Accuracy and quality assessment of 454 GS-FLX Titanium pyrosequencing

By André Gilles, Emese Meglécz, Nicolas Pech, Stéphanie Ferreira, Thibaut Malausa, Jean-François Martin. BMC Genomics, 2011

401 citations recorded by the source corpus.

BackgroundThe 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.ResultsWe 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.ConclusionsThe 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.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/7366beac1ab850e6af90b3cbad08e1f76527397f

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Citations in the corpus

Five simple guidelines for establishing basic authenticity and reliability of newly generated fungal ITS sequences. (2012, background): “The sheer volume and the high frequency of platform-generated sequencing errors derived from NGS approaches necessitate extensive, elaborate quality control measures (Gilles et al. 2011; Quince et al. 2011), and the guidelines presented here should certainly not be used as a replacement for those.”
Shining a Light on Dark Sequencing: Characterising Errors in Ion Torrent PGM Data (2013, result): “Similar to Roche 454 pyrosequencing [11,12], indel-error rates increase with distance from the read start (Figure 3a).”
FunGene: the functional gene pipeline and repository (2013, background): “The rates for the ﬁrst two compare favorably with the claimed error rate for the 454 Titanium platform (1.07%; Gilles et al., 2011), while the error rate for BUK2 is similar to the claimed error rate for the junior platform (1.88%; Loman et al., 2012).”
Next-Generation Sequencing — An Overview of the History, Tools, and “Omic” Applications (2016, background): “[90] Gilles A, Meglecz E, Pech N, et al: Accuracy and quality assessment of 454 GS-FLX Titanium pyrosequencing.”
Not All Sequence Tags Are Created Equal: Designing and Validating Sequence Identification Tags Robust to Indels (2012, background): “, Illumina, Ion Torrent and Roche) with most errors occurring at the beginning and end of reads [18,22,29].”