Efficient error correction for next-generation sequencing of viral amplicons

Abstract

BackgroundNext-generation sequencing allows the analysis of an unprecedented number of viral sequence variants from infected patients, presenting a novel opportunity for understanding virus evolution, drug resistance and immune escape. However, sequencing in bulk is error prone. Thus, the generated data require error identification and correction. Most error-correction methods to date are not optimized for amplicon analysis and assume that the error rate is randomly distributed. Recent quality assessment of amplicon sequences obtained using 454-sequencing showed that the error rate is strongly linked to the presence and size of homopolymers, position in the sequence and length of the amplicon. All these parameters are strongly sequence specific and should be incorporated into the calibration of error-correction algorithms designed for amplicon sequencing.ResultsIn this paper, we present two new efficient error correction algorithms optimized for viral amplicons: (i) k-mer-based error correction (KEC) and (ii) empirical frequency threshold (ET). Both were compared to a previously published clustering algorithm (SHORAH), in order to evaluate their relative performance on 24 experimental datasets obtained by 454-sequencing of amplicons with known sequences. All three algorithms show similar accuracy in finding true haplotypes. However, KEC and ET were significantly more efficient than SHORAH in removing false haplotypes and estimating the frequency of true ones.ConclusionsBoth algorithms, KEC and ET, are highly suitable for rapid recovery of error-free haplotypes obtained by 454-sequencing of amplicons from heterogeneous viruses.The implementations of the algorithms and data sets used for their testing are available at: http://alan.cs.gsu.edu/NGS/?q=content/pyrosequencing-error-correction-algorithm

Analysis of the Evolution and Structure of a Complex Intrahost Viral Population in Chronic Hepatitis C Virus Mapped by Ultradeep Pyrosequencing

“…The obtained data sets were processed by the sequential application of the algorithms k-mer error correction (KEC) and a customized version of empirical threshold (ET) (31). Skums et al (31) have previously demonstrated this process to be highly accurate in finding true haplotypes and removing false haplotypes.…”

“…In stage 1, the set of k-mers (substring of fixed length k) of reads from the processed data set is calculated and the distribution of frequencies of k-mers is analyzed (31). It was previously observed that the frequencies of erroneous and correct k-mers follow different distributions (32)(33)(34).…”

Network Analysis of the Chronic Hepatitis C Virome Defines Hypervariable Region 1 Evolutionary Phenotypes in the Context of Humoral Immune Responses

“…The resultant data files were sequentially processed through implementation of the k-mer error correction (KEC) and empirical threshold algorithms as previously described, using the parameters k ϭ 25 and i ϭ 3 (22,27). A panel of clonal sequences temporally matched to the UDPS data was used to further identify and correct homopolymer errors (22,25).…”

Virus evolution during chronic hepatitis B virus infection as revealed by ultradeep sequencing data

“…The obtained reads were further processed with the program PRINSEQ, which depurates pyrosequencing data based on the read quality (Schmieder & Edwards, 2011). After this, the data were filtered by the error correction algorithm implemented in the program KEC, which corrects outlier sequences based on k-mer frequencies and identifies haplotypes based on the corresponding results (Skums et al, 2012). Given that KEC returns a corrected set of sequences, we did not use this output directly.…”