Open Access Open Badges Research article

Empirical assessment of sequencing errors for high throughput pyrosequencing data

Paulo GS da Fonseca12*, Jorge AP Paiva3, Luiz GP Almeida4, Ana TR Vasconcelos4 and Ana T Freitas15

Author Affiliations

1 Instituto de Engenharia de Sistemas e Computadores: Investigação e Desenvolvimento (INESC-ID), R. Alves Redol 9, Lisboa 1000-029, Portugal

2 Centro de Informática–Universidade Federal de Pernambuco, Av. Jornalista Anibal Fernandes s/n, Cidade Universitária Recife - PE 50740-560, Brasil

3 Instituto de Investigação Científica Tropical (IICT), Centro de Florestas e dos Produtos Florestais, Tapada da Ajuda, Lisboa 1349-018, Portugal

4 Laboratório Nacional de Computação Científica (LNCC), Laboratório de Bioinformática, Av. Getúlio Vargas, 333 Petrópolis, Rio de Janeiro, Brasil

5 Instituto Superior Técnico–Universidade Técnica de Lisboa (IST/UTL), Av. Rovisco Pais, Lisboa 1049-001, Portugal

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BMC Research Notes 2013, 6:25  doi:10.1186/1756-0500-6-25

Published: 22 January 2013



Sequencing-by-synthesis technologies significantly improve over the Sanger method in terms of speed and cost per base. However, they still usually fail to compete in terms of read length and quality. Current high-throughput implementations of the pyrosequencing technique yield reads whose length approach those of the capillary electrophoresis method. A less obvious question is whether their quality is affected by platform-specific sequencing errors.


We present an empirical study aimed at assessing the quality and characterising sequencing errors for high throughput pyrosequencing data. We have developed a procedure for extracting sequencing error data from genome assemblies and study their characteristics, in particular the length distribution of indel gaps and their relation to the sequence contexts where they occur. We used this procedure to analyse data from three prokaryotic genomes sequenced with the GS FLX technology. We also compared two models previously employed with success for peptide sequence alignment.


We observed an overall very low error rate in the analysed data, with indel errors being much more abundant than substitutions. We also observed a dependence between the length of the gaps and that of the homopolymer context where they occur. As with protein alignments, a power-law model seems to approximate the indel errors more accurately, although the results are not so conclusive as to justify a depart from the commonly used affine gap penalty scheme. In whichever case, however, our procedure can be used to estimate more realistic error model parameters.