PCR-induced transitions are the major source of error in cleaned ultra-deep pyrosequencing data

Abstract

Background: Ultra-deep pyrosequencing (UDPS) is used to identify rare sequence variants. The sequence depth is influenced by several factors including the error frequency of PCR and UDPS. This study investigated the characteristics and source of errors in raw and cleaned UDPS data.

Results: UDPS of a 167-nucleotide fragment of the HIV-1 SG3Δenv plasmid was performed on the Roche/454 platform. The plasmid was diluted to one copy, PCR amplified and subjected to bidirectional UDPS on three occasions. The dataset consisted of 47,693 UDPS reads. Raw UDPS data had an average error frequency of 0.30% per nucleotide site. Most errors were insertions and deletions in homopolymeric regions. We used a cleaning strategy that removed almost all indel errors, but had little effect on substitution errors, which reduced the error frequency to 0.056% per nucleotide. In cleaned data the error frequency was similar in homopolymeric and non-homopolymeric regions, but varied considerably across sites. These site-specific error frequencies were moderately, but still significantly, correlated between runs (r=0.15-0.65) and between forward and reverse sequencing directions within runs (r=0.33-0.65). Furthermore, transition errors were 48-times more common than transversion errors (0.052% vs. 0.001%; p<0.0001). Collectively the results indicate that a considerable proportion of the sequencing errors that remained after data cleaning were generated during the PCR that preceded UDPS.

Conclusions: A majority of the sequencing errors that remained after data cleaning were introduced by PCR prior to sequencing, which means that they will be independent of platform used for next-generation sequencing. The transition vs. transversion error bias in cleaned UDPS data will influence the detection limits of rare mutations and sequence variants.

Figure 1. Examples of how different types of UDPS error were defined.

Figure 2. The average frequency of different substitution errors in percent (%) in cleaned UDPS data from three sequencing runs.

Thick arrows indicate transitions and thin arrows indicate transversions.

Figure 3. Site-specific error frequencies in percent (%) in cleaned UDPS data obtained in the forward sequencing direction of run 1.

All sequencing errors were substitutions since all deletions and insertions were removed by the data cleaning procedure. The bars are color-coded according to the type of substitution error. Homopolymeric regions are shaded.

Figure 4. PCR/UDPS error ratio in our cleaned data.

This figure shows a comparison of the counts of reverse to forward variants of run 1. A) Our filtered UDPS data. B) Same data, normalized by the main variant forward and reverse counts.