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Open Access Highly Accessed Research article

Accuracy and quality assessment of 454 GS-FLX Titanium pyrosequencing

André Gilles1, Emese Meglécz1, Nicolas Pech1, Stéphanie Ferreira2, Thibaut Malausa3 and Jean-François Martin4*

Author Affiliations

1 Aix-Marseille Université, CNRS, IRD, UMR 6116 - IMEP, Equipe Evolution Génome Environnement, Centre Saint-Charles, Case 36, 3 place Victor Hugo, 13331 Marseille Cedex 3, France

2 Genoscreen, Genomic Platform and R&D, Campus de l'Institut Pasteur, 1 rue du Professeur Calmette, Bâtiment Guérin, 4ème étage, 59000 Lille, France

3 Institut National de la Recherche Agronomique, UMR 1301, Equipe BPI, 400 route des Chappes, BP 167, 06903 Sophia-Antipolis Cedex, France

4 UMR CBGP (INRA/IRD/Cirad/Montpellier SupAgro), Campus international de Baillarguet, CS 30016, F-34988 Montferrier-sur-Lez cedex, France

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BMC Genomics 2011, 12:245  doi:10.1186/1471-2164-12-245

Published: 19 May 2011

Abstract

Background

The rapid evolution of 454 GS-FLX sequencing technology has not been accompanied by a reassessment of the quality and accuracy of the sequences obtained. Current strategies for decision-making and error-correction are based on an initial analysis by Huse et al. in 2007, for the older GS20 system based on experimental sequences. We analyze here the quality of 454 sequencing data and identify factors playing a role in sequencing error, through the use of an extensive dataset for Roche control DNA fragments.

Results

We obtained a mean error rate for 454 sequences of 1.07%. More importantly, the error rate is not randomly distributed; it occasionally rose to more than 50% in certain positions, and its distribution was linked to several experimental variables. The main factors related to error are the presence of homopolymers, position in the sequence, size of the sequence and spatial localization in PT plates for insertion and deletion errors. These factors can be described by considering seven variables. No single variable can account for the error rate distribution, but most of the variation is explained by the combination of all seven variables.

Conclusions

The pattern identified here calls for the use of internal controls and error-correcting base callers, to correct for errors, when available (e.g. when sequencing amplicons). For shotgun libraries, the use of both sequencing primers and deep coverage, combined with the use of random sequencing primer sites should partly compensate for even high error rates, although it may prove more difficult than previous thought to distinguish between low-frequency alleles and errors.