality scores that are logarythmically related to error probabilities. This study assessed the accuracy of PHRED's error-rate prediction by analyzing sequencing projects from six different large-scale sequencing laboratories. All projects used four-color fluorescent sequencing, but the sequencing methods used varied widely between the different projects. The results indicate that the error-rate predictions such as those given by PHRED can be highly accurate for a large variety of different sequencing methods as well as over a wide range of sequence quality.

Mesh:

Year:  1998        PMID: 9521928      PMCID: PMC310698          DOI: 10.1101/gr.8.3.251

Source DB:  PubMed          Journal:  Genome Res        ISSN: 1088-9051            Impact factor:   9.043


  6 in total

1.  The accuracy of DNA sequences: estimating sequence quality.

Authors:  G A Churchill; M S Waterman
Journal:  Genomics       Date:  1992-09       Impact factor: 5.736

2.  Base-calling of automated sequencer traces using phred. I. Accuracy assessment.

Authors:  B Ewing; L Hillier; M C Wendl; P Green
Journal:  Genome Res       Date:  1998-03       Impact factor: 9.043

3.  Base-calling of automated sequencer traces using phred. II. Error probabilities.

Authors:  B Ewing; P Green
Journal:  Genome Res       Date:  1998-03       Impact factor: 9.043

4.  A graph theoretic approach to the analysis of DNA sequencing data.

Authors:  A J Berno
Journal:  Genome Res       Date:  1996-02       Impact factor: 9.043

5.  The application of numerical estimates of base calling accuracy to DNA sequencing projects.

Authors:  J K Bonfield; R Staden
Journal:  Nucleic Acids Res       Date:  1995-04-25       Impact factor: 16.971

6.  Assignment of position-specific error probability to primary DNA sequence data.

Authors:  C B Lawrence; V V Solovyev
Journal:  Nucleic Acids Res       Date:  1994-04-11       Impact factor: 16.971

  6 in total
  31 in total

1.  Sequence-tagged connectors: a sequence approach to mapping and scanning the human genome.

Authors:  G G Mahairas; J C Wallace; K Smith; S Swartzell; T Holzman; A Keller; R Shaker; J Furlong; J Young; S Zhao; M D Adams; L Hood
Journal:  Proc Natl Acad Sci U S A       Date:  1999-08-17       Impact factor: 11.205

2.  Detecting and analyzing DNA sequencing errors: toward a higher quality of the Bacillus subtilis genome sequence.

Authors:  C Médigue; M Rose; A Viari; A Danchin
Journal:  Genome Res       Date:  1999-11       Impact factor: 9.043

3.  Basecalling with LifeTrace.

Authors:  D Walther; G Bartha; M Morris
Journal:  Genome Res       Date:  2001-05       Impact factor: 9.043

4.  Sequence organization and matrix attachment regions of the human serine protease inhibitor gene cluster at 14q32.1.

Authors:  Stephanie J Namciu; Richard D Friedman; Mark D Marsden; Lourdes M Sarausad; Christine L Jasoni; R E K Fournier
Journal:  Mamm Genome       Date:  2004-03       Impact factor: 2.957

Review 5.  Next-generation and whole-genome sequencing in the diagnostic clinical microbiology laboratory.

Authors:  W M Dunne; L F Westblade; B Ford
Journal:  Eur J Clin Microbiol Infect Dis       Date:  2012-06-08       Impact factor: 3.267

Review 6.  New virologic tools for management of chronic hepatitis B and C.

Authors:  Stéphane Chevaliez; Christophe Rodriguez; Jean-Michel Pawlotsky
Journal:  Gastroenterology       Date:  2012-05       Impact factor: 22.682

7.  Incorporating experimental design and error into coalescent/mutation models of population history.

Authors:  Bjarne Knudsen; Michael M Miyamoto
Journal:  Genetics       Date:  2007-06-11       Impact factor: 4.562

8.  Estimation of nucleotide diversity, disequilibrium coefficients, and mutation rates from high-coverage genome-sequencing projects.

Authors:  Michael Lynch
Journal:  Mol Biol Evol       Date:  2008-08-25       Impact factor: 16.240

9.  Whole-genome analysis of Exserohilum rostratum from an outbreak of fungal meningitis and other infections.

Authors:  Anastasia P Litvintseva; Steven Hurst; Lalitha Gade; Michael A Frace; Remy Hilsabeck; James M Schupp; John D Gillece; Chandler Roe; David Smith; Paul Keim; Shawn R Lockhart; Shankar Changayil; M Ryan Weil; Duncan R MacCannell; Mary E Brandt; David M Engelthaler
Journal:  J Clin Microbiol       Date:  2014-06-20       Impact factor: 5.948

10.  Characterization of synthetic DNA bar codes in Saccharomyces cerevisiae gene-deletion strains.

Authors:  Robert G Eason; Nader Pourmand; Waraporn Tongprasit; Zelek S Herman; Kevin Anthony; Olufisayo Jejelowo; Ronald W Davis; Viktor Stolc
Journal:  Proc Natl Acad Sci U S A       Date:  2004-07-16       Impact factor: 11.205

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