Accuracy and quality assessment of 454 GS-FLX Titanium pyrosequencing

Abstract

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.

Mosquito vector‐associated microbiota: Metabarcoding bacteria and eukaryotic symbionts across habitat types in Thailand endemic for dengue and other arthropod‐borne diseases

“…Despite a relatively low error rate, the large number of reads resulting from next‐generation sequencing can include a substantial number of erroneous sequences and thus an overestimation of total OTUs (Gilles et al., 2011). Consequently, it is critical to distinguish true sequence diversity from noise introduced by experimental procedures.…”

Oligotyping analysis of the human oral microbiome

“…This noise filtering removed 9.3% of reads from the V1-V3 dataset and 3.8% of reads from the V3-V5 dataset. High-information positions resulting from systematic errors such as those introduced at homopolymer regions by 454 sequencing (18,19,33) are also detected during the oligotyping process and can be selectively disregarded through the user-curation steps of oligotyping. Because we ensured the convergence criterion to be met only for oral sites, oligotypes composed of reads found mostly in stool samples may contain unexplained diversity of very closely related organisms; hence, the diversity of stool samples may have been underestimated in our final results.…”

Humboldt’s spa: microbial diversity is controlled by temperature in geothermal environments

“…To confirm the observed trends, multiple quality-filtering steps were performed, including denoising of the entire data set using the built-in QIIME denoiser workflow (at default settings), removal of OTUs that were only present once in the data set and removal of OTUs that only appeared in one sample in the data set. Pyrotag sequencing has an error rate of B1% (Gilles et al, 2011), however this was not expected to influence any trends in diversity as all samples would be equally affected.…”