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Empirical assessment of sequencing errors for high throughput pyrosequencing data.

da Fonseca PG, Paiva JA, Almeida LG, Vasconcelos AT, Freitas AT - BMC Res Notes (2013)

Bottom Line: We also compared two models previously employed with success for peptide sequence alignment.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Instituto de Engenharia de Sistemas e Computadores: Investigação e Desenvolvimento (INESC-ID), R, Alves Redol 9, Lisboa 1000-029, Portugal. pgsf@kdbio.inesc-id.pt.

ABSTRACT

Background: 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.

Results: 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.

Conclusions: 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.

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Related in: MedlinePlus

Data set construction workflow. Data set construction workflow.
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Figure 1: Data set construction workflow. Data set construction workflow.

Mentions: Our data set construction strategy, depicted in Figure1, stands upon two concepts: redundancy (coverage) and homology (conservation). Starting with the flowgram files (SFF) produced by the sequencer, we use an automatic de novo genome assembler to reconstruct the original genome. This results in an ACE file that describes how a certain number of contigs are formed from a patchwork of the original reads (or pieces of them). During the assembly, a minimum overlap is required between reads so that they can be stitched together. Consequently, every position of a contig is ‘covered’ by multiple overlapping reads. This redundancy is usually regarded as an indicator of the veracity of the assembled contigs, the idea being that errors eventually introduced in some reads are unlikely to be consistently reproduced over the other reads covering the same region.


Empirical assessment of sequencing errors for high throughput pyrosequencing data.

da Fonseca PG, Paiva JA, Almeida LG, Vasconcelos AT, Freitas AT - BMC Res Notes (2013)

Data set construction workflow. Data set construction workflow.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3852801&req=5

Figure 1: Data set construction workflow. Data set construction workflow.
Mentions: Our data set construction strategy, depicted in Figure1, stands upon two concepts: redundancy (coverage) and homology (conservation). Starting with the flowgram files (SFF) produced by the sequencer, we use an automatic de novo genome assembler to reconstruct the original genome. This results in an ACE file that describes how a certain number of contigs are formed from a patchwork of the original reads (or pieces of them). During the assembly, a minimum overlap is required between reads so that they can be stitched together. Consequently, every position of a contig is ‘covered’ by multiple overlapping reads. This redundancy is usually regarded as an indicator of the veracity of the assembled contigs, the idea being that errors eventually introduced in some reads are unlikely to be consistently reproduced over the other reads covering the same region.

Bottom Line: We also compared two models previously employed with success for peptide sequence alignment.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Instituto de Engenharia de Sistemas e Computadores: Investigação e Desenvolvimento (INESC-ID), R, Alves Redol 9, Lisboa 1000-029, Portugal. pgsf@kdbio.inesc-id.pt.

ABSTRACT

Background: 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.

Results: 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.

Conclusions: 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.

Show MeSH
Related in: MedlinePlus